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m_AnchoredPosition: {x: -20.6568, y: 0} + m_AnchoredPosition: {x: -20.666668, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1669330273 @@ -29895,7 +29895,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 1} m_AnchorMax: {x: 0.5, y: 1} m_AnchoredPosition: {x: -0, y: -2} - m_SizeDelta: {x: 213.13599, y: 26.641998} + m_SizeDelta: {x: 213.33333, y: 26.666666} m_Pivot: {x: 0.5, y: 1} --- !u!114 &1697246156 MonoBehaviour: @@ -30288,7 +30288,7 @@ RectTransform: m_GameObject: {fileID: 1721895619} m_LocalRotation: {x: 0, y: 0, z: 0, w: 1} m_LocalPosition: {x: 0, y: 0, z: 0} - m_LocalScale: {x: 1, y: 1, z: 1} + m_LocalScale: {x: 0, y: 0, z: 0} m_Children: - {fileID: 111303086} - {fileID: 1821850659} @@ -30298,7 +30298,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -63} - m_SizeDelta: {x: 29.313599, y: 23.537466} + m_SizeDelta: {x: 29.333334, y: 23.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1721895621 MonoBehaviour: @@ -30365,7 +30365,7 @@ RectTransform: m_GameObject: {fileID: 1725213615} m_LocalRotation: {x: 0, y: 0, z: 0, w: 1} m_LocalPosition: {x: 0, y: 0, z: 0} - m_LocalScale: {x: 1, y: 1, z: 1} + m_LocalScale: {x: 0, y: 0, z: 0} m_Children: - {fileID: 1536968432} - {fileID: 1952665839} @@ -30375,7 +30375,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: 69} - m_SizeDelta: {x: 29.313599, y: 23.537466} + m_SizeDelta: {x: 29.333334, y: 23.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1725213617 MonoBehaviour: @@ -30687,7 +30687,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 10.656799, y: 19.537466} + m_SizeDelta: {x: 10.666667, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1744030219 MonoBehaviour: @@ -30980,7 +30980,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 21.313599, y: 19.537466} + m_SizeDelta: {x: 21.333334, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1753569903 MonoBehaviour: @@ -31277,7 +31277,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -20.6568, y: 0} + m_AnchoredPosition: {x: -20.666668, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1769014920 @@ -31355,7 +31355,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: 14.5, y: -140} - m_SizeDelta: {x: 27.537466, y: 23.537466} + m_SizeDelta: {x: 27.555555, y: 23.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1770477495 MonoBehaviour: @@ -31933,7 +31933,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 19.537466, y: 19.537466} + m_SizeDelta: {x: 19.555555, y: 19.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1811785219 MonoBehaviour: @@ -32254,7 +32254,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -20.6568, y: 0} + m_AnchoredPosition: {x: -20.666668, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1821850660 @@ -32929,7 +32929,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 21.313599, y: 19.537466} + m_SizeDelta: {x: 21.333334, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1863075756 MonoBehaviour: @@ -33008,7 +33008,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 19.537466, y: 19.537466} + m_SizeDelta: {x: 19.555555, y: 19.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1863263629 MonoBehaviour: @@ -33087,7 +33087,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 10.656799, y: 19.537466} + m_SizeDelta: {x: 10.666667, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1867950665 MonoBehaviour: @@ -33203,7 +33203,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 21.313599, y: 19.537466} + m_SizeDelta: {x: 21.333334, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1872392833 MonoBehaviour: @@ -33311,7 +33311,7 @@ RectTransform: m_GameObject: {fileID: 1884023749} m_LocalRotation: {x: 0, y: 0, z: 0, w: 1} m_LocalPosition: {x: 0, y: 0, z: 0} - m_LocalScale: {x: 1, y: 1, z: 1} + m_LocalScale: {x: 0, y: 0, z: 0} m_Children: - {fileID: 1614878826} - {fileID: 208643481} @@ -33321,7 +33321,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -3} - m_SizeDelta: {x: 29.313599, y: 23.537466} + m_SizeDelta: {x: 29.333334, y: 23.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1884023751 MonoBehaviour: @@ -33475,7 +33475,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 1} m_AnchorMax: {x: 0.5, y: 1} m_AnchoredPosition: {x: 0, y: 0} - m_SizeDelta: {x: 221.13599, y: 30.641998} + m_SizeDelta: {x: 221.33333, y: 30.666666} m_Pivot: {x: 0.5, y: 1} --- !u!114 &1885190693 MonoBehaviour: @@ -33617,7 +33617,7 @@ RectTransform: m_GameObject: {fileID: 1907076004} m_LocalRotation: {x: 0, y: 0, z: 0, w: 1} m_LocalPosition: {x: 0, y: 0, z: 0} - m_LocalScale: {x: 1, y: 1, z: 1} + m_LocalScale: {x: 0, y: 0, z: 0} m_Children: - {fileID: 1537446175} - {fileID: 1495256092} @@ -33627,7 +33627,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -553.2, y: -91} - m_SizeDelta: {x: 29.313599, y: 23.537466} + m_SizeDelta: {x: 29.333334, y: 23.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &1907076006 MonoBehaviour: @@ -33779,7 +33779,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: 230.26001, y: -140} - m_SizeDelta: {x: 27.537466, y: 23.537466} + m_SizeDelta: {x: 27.555555, y: 23.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1926093283 MonoBehaviour: @@ -34282,7 +34282,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: -0, y: 0} - m_SizeDelta: {x: 19.537466, y: 19.537466} + m_SizeDelta: {x: 19.555555, y: 19.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1935182774 MonoBehaviour: @@ -34572,7 +34572,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -20.6568, y: 0} + m_AnchoredPosition: {x: -20.666668, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &1952665840 @@ -35476,7 +35476,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 21.313599, y: 19.537466} + m_SizeDelta: {x: 21.333334, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &2021417084 MonoBehaviour: @@ -35689,11 +35689,11 @@ PrefabInstance: objectReference: {fileID: 1718911498} - target: {fileID: 4658081160006525512, guid: db65a59014728b245830bf6269cbbfa6, type: 3} propertyPath: m_RootOrder - value: 1 + value: 0 objectReference: {fileID: 0} - target: {fileID: 4658081160006525512, guid: db65a59014728b245830bf6269cbbfa6, type: 3} propertyPath: m_LocalPosition.x - value: -23 + value: -24 objectReference: {fileID: 0} - target: {fileID: 4658081160006525512, guid: db65a59014728b245830bf6269cbbfa6, type: 3} propertyPath: m_LocalPosition.y @@ -35701,7 +35701,7 @@ PrefabInstance: objectReference: {fileID: 0} - target: {fileID: 4658081160006525512, guid: db65a59014728b245830bf6269cbbfa6, type: 3} propertyPath: m_LocalPosition.z - value: 22 + value: -8 objectReference: {fileID: 0} - target: {fileID: 4658081160006525512, guid: db65a59014728b245830bf6269cbbfa6, type: 3} propertyPath: m_LocalRotation.w @@ -36365,7 +36365,7 @@ RectTransform: m_AnchorMin: {x: 1, y: 0.5} m_AnchorMax: {x: 1, y: 0.5} m_AnchoredPosition: {x: -4, y: 0} - m_SizeDelta: {x: 21.313599, y: 19.537466} + m_SizeDelta: {x: 21.333334, y: 19.555555} m_Pivot: {x: 1, y: 0.5} --- !u!114 &2054949624 MonoBehaviour: @@ -36658,7 +36658,7 @@ RectTransform: m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} m_AnchoredPosition: {x: 230.26001, y: -140} - m_SizeDelta: {x: 27.537466, y: 23.537466} + m_SizeDelta: {x: 27.555555, y: 23.555555} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &2085253773 MonoBehaviour: @@ -37014,7 +37014,7 @@ RectTransform: m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0} m_AnchorMin: {x: 0.5, y: 0.5} m_AnchorMax: {x: 0.5, y: 0.5} - m_AnchoredPosition: {x: -19.768734, y: 0} + m_AnchoredPosition: {x: -19.777779, y: 0} m_SizeDelta: {x: 20, y: 20} m_Pivot: {x: 0.5, y: 0.5} --- !u!114 &2121688059 diff --git a/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs b/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs index 0f7bbd5..e4969bb 100644 --- a/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs +++ b/Aimbot-PPO-MultiScene/Assets/Script/InGame/AgentWithGun.cs @@ -79,6 +79,7 @@ public class AgentWithGun : Agent private float LoadDirDateF; private float loadDirTimeF; public bool defaultTPCamera = true; + private bool gunReadyToggle = true; private StartSeneData DataTransfer; private UIController UICon; private HistoryRecorder HistoryRec; @@ -357,10 +358,9 @@ public class AgentWithGun : Agent Ray ray = thisCam.ScreenPointToRay(point); RaycastHit hit; Debug.DrawRay(ray.origin, ray.direction * 100, Color.blue); - bool isGunReady = gunReady(); - UICon.updateShootKeyViewer(shoot, isGunReady); + UICon.updateShootKeyViewer(shoot, gunReadyToggle); //按下鼠标左键 - if (shoot != 0 && isGunReady == true) + if (shoot != 0 && gunReadyToggle == true) { lastShootTime = Time.time; @@ -377,7 +377,7 @@ public class AgentWithGun : Agent shoot = 0; return shootReward; } - else if (shoot != 0 && isGunReady == false) + else if (shoot != 0 && gunReadyToggle == false) { shoot = 0; return shootWithoutReadyReward; @@ -536,6 +536,8 @@ public class AgentWithGun : Agent sensor.AddObservation(rayDisResult); // 探测用RayDis结果 float[](raySensorNum,1) //sensor.AddObservation(focusEnemyObserve); // 最近的Enemy情报 float[](3,1) MinEnemyIndex,x,z //sensor.AddObservation(raySensorNum); // raySensor数量 int + gunReadyToggle = gunReady(); + sensor.AddObservation(gunReadyToggle); // save gun is ready? sensor.AddObservation(LoadDirDateF); // 用于loadModel的第一级dir sensor.AddObservation(loadDirTimeF); // 用于loadModel的第二级dir sensor.AddObservation(saveNow); // sent saveNow Toggle to python let agent save weights diff --git a/Aimbot-PPO-Python/DemoRecorder.ipynb b/Aimbot-PPO-Python/DemoRecorder.ipynb index a2fce9c..6cf6d34 100644 --- a/Aimbot-PPO-Python/DemoRecorder.ipynb +++ b/Aimbot-PPO-Python/DemoRecorder.ipynb @@ -54,185 +54,239 @@ "EP Start\n", "EP Start\n", "EP Start\n", - "nowMemNum 743\n", - "lastMemCheckPoint 1\n", - "mem_saved\n", - "EP Start\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\UCUNI\\OneDrive\\Unity\\ML-Agents\\Aimbot-PPO\\Aimbot-PPO-Python\\GAILMem.py:33: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " actionsNP = np.asarray(self.actions)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nowMemNum 993\n", + "nowMemNum 605\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 1199\n", + "nowMemNum 765\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 1426\n", + "nowMemNum 912\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 1671\n", + "nowMemNum 1037\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 1890\n", + "nowMemNum 1206\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 2097\n", + "nowMemNum 1355\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 2307\n", + "nowMemNum 1506\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 2510\n", + "nowMemNum 1673\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n", - "nowMemNum 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Start\n", + "nowMemNum 37878\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38037\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38175\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38345\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38490\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38593\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38728\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 38947\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 39089\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 39236\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 39411\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 39590\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP 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Start\n", + "nowMemNum 41674\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 41815\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 41999\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42181\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42359\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42551\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42671\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42809\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 42971\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43104\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43254\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43395\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43590\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43755\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43889\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 43993\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44142\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44273\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44410\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44536\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44659\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44812\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 44933\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45046\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45187\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45318\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45450\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45593\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45712\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 45868\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46056\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46187\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46310\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46456\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46611\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46756\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 46909\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47093\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47211\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47359\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47487\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47605\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47801\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 47959\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48143\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48306\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48436\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48573\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48774\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 48942\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49052\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49167\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49306\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49443\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49570\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49712\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 49865\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50005\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50185\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50309\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50459\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50653\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50768\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 50897\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51035\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51237\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51403\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51578\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51720\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51825\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 51969\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52129\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52265\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52472\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52612\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52794\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 52935\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 53075\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 53206\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 53347\n", + "lastMemCheckPoint 1\n", + "mem_saved\n", + "EP Start\n", + "nowMemNum 53518\n", "lastMemCheckPoint 1\n", "mem_saved\n", "EP Start\n" @@ -561,7 +1487,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mUnityCommunicatorStoppedException\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_19308/2258777724.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemoAct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHumanActions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mnextState\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mdemoMem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msaveMems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactorProb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnextState\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39764/2258777724.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mactions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdemoAct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetHumanActions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mnextState\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mdemoMem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msaveMems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactorProb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnextState\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Users\\UCUNI\\OneDrive\\Unity\\ML-Agents\\Aimbot-PPO\\Aimbot-PPO-Python\\aimBotEnv.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, actions, behaviorName, trackedAgent)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;31m# take action to env\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_actions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbehavior_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbehaviorName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mthisActionTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 89\u001b[0m \u001b[1;31m# get nextState & reward & done after this action\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[0mnextState\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloadDir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaveNow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSteps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbehaviorName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrackedAgent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\mlagents_envs\\timers.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 303\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 304\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mhierarchical_timer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__qualname__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 305\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 307\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m \u001b[1;31m# type: ignore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\mlagents_envs\\environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 333\u001b[0m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_communicator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexchange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_input\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_poll_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moutputs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 335\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mUnityCommunicatorStoppedException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Communicator has exited.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 336\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_behavior_specs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[0mrl_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", @@ -591,6 +1517,23 @@ " demoMem.saveMemtoFile(gailExpertDataDir)\n", " print(\"mem_saved\")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 0, 0, [2.069664997294527]]\n" + ] + } + ], + "source": [ + "print(actions)" + ] } ], "metadata": { diff --git a/Aimbot-PPO-Python/GAIL-Main.ipynb b/Aimbot-PPO-Python/GAIL-Main.ipynb index ef7f6df..0f91fa9 100644 --- a/Aimbot-PPO-Python/GAIL-Main.ipynb +++ b/Aimbot-PPO-Python/GAIL-Main.ipynb @@ -46,7 +46,7 @@ "√√√√√Buffer Initialized Success√√√√√\n", "√√√√√Buffer Initialized Success√√√√√\n", "---------thisPPO Params---------\n", - "self.stateSize = 90\n", + "self.stateSize = 93\n", "self.disActShape = [3, 3, 2]\n", "self.disActSize 3\n", "self.disOutputSize 8\n", @@ -54,38 +54,38 @@ "self.conActRange = 10\n", "self.conOutputSize = 2\n", "---------thisPPO config---------\n", - "self.NNShape = [512, 256, 128]\n", + "self.NNShape = [512, 512, 256]\n", "self.criticLR = 0.002\n", "self.actorLR = 0.002\n", "self.gamma = 0.99\n", "self.lmbda = 0.95\n", "self.clipRange = 0.2\n", - "self.entropyWeight = 0.01\n", - "self.trainEpochs = 10\n", - "self.saveDir = GAIL-Model/1015-0101/\n", + "self.entropyWeight = 0.005\n", + "self.trainEpochs = 5\n", + "self.saveDir = GAIL-Model/1020-0318/\n", "self.loadModelDir = None\n", "---------Actor Model Create Success---------\n", "Model: \"model_1\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", - " stateInput (InputLayer) [(None, 90)] 0 [] \n", + " stateInput (InputLayer) [(None, 93)] 0 [] \n", " \n", - " dense0 (Dense) (None, 512) 46592 ['stateInput[0][0]'] \n", + " dense0 (Dense) (None, 512) 48128 ['stateInput[0][0]'] \n", " \n", - " dense1 (Dense) (None, 256) 131328 ['dense0[0][0]'] \n", + " dense1 (Dense) (None, 512) 262656 ['dense0[0][0]'] \n", " \n", - " dense2 (Dense) (None, 128) 32896 ['dense1[0][0]'] \n", + " dense2 (Dense) (None, 256) 131328 ['dense1[0][0]'] \n", " \n", - " muOut (Dense) (None, 1) 129 ['dense2[0][0]'] \n", + " muOut (Dense) (None, 1) 257 ['dense2[0][0]'] \n", " \n", - " sigmaOut (Dense) (None, 1) 129 ['dense2[0][0]'] \n", + " sigmaOut (Dense) (None, 1) 257 ['dense2[0][0]'] \n", " \n", - " disAct0 (Dense) (None, 3) 387 ['dense2[0][0]'] \n", + " disAct0 (Dense) (None, 3) 771 ['dense2[0][0]'] \n", " \n", - " disAct1 (Dense) (None, 3) 387 ['dense2[0][0]'] \n", + " disAct1 (Dense) (None, 3) 771 ['dense2[0][0]'] \n", " \n", - " disAct2 (Dense) (None, 2) 258 ['dense2[0][0]'] \n", + " disAct2 (Dense) (None, 2) 514 ['dense2[0][0]'] \n", " \n", " tf.math.multiply (TFOpLambda) (None, 1) 0 ['muOut[0][0]'] \n", " \n", @@ -98,8 +98,8 @@ " 'tf.math.add[0][0]'] \n", " \n", "==================================================================================================\n", - "Total params: 212,106\n", - "Trainable params: 212,106\n", + "Total params: 444,682\n", + "Trainable params: 444,682\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n", "---------Critic Model Create Success---------\n", @@ -107,19 +107,19 @@ "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " stateInput (InputLayer) [(None, 90)] 0 \n", + " stateInput (InputLayer) [(None, 93)] 0 \n", " \n", - " dense0 (Dense) (None, 512) 46592 \n", + " dense0 (Dense) (None, 512) 48128 \n", " \n", - " dense1 (Dense) (None, 256) 131328 \n", + " dense1 (Dense) (None, 512) 262656 \n", " \n", - " dense2 (Dense) (None, 128) 32896 \n", + " dense2 (Dense) (None, 256) 131328 \n", " \n", - " dense (Dense) (None, 1) 129 \n", + " dense (Dense) (None, 1) 257 \n", " \n", "=================================================================\n", - "Total params: 210,945\n", - "Trainable params: 210,945\n", + "Total params: 442,369\n", + "Trainable params: 442,369\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -127,10 +127,10 @@ ], "source": [ "ENV_PATH = \"./Build-CloseEnemyCut/Aimbot-PPO\"\n", - "EXPERT_DIR = \"GAIL-Expert-Data/1014-1302/pack-24957-RE.npz\"\n", + "EXPERT_DIR = \"GAIL-Expert-Data/1015-0148/pack-53518.npz\"\n", "WORKER_ID = 1\n", "BASE_PORT = 200\n", - "MAX_BUFFER_SIZE = 2048\n", + "MAX_BUFFER_SIZE = 256\n", "\n", "MAX_EP = 1000000000\n", "STACKSTATESSIZE = 3\n", @@ -150,21 +150,21 @@ "CONACT_RANGE = 10\n", "\n", "ppoConf = PPOConfig(\n", - " NNShape=[512, 256, 128],\n", + " NNShape=[512, 512, 256],\n", " actorLR=2e-3,\n", " criticLR=2e-3,\n", " gamma=0.99,\n", " lmbda=0.95,\n", " clipRange=0.20,\n", - " entropyWeight=1e-2,\n", - " trainEpochs=10,\n", + " entropyWeight=5e-3,\n", + " trainEpochs=5,\n", " saveDir=\"GAIL-Model/\" + datetime.datetime.now().strftime(\"%m%d-%H%M\") + \"/\",\n", " loadModelDir=None,\n", ")\n", "gailConf = GAILConfig(\n", " discrimNNShape=[256, 128],\n", - " discrimLR=1e-3,\n", - " discrimTrainEpochs=10,\n", + " discrimLR=1e-4,\n", + " discrimTrainEpochs=5,\n", " discrimSaveDir=\"GAIL-Model/\" + datetime.datetime.now().strftime(\"%m%d-%H%M\") + \"/\",\n", " ppoConfig=ppoConf\n", ")\n", @@ -187,11 +187,18 @@ "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20777.3\n" + ] + }, { "data": { - "image/png": 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yswkMaGF0FBERETGQijMRERGpVlwbNGDooAFEhIXS5qrWnCwuZtXa9URGRbNz9x6j40kdEpeQSEsVZyIiInWaijMRERGpFjq2b0dEeCiD+/Wlfv36HIr9g4lTprJ05Sry8vONjid1UFxCIiGDdammiIhIXVap4mzkiDuJCA8lJ+cEAJ9N/4ZNW7cB0CowkOefGk0DFxcsVgv3PTqaUyUl5dZ/7MGR9OnRndLSUpKSk3ln4gfkFxTQxM+POdO/IPFoEgB79x9gwkcfVyaqiIiIVEOeHh6EDw1mWFgoAc39yS8oYMmy5SxYEs3Bw7FGx5M6Li4hkYaurnh7eZKRmWV0HBERETFApc84mztvPrN/mldumr3JxBsvjuGNdycQeyQON7eGlJrNZ627dcevfPbVdMwWC6NG3sfdt9/K1K+mA3AsOYURD4+qbDwRERGpZkwmE927dmF4WCh9enbHwcGBnbv38M3sOaxcu57i4mKjI4oAEJeQAMAVAQEqzkREROqoy3Kp5nVduxB7JI7YI3FA2c1VK7J1x6+2x3v3H2BQvz6XI46IiIhUA038/LghdCg3hAzFz9eHrOwc5s6bz8LoGBJOn2UuUp3EJyQC0DKgBVt/3WlwGhERETFCpYuzm4dHED4kmP2HDjFl2pfk5efTwr8ZVquVye+OpZG7O8tWrWbWjz//7esMCx3K8tVrbc+bNm7MzGmfUFBQyOczZvLb3n2VjSoiIiJVzNHRkX69ehARFkq3zp0A2LxtOx9+Oo31m7dQWlpqcEKRc8vKyeFEbi6BAQFGRxERERGDnLc4+3jCeDwbNTpr+rQZ3/BL5CKmz5qN1WrloXtGMPrhBxg76UPs7e3p2L4d944azcniYj6Z+C4HDseyfeeuCt/jnv+7jVKzmegVKwHIyMpi+B13kZubR1DrVkx483VuH/kQhYWFZ607/PowbgwPA8DDw/1itl1EREQuk8CAACLCQwgLHoyHuzspx1P56ttZLIpZSlp6htHxRC7YkfgEAjWypoiISJ113uLs8TEvXtALLVgSzaR33gQgLT2DnXv2cCI3F4CNW7YR1LpVhcXZ9UOH0LtHdx577gXbtJKSEkpODyRw8HAsx1JSaOHfjAOHDp/9voujWLA4CoAZU6dcUFYRERG59Jzr1yd4QH8iwkPpcHVbSkpKWLNhI5FLotm2cxdWq9XoiCIXLS4hkcH9+xkdQ0RERAxSqUs1vTw9ycwqu1Fq/z69OBIfD8CW7Tu469abcXJyorSkhM4dOzBn3vyz1u/RrQt33vofHnl6TLkbAXu4u5Obl4fFYqFpk8b4N2tKckpKZaKKiIjIZdKuTRAR4aEED+hPAxcXjsQnMPmzz4levpKcEyeMjidSKfEJibi7NcTTw4OsnByj44iIiEgVq1Rx9tgD99O61RVghZTjqbw7ueyMr7z8fOb8/Aszpk7BarWyaes2Nm7ZCsBLTz/JL4sWc+DQYZ55bBT1HB2Z8t44oGyAgAkffUyna9rzwN0jKC0txWq1MmHyx+Tm5VdyU0VERORScXNrSOjgQUSEh9IqMJCiopMsW72GyCXR7N2/3+h4IpdMXOJfAwSoOBMREal7KlWcvfnexHPOi16x0nbPsjON+2Cy7fHNd99X4bqr1m1g1boNlYkmIiIil5idnR1dO11LRFgI/Xv3ol69euzbf4DxH0xm2eq1Fd6LVKSmi0tIAMru2/frb7sNTiMiIiJVrdKjaoqIiEjt5uPtzQ0hQxgWGkLTJo05kZvLLwsXszA6hj/i4o2OJzVEj25deOrRRzCZTERGRfPd3B/PWmZw/76MHHEnViscPnKE18e9Z0DS8jIys8jLz+eKlhogQEREpC5ScSYiIiJnsbe3p3f364gID6Vnt67Y29uz7dedfPr1DNZu2Mip04P4iFwIk8nEs4+PYvTzL5GWnsGMqVNYt3Ez8acvgwRo3qwpI26/lQefeIa8/HwaVaPR0uMSEgkMCDA6hoiIiBhAxZmIiIjYNG/WlGFhIVw/dAhenp6kZWTw7dwfWRgdQ3LKcaPjSQ11dVAQSckptn9Dy1avoV/vnuWKs+HhYcxbsIi8/LL72mbnVJ+BJeITEunTs4fRMURERMQAKs5ERETqOCcnJwb27UNEWAidO15DqdnMhs1biFwSzeZt2zFbLEZHlBrOx9uLtLR02/O09AzatQkqt0xz/2YAfDH5fUz2Jr76dhabt+2o0pznciQhgYjwUDzc3TVSrIiISB2j4kxERKSOuqrVlUSEhRIyeCANXV05mnSMqV9+zZJlK8jMyjI6ntQx9vb2+DdryiPPjMHXx5tpH0zijgceJr+goNxyw68P48bwMAA8quhyzriE0yNrtmjOrj0qzkREROoSFWciIiJ1iGuDBgwdNICIsFDaXNWak8XFrFq7nsioaHbu3mN0PKml0jMy8fX1sT339fEmPTOz3DJp6RnsO3AAs9lMyvFUEpOSaO7fjP0HD5VbbsHiKBYsjgJgxtQplz88Z46s2YJde/ZWyXuKiIhI9aDiTEREpA7o2L4dEeGhDO7Xl/r163Mo9g8mTpnK0pWrbPeUErlc9h88SPNmTWnS2I/0jEyGDOjPa/8zYubajRsZMnAAi2OW4e7mRgt/f46lpBgT+H+kpWdQUFioAQJERETqIBVnIiIitZSnhwfhQ4MZFhZKQHN/8gsKWLJsOQuWRHPwcKzR8aQOMVssTPr4Uz56dywmk4lF0UuJS0jggbvv4sChw6zbtJnN23bQvUsX5nz9OWaLhY+/+Irc3Dyjo9vEJyQSGNDC6BgiIiJSxVSciYiI1CImk4nuXbswPCyUPj274+DgwM7de/hm9hxWrl1PcXGx0RGljtq0dRubtm4rN+3Lmd+Ve/7RtC/4aFpVprpwcQmJdO/axegYIiIiUsVUnImIiNQCTfz8uCF0KDeEDMXP14es7BzmzpvPwugYEo4mGR1PpMaLS0zkhtChNHR11eXNIiIidYiKMxERkRrK0dGRfr16EBEWSrfOnQDYvG07H346jfWbt1BaWmpwQpHa48+RNQMDWrB73+8GpxEREZGqouJMRESkhgkMCCAiPISw4MF4uLuTcjyVr76dxaKYpaSlZxgdT6RWiov/a2RNFWciIiJ1h4ozERGRGsC5fn2CB/QnIjyUDle3paSkhDUbNhK5JJptO3dhtVqNjihSqx1PS6Oo6CQtNUCAiIhInaLiTEREpBpr17YNEWEhBA/oTwMXF47EJzD5s8+JXr6SnBMnjI4nUmdYrVbiExMJDAgwOoqIiIhUIRVnIiIi1Yy7mxuhwYOICAvlysCWFBWdZNnqNUQuiWbv/v1GxxOps+ISE+nSsaPRMURERKQKqTgTERGpBuzs7Oja6VoiwkLo37sX9erVY9/+A4z/YDLLVq+lsLDQ6IgidV5cQiLhQ4JxcXHR96SIiEgdUanibOSIO4kIDyUnp+xSkc+mf8OmrdsAaBUYyPNPjaaBiwsWq4X7Hh3NqZKSC15/xO23Miw0BIvFwgdTP2PL9h2ViSoiIlIt+Xh7c0PIEIaFhtC0SWNO5Obyy8LFLIyO4Y+4eKPjicgZ4v8cWbNFc/YdOGhwGhEREakKlT7jbO68+cz+aV65afYmE2+8OIY33p1A7JE43NwaUmo2X/D6LVu0YMiA/vzfyIfw9vLk4wnjueWekVgslsrGFRERMZy9vT19enRnWFgIPbt1xd7enm2/7uTTr2ewdsPGs/7QJCLVQ1zC6ZE1WwaoOBMREakjLsulmtd17ULskThij8QBkJubd1Hr9+vdk2Wr11BSUkLK8VSSklO4OihI93UREZEarXmzZkSEhRA+NBgvT0/SMjL4du6PLIyOITnluNHxROQ8ko+ncrK4mMAWGllTRESkrqh0cXbz8AjChwSz/9Ahpkz7krz8fFr4N8NqtTL53bE0cndn2arVzPrx5wte38fLi337D9iWSUvPwMfbq7JRRUREqpyTkxMD+/YhIiyEzh2vodRsZsPmLUQuiWbztu2YdTa1SI1hsVhIOHqUwAAVZyIiInXFeYuzjyeMx7NRo7OmT5vxDb9ELmL6rNlYrVYeumcEox9+gLGTPsTe3p6O7dtx76jRnCwu5pOJ73LgcCzbd+4q9xrnWv9iDL8+jBvDwwDw8HC/qHVFREQul6taXUlEWCghgwfS0NWVo0nHmPrl1yxZtoLMrCyj44nIPxSfkMg17doZHUNERESqyHmLs8fHvHhBL7RgSTST3nkTKDtDbOeePZzIzQVg45ZtBLVudVZxlpWTU+H66ZmZ+Pr62Ob5+niTnpFZ8fsujmLB4igAZkydckFZRURELgfXBg0IGTyQYaEhtLmqNSeLi1m1dj2RUdHs3L3H6HgicgnEJSQSMngQzvXrU3TypNFxRERE5DIzVWZlL09P2+P+fXpxJD4egC3bd9AqMBAnJyfsTSY6d+xA3OlRiC5k/XUbNzNkQH8cHR1p0tiP5s2a8vtB3YBVRESqp2s7tOe1559l0Y+zeW70Y9iZ7Jg45RNuuOX/ePO9iSrNRGqRP49pA1o0NziJiIiIVIVK3ePssQfup3WrK8AKKcdTeXdy2Rlfefn5zPn5F2ZMnYLVamXT1m1s3LIVgJeefpJfFi3mwKHD51w/LiGBFWvWMufrzzGbLUyaMlUjaoqISLXi6eFB+NBghoWFEtDcn/yCAhbHLCVySQwHY2ONjicil8mfxVlgQAsOHDpscBoRERG53CpVnL353sRzzotesZLoFSvPmj7ug8kXtP43s+fyzey5lYknIiJySZlMJrp37cLwsFD69OyOg4MDO3fv4ZvZc1i5dj3FxcVGRxSRy+xYcjIlJSUaIEBERKSOqPSomiIiIrVdk8Z+3BAylGGhQ/H18SErO5u58+YTGRVDYlKS0fFEpAqZLRYSkpK4IiDA6CgiIiJSBVSciYiIVMDR0ZH+vXsSERbKdV06Yzab2bx9Bx9Mncb6zVsoLS01OqKIGCQ+IZE2V7U2OoaIiIhUARVnIiIiZ7iiZQDDwkIICx6Mh7s7KcdT+XzGtyxeupS09Ayj44lINXAkPoFB/fri5OSkS7RFRERqORVnIiJS5znXr0/wgP5EhIfS4eq2lJSUsGbDRiKXRLNt5y6sVqvREUWkGolLSMRkMhHQ3J9DsX8YHUdEREQuIxVnIiJSZ7Vr24aIsBCCB/SngYsLR+ITmPzZ50QtW8GJ3Fyj44nUKj26deGpRx/BZDIRGRXNd3N/LDf/+qFDeOzB+0nPyATg5wULiYyKNiLqedlG1mzRQsWZiIhILafiTERE6hR3NzdCgwcRERbKlYEtKSwqYvmqNURGxbB3/36j44nUSiaTiWcfH8Xo518iLT2DGVOnsG7jZuITE8stt3z1Wt7/5FODUl64pORkSktLNbKmiIhIHaDiTEREaj07Ozu6drqWiLAQ+vfuRb169di7fz/j3p/M8tVrKCwqMjqiSK12dVAQSckpJKccB2DZ6jX0693zrOKspigtLeXosWO0VHEmIiJS66k4ExGRWsvH25sbQoYwLDSEpk0acyI3l18WLmZhdAx/xMUbHU+kzvDx9iItLd32PC09g3Ztgs5abmDfPnS6pgOJSUlM/uzzaj0gR1xCIlcGBhodQ0RERC4zFWciIlKr2Nvb06dHd4aFhdCzW1fs7e3Z9utOPv16Bms3bORUSYnREUWkAus2b2bpqtWUlJRw4/XhvDbmWR577oWzlht+fRg3hocB4OHhXtUxbeISEsvOYHV01M8VERGRWkzFmYiI1ArNmzUjIiyE8KHBeHl6kpaRwcw5P7AoZqnt8jARMUZ6Ria+vj62574+3qRnZpZbJjc3z/Y4Miqaxx68v8LXWrA4igWLowCYMXXKZUh7YeISErG3t6eFvz+xcXGG5RAREZHLS8WZiIjUWE5OTgzs24eIsBA6d7yGUrOZ9Zs2ExkVw5Zt2zFbLEZHFBFg/8GDNG/WlCaN/UjPyGTIgP68Nu69cst4eXqSmZUFQN+ePar9/c/iEhIAaBnQQsWZiIhILabiTEREapyrWl1JRFgoIYMH0tDVlaNJx5j65dcsXrqcrOxso+OJyP8wWyxM+vhTPnp3LCaTiUXRS4lLSOCBu+/iwKHDrNu0mVtuGk7fnj0wm83k5uXx9oT3jY79txKTjmE2mzWypoiISC2n4kxERGoE1wYNCBk8kGGhIbS5qjUni4tZtXY9kVHR7Ny9x+h4InIem7ZuY9PWbeWmfTnzO9vjz76ewWdfz6jqWP9YSUkJSckpKs5ERERqORVnIiJSrV3boT0R4aEM6teX+k5OHIyNZeKUT4hZsYr8ggKj44lIHRaXkKDiTEREpJZTcSYiItWOZ6NGhA8ZTER4KC38/ckvKGBxzFIil8RwMDbW6HgiIgDEJyTSp0d3HBwcKC0tNTqOiIiIXAYqzkREpFqwN5no3q0rEWEhtl9Ed+7ew4zv57By7XqKi4uNjigiUk5cQiIODg74N21a7QczEBERkX+mUsXZyBF3EhEeSk7OCQA+m/6N7d4VrQIDef6p0TRwccFitXDfo6M5VVJSbv13XnmRFv7+ADR0dSUvP58RD4+iiZ8fc6Z/QeLRJAD27j/AhI8+rkxUERGpppo09mNYaAg3hAzB18eHrOxs5s6bT2RUDIlJSUbHExE5p7jTZVlgQAsVZyIiIrVUpc84mztvPrN/mldumr3JxBsvjuGNdycQeyQON7eGlJrNZ637yjvjbY9HP/RAuXvVHEtOYcTDoyobT0REqiFHR0f69+5JRFgo13XpjNlsZvP2HXwwdRrrN2/RJU8iUiMkHE3CYrFwRcsAVq1bb3QcERERuQwuy6Wa13XtQuyROGKPxAGQm5t33nUG9+/HY889fzniiIhINXFFywCGhYUQPiQYdzc3Uo6n8vmMb1m8dClp6RlGxxMRuSjFxcUcSzmuAQJERERqsUoXZzcPjyB8SDD7Dx1iyrQvycvPp4V/M6xWK5PfHUsjd3eWrVrNrB9/PudrXNuhPVnZ2Rw9lmyb1rRxY2ZO+4SCgkI+nzGT3/buq2xUERExgIuzM8ED+hMRHkL7tm0pKSlhzYaNRC6JZtvOXVitVqMjioj8Y/EJibRUcSYiIlJrnbc4+3jCeDwbNTpr+rQZ3/BL5CKmz5qN1WrloXtGMPrhBxg76UPs7e3p2L4d944azcniYj6Z+C4HDseyfeeuCt9j6KABLFu12vY8IyuL4XfcRW5uHkGtWzHhzde5feRDFBYWnrXu8OvDuDE8DAAPD/cL3GwREbnc2rVtw/CwUIIH9sfF2Zkj8QlM/uxzopat4ERurtHxREQuibjEBHp064K9yYTZYjE6joiIiFxi5y3OHh/z4gW90IIl0Ux6500A0tIz2Llnj+0Xo41bthHUulWFxZm9ycSAPr25+5HHbdNKSkooOT2QwMHDsRxLSaGFfzMOHDp89vsujmLB4igAZkydckFZRUTk8nB3cyM0eBARYaFcGdiSwqIilq9aQ2RUDHv37zc6nojIJReXkIijoyPNmjbVgCYiIiK1UKUu1fTy9CQzKwuA/n16cSQ+HoAt23dw16034+TkRGlJCZ07dmDOvPkVvka3Lp2ITzxKesZf97bxcHcnNy8Pi8VC0yaN8W/WlOSUlMpEFRGRy8TOzo6una4lIiyE/r17Ua9ePfbu38+49yezfPUaCouKjI4oInLZxCX8NbKmijMREZHap1LF2WMP3E/rVleAFVKOp/Lu5LIzvvLy85nz8y/MmDoFq9XKpq3b2LhlKwAvPf0kvyxabDt7bMiA8pdpAnS6pj0P3D2C0tJSrFYrEyZ/TG5efmWiiojIJebj7c0NIUMZFjqUpk0acyI3l18WLmZhdAx/xMUbHU9EpEokJB4FyoqzNRs2GpxGRERELrVKFWdvvjfxnPOiV6wkesXKs6aP+2ByuedvT3z/rGVWrdvAqnUbKhNNREQuA3t7e/r06E5EeCg9unbB3t6ebb/u5NOvZ7B2w0ZOnb7MXkSkrig6eZKU46kaWVNERKSWqvSomiIiUvs1b9aMiLAQrg8ZgmejRqRlZDBzzg8sillKcspxo+OJiBgqLiGBwIAAo2OIiIjIZaDiTEREKuTk5MTAvn2ICAuhc8drKDWbWb9pM5FRMWzZtl2jx4mInBaXkEiXTtdiMpmw6GejiIhIraLiTEREyglq1YqI8BBCBg/CtUEDjiYdY+qXX7N46XKysrONjiciUu0cSUjAqV49mjb2IylZA1qJiIjUJirOREQE1wYNCBk8kIiwUIJat+JkcTGr1q4nMiqanbv3GB1PRKRai7eNrBmg4kxERKSWUXEmIlKHXduhPRHhoQzq15f6Tk4cjI1l4pRPiFmxivyCAqPjiYjUCHGnR9ZsGdCCdZs2G5xGRERELiUVZyIidYxno0aEDxlMRHgoLfz9yS8oYHHMUiKXxHAwNtboeCIiNU5hYSGpaekaWVNERKQWUnEmIlIH2JtMdO/WlYiwEPr06I6DgwM7d+9hxvdzWLl2PcXFxUZHFBGp0eISEwlsoeJMRESktlFxJiJSizVp7Mew0BBuCBmCr48PWdnZzJ03n8ioGBKTkoyOJyJSa8QlJHBjeDh2dnZYrVaj44iIiMglouJMRKSWcXR0pH/vnkSEhXJdl86YzWY2b9/BB1OnsX7zFkpLS42OKCJ1UI9uXXjq0UcwmUxERkXz3dwfK1xuYN/ejH/9Ve559HEOHDpcxSn/ufiERJyd69PYz5eU46lGxxEREZFLRMWZiEgtcUXLAIaFhRA+JBh3NzdSjqfy+YxvWbx0KWnpGUbHE5E6zGQy8ezjoxj9/EukpWcwY+oU1m3cTHxiYrnlXJydueWmG9m7f79BSf+5uDNG1lRxJiIiUnuoOBMRqcFcnJ0JHtCfiPAQ2rdty6lTp1i7cRORS6LZtnOXLhcSkWrh6qAgkpJTSE45DsCy1Wvo17vnWcXZg/eM4LsffuLOW/5jRMxK+as4a8HGLVsNTiMiIiKXioozEZEaqF3bNgwPCyV4YH9cnJ05Ep/Ah59OI3r5Sk7k5hodT0SkHB9vL9LS0m3P09IzaNcmqNwyQa1a4efrw8YtW2tkcZaXn096RqYGCBAREallVJyJiNQQ7m5uhAYPIiIslCsDW1JYVMTyVWtYEBXNvv0HjI4nIvKP2dnZ8cQjD/L2hPfPu+zw68O4MTwMAA8P98sd7aLEJyYSGKDiTEREpDZRcSYiUo3Z2dnRrdO1RISH0q9XT+rVq8fe/fsZ9/5klq9eQ2FRkdERRUTOKz0jE19fH9tzXx9v0jMzbc9dXJy5omUAn74/AQBPz0ZMfOsNnnvtjbMGCFiwOIoFi6MAmDF1yuUPfxHiEhK5PmSI0TFERETkElJxJiJSDfl4e3NDyFCGhQ6laZPGnMjN5ZeFi1kYHcMfcfFGxxMRuSj7Dx6kebOmNGnsR3pGJkMG9Oe1ce/Z5hcUFBL671ttzz99fwJTPv+yRo2qCRCXkEADFxd8fbw1KIuIiEgtoeJMRKSasLe3p0+P7kSEh9Kjaxfs7e3ZuuNXPv16Oms3bOJUSYnREUVE/hGzxcKkjz/lo3fHYjKZWBS9lLiEBB64+y4OHDrMuk2bjY54SZw5sqaKMxERkdpBxZmIiMFa+PsTERZC+NBgPBs1Ii0jg5lzfmBRzFLbCHQiIjXdpq3b2LR1W7lpX878rsJlH31mTFVEuuTOHFlzy/YdBqcRERGRS6HSxdnIEXcSER5KTs4JAD6b/g2btm4jZNBA7jhjRKRWVwRy9yOPcfiPI+XWd2voyjuvvEQTPz9SUlN5+e1x5OXnA/D0qEfoeV03iouLeXvC+xyMja1sXBGRasHJyYlB/foQERZKp2s6UGo2s37TZiKjYtiybTtmi8XoiCIicpFO5OaSlZ3DFQEBRkcRERGRS+SSnHE2d958Zv80r9y0mJWriFm5CoArA1vy3puvnVWaAYy47Va27dzFd3N/5K7bbmHEbbcw9avp9LyuG82bNeXmu++jXds2jHniMe5//MlLEVdExDBBrVoRER5CyOBBuDZoQGJSElO//JrFS5eTlZ1tdDwREamkuIQEWmpkTRERkVqjSi7VHDJwAMtXralwXt9ePW2n4y9ZupxP35/A1K+m069XT5YsWwHAvv0HcHV1xcvTk8ysrKqILCJyybg2aEDI4IFEhIUS1LoVJ4uLWbl2HZFLotm1Z6/R8URE5BKKS0gkZPBAo2OIiIjIJXJJirObh0cQPiSY/YcOMWXal7ZLLf8UPKAfY157s8J1PRt52MqwzKwsPBt5AODj7UVaerptubT0dHy8vc4qzoZfH8aN4WEAeHi4X4rNERG5JDpd04FhYSEM6teX+k5OHIyNZeKUT4hZsYr8ggKj44mIyGUQl5BIQ1dXvL08ycjUH3xFRERqugsqzj6eMB7PRo3Omj5txjf8ErmI6bNmY7VaeeieEYx++AHGTvrQtky7NkGcLC7mSHzCBQWyWq0XGL3MgsVRLFgcBcCMqVMual0RkUvNs1Ejrh8azLCwEFr4+5NfUMDimKVELonRfRpFROqAuISyY97AgAAVZyIiIrXABRVnj4958YJebMGSaCa9U/7MsuCB/Vm2cvU518nKzrFdgunl6Un26UEG0jMy8fXxsS3n6+NDekbmBeUQEalK9iYT3bt1JSIshD49e+Bgb8/O3XuY8f0cVq5dT3FxsdERRUSkisT/ObJmixZs+3WnwWlERESksip9qeaZ9x3r36cXR+LjbfPs7OwY3L8fDz/17DnXX7dpM+FDg/lu7o+EDw1m3cZNtuk3Dx/GslWrade2DfkFBbq/mYhUK00a+zEsNIQbQobg6+NDVnY2c36ax8LopSQmJRkdT0REDJCVk8OJ3FwNECAiIlJLVLo4e+yB+2nd6gqwQsrxVN6d/Nflkp2u6UBaejrJKcfLrfPS00/yy6LFHDh0mG/n/sDYV14iIjSE42lpvPz2WAA2btlKr+u68fO30zlZXMw7Ez+obFQRkUqr5+hIv969iAgL4bounTGbzWzevoP3P/mM9Zu3YDabjY4oIiIGi0tIJFDFmYiISK1Q6eLszfcmnnPer7/tZuTjT501fdwHk22Pc3Pzznkp6KSPp1Y2nojIJXFFywAiwkIJGzIYdzc3Uo6n8vmMb1m8dClp6RlGxxMRkWokLiGRQf36GB1DRERELoFLMqqmiEht5OLsTPCA/kSEh9C+bVtOnTrF2o2biFwSzbaduy56MBMREakb4uITcL8hHE8PD7JycoyOIyIiIpWg4kxE5H+0a9uG4WGhBA/sj4uzM0fiE/jw02lEL1/Jidxco+OJiEg1F5dYNkBAy4AWKs5ERERqOBVnIiKAu5sbYUMGMyw0hCsDW1JYVMTyVWtYEBXNvv0HjI4nIiI1SFxCAgCBAQH8+ttug9OIiIhIZag4E5E6y87Ojm6driUiPJR+vXpSr1499u7fz7j3J7N89RoKi4qMjigiIjVQRmYWefn5GiBARESkFlBxJiJ1jo+3NzeEDGVY6FCaNmnMidxcflm4mIXRMfwRF290PBERqQU0sqaIiEjtoOJMROoEe3t7+vToTkR4KD26dsHe3p6tO37l06+ns3bDJk6VlBgdUUREapH4hET69OxudAwRERGpJBVnIlKrtfD3JyIshPChwXg2akRaRgYz5/zAopilJKccNzqeiIjUUnEJiUSEh+Lu5qaBZURERGowFWciUus4OTkxqF8fIsJC6XRNB0rNZtZv2kxkVAxbtm3HbLEYHVFERGq5uMQ/Bwhowa49ew1OIyIiIv+UijMRqTWCWrUiIjyEkMGDcG3QgMSkJKZ++TWLly4nKzvb6HgiIlKHHIlXcSYiIlIbqDgTkRrNtUEDQgYPJCIslKDWrThZXMzKteuIXBKtX1RERMQwaekZFBQWEhgQYHQUERERqQQVZyJSI3W6pgPDwkIY1K8v9Z2cOBgby8QpnxCzYhX5BQVGxxMRkf/Ro1sXnnr0EUwmE5FR0Xw398dy82+6IZx/Dx+GxWyh6ORJxn/wEfGJiQalvTTiNbKmiIhIjafiTERqDM9Gjbh+aDDDwkJo4e9PfkEBi2OWErkkhoOxsUbHExGRczCZTDz7+ChGP/8SaekZzJg6hXUbN5crxmJWrmb+oiUA9O3ZgyceeZCnXnzFqMiXRFxCIt27djE6hoiIiFSCijMRqdbsTSa6d+tKRFgIfXr2wMHenp279zDj+zmsXLue4uJioyOKiMh5XB0URFJyim0042Wr19Cvd89yxVlhYaHtcf369cFqrfKcl1pcYiI3hA6loasrefn5RscRERGRf0DFmYhUS02bNGZYaAjXhwzB19ubrOxs5vw0j4XRS0lMSjI6noiIXAQfby/S0tJtz9PSM2jXJuis5f4dMYzb/3MTjg6OPPbc81UZ8bKISygrBlu2aM6e3/cbnEZERET+CRVnIlJt1HN0pF/vXgwPD6Vb506YzWY2b9/B+x9/yvrNWzCbzUZHFBGRy2he5ELmRS5k6KAB3HPH7bw94f2zlhl+fRg3hocB4OHhXtURL0pcwp8jawaoOBMREamhVJyJiOGuDGzJsNAQwoYMxt3NjZTjqXw+41sWL11KWnqG0fFERKSS0jMy8fX1sT339fEmPTPznMsvW7WGMU88ztucXZwtWBzFgsVRAMyYOuXSh72EjqemUVR0ksCWGiBARESkpqpUcTZyxJ1EhIeSk3MCgM+mf8OmrdsIGTSQO275j225VlcEcvcjj3H4jyPl1n/swZH06dGd0tJSkpKTeWfiB+QXFNDEz485078g8WjZ5Vh79x9gwkcfVyaqiFQzLs7OBA/oT0R4CO3btuXUqVOs3biJyCXRbNu5C2stuLeNiIiU2X/wIM2bNaVJYz/SMzIZMqA/r417r9wyzZs15eixZAB6d7+Oo0nHjIh6SVmtVuITEwkMCDA6ioiIiPxDlT7jbO68+cz+aV65aTErVxGzchVQdibJe2++dlZpBrB1x6989tV0zBYLo0bex92338rUr6YDcCw5hREPj6psPBGpZtq3bUtEeCjBA/rh4uzMkfgEPvx0GtHLV3IiN9foeCIichmYLRYmffwpH707FpPJxKLopcQlJPDA3Xdx4NBh1m3azH+GR9CtcydKS0vJy8/nrQou06yJ4hIT6dKxo9ExRERE5B+67JdqDhk4gOWr1lQ4b+uOX22P9+4/wKB+fS53HBExgLubG2FDBhMRFsoVLQMoLCpi+ao1LIiKZt/+A0bHExGRKrBp6zY2bd1WbtqXM7+zPf7w02lVHalKxCUkEj4kGBcXl3Ijh4qIiEjNUOni7ObhEYQPCWb/oUNMmfblWUNtBw/ox5jX3jzv6wwLHcry1Wttz5s2bszMaZ9QUFDI5zNm8tvefZWNKiJVyM7Ojm6driUiPJT+vXvh6OjI3v37Gff+ZJavXkNhUZHREUVERC67+NMjawa2aM6+AwcNTiMiIiIX67zF2ccTxuPZqNFZ06fN+IZfIhcxfdZsrFYrD90zgtEPP8DYSR/almnXJoiTxcUciU/42/e45/9uo9RsJnrFSgAysrIYfsdd5ObmEdS6FRPefJ3bRz5U4V/patLISiJ1ga+PN9cPHUpEWAhNGvtxIjeXnyMXsjAq5rw/C0RERGqbP0fWbBnQQsWZiIhIDXTe4uzxMS9e0AstWBLNpHfKn1kWPLA/y1au/tv1rh86hN49uvPYcy/YppWUlFBSUgLAwcOxHEtJoYV/Mw4cOnz2+9agkZVEait7e3v69uxBRFgI3bt2wd7enq07fmXqV1+zZsMm2/eziIhIXZN8PJXiU6c0QICIiEgNValLNb08PcnMygKgf59eHImPt82zs7NjcP9+PPzUs+dcv0e3Ltx563945OkxFBcX26Z7uLuTm5eHxWKhaZPG+DdrSnJKSmWiishl0MLfn4iwEMKHBuPZqBFp6enMnPMDC6NjSDmeanQ8ERERw1ksFhISjxIY0MLoKCIiIvIPVKo4e+yB+2nd6gqwQsrxVN6d/NcZX52u6UBaejrJKcfLrfPS00/yy6LFHDh0mGceG0U9R0emvDcOKBsgYMJHH9PpmvY8cPcISktLsVqtTJj8Mbl55e+dJiLGcHJyYlC/PkSEhdLpmg6UlpayftMWFkRFs2X7DiwWi9ERRUREqpW4xEQ6tG1rdAwRERH5BypVnL353sRzzvv1t92MfPyps6aP+2Cy7fHNd99X4bqr1m1g1boNlYkmIpdYUKtWRISHEDJ4EK4NGpCYlMQnX3zFkmUryMrONjqeiIhItRUXn0DIoIE4169P0cmTRscRERGRi1DpUTVFpPZq6OrK0EEDiQgPIahVK04WF7Ny7Toil0Sza89eo+OJiIjUCHGnR9YMaNG8wnv2ioiISPWl4kxEztLpmg5EhIUysF8f6js5cfBwLBOnfELMilXkFxQYHU9ERKRG+bM4CwxooeJMRESkhlFxJiIAeDZqxPVDgxkWFkILf3/y8vNZFL2UhVExHIyNNTqeiIhIjXUsOZmSkhINECAiIlIDqTgTqcPsTSa6d+tKRFgIfXr2wMHenl9/2830WXNYtW59udFuRURE5J8xWywkJCUR2CLA6CgiIiJykVScidRBTZs0ZlhoCNeHDMHX25us7Gzm/DSPhdFLSUxKMjqeiIhIrROfkEhQ69ZGxxAREZGLpOJMpI6o5+hIv969GB4eSrfOnTCbzWzevoP3P/6U9Zu3YDabjY4oIiJSa8UlJDKoX1+c6tWj+NQpo+OIiIjIBVJxJlLLXRnYkmGhIYQNGYy7mxvJKcf5fMZMFsUsIz0jw+h4IiIidUJcQiImk4kWzf05/McRo+OIiIjIBVJxJlILuTg7EzygPxHhIbRv25ZTp06xZsNGIqNi2L5zF1ar1eiIIiIidcqfI2teERCg4kxERKQGUXEmUou0b9uWiPBQggf0w8XZmT/i4vnw02lEL1/Jidxco+OJiIjUWUePHaO0tFQja4qIiNQwKs5Eajh3NzfChgwmIiyUK1oGUFhUxPJVa1gQFc2+/QeMjiciIiJAaWkpR48do6WKMxERkRpFxZlIDWRnZ0e3TtcSER5K/969cHR0ZM/v+xk76UNWrFlLYVGR0RFFRETkf8QlJHJlYKDRMUREROQiqDgTqUF8fby5fuhQIsJCaNLYjxO5ufwcuZCFUTEciU8wOp6IiIj8jbiERNsfvEpKSoyOIyIiIhdAxZlINefg4ECfHt2JCAuhe9cu2Nvbs3XHr0z96mvWbNikA28REZEaIi4hEXt7e1r4N+OPuHij44iIiMgFUHEmUk218PcnIiyE8KHBeDZqRFp6OjPn/MDC6BhSjqcaHU9EREQuUvzpkTUDA1qoOBMREakhVJyJVCNOTk4M7teXYWEhdLqmA6WlpazftIUFUdFs2b4Di8VidEQREZF/pEe3Ljz16COYTCYio6L5bu6P5ebf/u9/EREegtlsITsnh7GTPuR4WppBaS+PxKQkzGYzLVtogAAREZGaQsWZSDUQ1LoVw8NDGTpoIK4NGpCYlMQnX3zFkmUryMrONjqeiIhIpZhMJp59fBSjn3+JtPQMZkydwrqNm4lPTLQtczA2lnseXUxxcTH/GnY9jz14P6+8M97A1JfeqZISjqWkcEXLAKOjiIiIyAWqVHE2csSdRISHkpNzAoDPpn/Dpq3bCBk0kDtu+Y9tuVZXBHL3I49x+I8jF7Q+wIjbb2VYaAgWi4UPpn7Glu07KhNVpNpp6OrK0EEDiQgPIahVK04WF7Ny7Toil0Sza89eo+OJiIhcMlcHBZGUnEJyynEAlq1eQ7/ePcsVZ7/+ttv2eO/+A4QOHlTlOavCkfgEAgN0xpmIiEhNUekzzubOm8/sn+aVmxazchUxK1cBcGVgS95787WzSrO/W79lixYMGdCf/xv5EN5ennw8YTy33DNSl6lJrdDpmg5EhIUysF8f6js5cfBwLBOnfELMilXkFxQYHU9EROSS8/H2Ii0t3fY8LT2Ddm2Czrn8sNAQNm3bXhXRqlx8QiJ9enTHwcGB0tJSo+OIiIjIeVz2SzWHDBzA8lVrLmqdfr17smz1GkpKSkg5nkpScgpXBwWxd//+yxNS5DLz8vQkfGgwEaEhNPdvRl5+Pouil7IwKoaDsbFGxxMREak2QgcPom1Qax55ekyF84dfH8aN4WEAeHi4V2W0SyIuIREHBwf8mzYtd8adiIiIVE+VLs5uHh5B+JBg9h86xJRpX5KXn19ufvCAfox57c2LWt/Hy4t9+w/YlklLz8DH26uyUUWqlL3JRI9uXYkID6V3j+442Nvz62+7+XrWbFatW09xcbHREUVERKpEekYmvr4+tue+Pt6kZ2aetVy3zp245/9u45FnnqOkpKTC11qwOIoFi6MAmDF1yuUJfBnFJf41sqaKMxERkervvMXZxxPG49mo0VnTp834hl8iFzF91mysVisP3TOC0Q8/wNhJH9qWadcmiJPFxRyJT6jwtc+3/oWo6X91lNqnaZPGDAsN4fqQIfh6e5OVnc2cn+axMHopiUlJRscTERGpcvsPHqR5s6Y0aexHekYmQwb057Vx75Vb5qpWV/L8k4/z1IuvkH36/re1UcLRJCwWC4EBLVi1zug0IiIicj7nLc4eH/PiBb3QgiXRTHqn/JllwQP7s2zl6nOuk5WTU+H66ZkV/FUy4+y/SkLN/6uj1A71HB3p17sXw8ND6da5E2azmc3bd/D+x5+yfvMWzGaz0RFFREQMY7ZYmPTxp3z07lhMJhOLopcSl5DAA3ffxYFDh1m3aTOPPzgSF2dnxr76MgCpaek899obxga/DIqLi0k+flwDBIiIiNQQlbpU08vTk8ysLAD69+nFkfh42zw7OzsG9+/Hw089e9Hrr9u4mbdeep45P/+Ct5cnzZs15feDBysTVeSyuDKwJRFhoYQGD8LdzY3klON8PmMmi2KWkZ6RYXQ8ERGRamPT1m220dP/9OXM72yPL/SPtbVBfEIigQEBRscQERGRC1Cp4uyxB+6ndasrwAopx1N5d/JfZ3x1uqYDaenptmHH//TS00/yy6LFHDh0+JzrxyUksGLNWuZ8/Tlms4VJU6ZqRE2pNlycnRkysD8RYaG0a9uGU6dOsWbDRiKjYti+cxdWq9XoiCIiIlKNxSUkcl2XztibTJh1jCsiIlKtVao4e/O9ieec9+tvuxn5+FNnTR/3weQLWv+b2XP5ZvbcysQTuaTat21LRHgowQP64eLszB9x8Xz46TSil6/kRG6u0fFERESkhjiSkEC9evVo1rSp7n8qIiJSzVV6VE2R2szdzY2wIYOJCAvlipYBFBYVsXzVGhZERZcb+VVERETkQsUl/DWypoozERGR6k3Fmcj/sLOzo1vnTkSEh9K/V08cHR3Z8/t+xk76kBVr1lJYVGR0RBEREanBEhKPAmXF2ZoNGw1OIyIiIn9HxZnIab4+3twQMpRhoSE0aezHidxcfo5cyMKoGI7EJxgdT0RERGqJopMnSTmeqpE1RUREagAVZ1KnOTg40KdHdyLCQujRrSsmk4mtO35l6ldfs2bDJkpKSoyOKCIiIrVQXEICLVWciYiIVHsqzqROauHvT0RYCOFDg/Fs1Ii09HS+mT2XhdExpBxPNTqeiIiI1HJxCYl0vrYjJpNJo8eLiIhUYyrOpM5wcnJicL++DAsLodM1HSgtLWX9pi0siIpmy/YdOmgVERGRKhOXmEh9Jyea+PlxLCXF6DgiIiJyDirOpNYLat2K4eGhDB00ENcGDUhMSuKTL75iybIVZGVnGx1PRERE6qAzR9ZUcSYiIlJ9qTiTWqmhqytDBw0kIjyEoFatOFlczMq164hcEs2uPXuNjiciIiJ1nK04axnA+s1bDE4jIiIi56LiTGqVTtd0ICIslIH9+lDfyYkDhw4zcconxKxYRX5BgdHxRERERAAoLCwkNS1dI2uKiIhUcyrOpMbz8vQkfGgwEaEhNPdvRl5+Pouil7IwKoaDsbFGxxMRERGpUFxiIoEtVJyJiIhUZyrOpEayN5no0a0rEeGh9O7RHQd7e379bTdfz5rNqnXrKS4uNjqiiIiIyN+KS0jgxvBw7OzssFqtRscRERGRCqg4kxqlaZPGDAsN4fqQIfh6e5OZlcWcn+YRGRXD0WPHjI4nIiIicsHiExJxdq5PY19fUlJTjY4jIiIiFVBxJtVePUdH+vfpTURYCN06d8JsNrNp23YmTZnKhi1bMZvNRkcUERERuWh/DhDQMqCFijMREZFqSsWZVFtXBrYkIiyU0ODBuLs1JDnlOJ/PmMmimGWkZ2QYHU9ERESkUuITT4+sGdCCTVu3GZxGREREKqLiTKoVF2dnhgzsT0RYKO3atuHUqVOs2bCRyKgYtu/cpft/iIiISK2Rm5dPRmaWRtYUERGpxlScSbXQ4eq2DAsLJXhAP1ycnfkjLp4PP51G9PKVnMjNNTqeiIiIyGURl5DAFQEBRscQERGRc6hUcTZyxJ1EhIeSk3MCgM+mf8OmrdsIGTSQO275j225VlcEcvcjj3H4jyPl1n/nlRdp4e8PQENXV/Ly8xnx8Cia+PkxZ/oXJB5NAmDv/gNM+OjjykSVasjD3Z2w4MFEhIcQGBBAYVERy1atJnJJNPsOHDQ6noiIiMhldzD2D277900ED+jH8tVrjY4jIiIi/6PSZ5zNnTef2T/NKzctZuUqYlauAsruU/Xem6+dVZoBvPLOeNvj0Q89QH5Bge35seQURjw8qrLxpJqxs7OjW+dORISH0r9XTxwdHdnz+37GTvqQFWvWUlhUZHREERERkSoz4/vZtGsTxFsvvUADFxcWLIk2OpKIiIic4bJfqjlk4ACWr1pz3uUG9+/HY889f7njiEF8fby5IWQow0JDaNLYjxO5ufwcuZCFUTEciU8wOp6IiIhcZj26deGpRx/BZDIRGRXNd3N/LDf/2g7teerRh7nyikBefWc8q9atNyhp1SooKOTJF19h/Gsv8+LTT+Lq6sr3P/5sdCwRERE5rdLF2c3DIwgfEsz+Q4eYMu1L8vLzy80PHtCPMa+9+bevcW2H9mRlZ3P0WLJtWtPGjZk57RMKCgr5fMZMftu7r7JRpYo5ODjQp0d3IsJC6NGtKyaTiS3bdzD1q69Zs2ETJSUlRkcUERGRKmAymXj28VGMfv4l0tIzmDF1Cus2braNKgmQmpbO2xPe5/9u+beBSY1RXFzMmNff4vXnn+XxB0fSsEEDps2YaXQsERER4QKKs48njMezUaOzpk+b8Q2/RC5i+qzZWK1WHrpnBKMffoCxkz60LdOuTRAni4vPe0bR0EEDWLZqte15RlYWw++4i9zcPIJat2LCm69z+8iHKCwsPGvd4deHcWN4GAAeHu7n2xypAgHN/RkWFkr4kGA8G3mQlp7OjO/nsChmKSnHU42OJyIiIlXs6qAgkpJTSE45DsCy1Wvo17tnueIsJbXsGMFqqZsjaJeWlvL6+AkUFBZyzx234+rqyvuffKoRxUVERAx23uLs8TEvXtALLVgSzaR3yp9ZFjywP8tWrv7b9exNJgb06c3djzxum1ZSUmI7G+ng4ViOpaTQwr8ZBw4dPvt9F0exYHEUADOmTrmgrHLp1a/vxKC+fYkID+XaDu0pLS1l/aYtLIiKZsv2HVgsFqMjioiIiEF8vL1IS0u3PU9Lz6BdmyADE1VPFouFdz+cQn5+AXfeejMNXFx4Z9IHmM1mo6OJiIjUWZW6VNPL05PMrCwA+vfpxZH4eNs8Ozs7Bvfvx8NPPfu3r9GtSyfiE4+SnpFhm+bh7k5uXh4Wi4WmTRrj36wpySkplYkql0lQ61YMDw9l6KCBuDZoQGJSEp988RVLlq0gKzvb6HgiIiJSy9SFqw0++fJrcvPzefT+e2nQwIVX3h7HKd3iQkRExBCVKs4ee+B+Wre6AqyQcjyVdyf/dcZXp2s6kJaebjsl/08vPf0kvyxabDt7bMiA8pdplq3bngfuHkFpaSlWq5UJkz8mN6/8vdPEOA1dXRk6aCAR4SEEtWrFyeJiVq5dR+SSaHbt2Wt0PBEREalm0jMy8fX1sT339fEmPTPzH71WXbna4Ns5P1BQUMBzox/jg3FvM+a1NzX6uIiIiAEqVZy9+d7Ec8779bfdjHz8qbOmj/tgcrnnb098/6xlVq3bwKp1GyoTTS6Dzh2vISIslAF9e1PfyYkDhw4z4aOPWbpyNfkFBUbHExERkWpq/8GDNG/WlCaN/UjPyGTIgP68Nu49o2NVe/MiF1FQUMgrY57h4wnjeeqlV/THZBERkSpW6VE1pXbz8vQkfGgwEaEhNPdvRl5+Pouil7IwKoaDsbFGxxMREZEawGyxMOnjT/no3bGYTCYWRS8lLiGBB+6+iwOHDrNu02baBl3Fe2+8SkPXhvTp2Z0H7r6L/xv5kNHRDRe9YiUFRYW888pLfPrBRJ54/mXbrVJERETk8lNxJmexN5no0a0rEeGh9O7RHQd7e379bTdfz5rNqnXrKS4uNjqiiIiI1DCbtm5j09Zt5aZ9OfM72+P9Bw8RcftdVR2rRli3cTNPv/QqE99+g88nT+LxMS9qpHIREZEqouJMbJo2acyw0BCuDxmCr7c3mVlZzPlpHpFRMRw9dszoeCIiIiJ11o5dv/H4cy/wwbh3+Hzy+4we8xLxiYlGxxIREan1VJzVcfUcHenfpzcRYSF069wJs9nMpm3bmTRlKhu2bNXw5yIiIiLVxL4DB3nk6WeZ8t54pn04kSdffMU24JaIiIhcHirO6qgrA1sSERZKaPBg3N0akpxynM9nzGRRzDLSMzKMjiciIiIiFTgSn8BDTz7DlAnjmDrxXZ599Q127t5jdCwREZFaS8VZHeLi7MyQgf2JCAulXds2nDp1ijUbNhIZFcP2nbuwWq1GRxQRERGR8ziWksLDTz7LR++N48Px7/DyW2PZsGWr0bFERERqJRVndUCHq9syLCyU4AH9cHF25o+4eD78dBrRy1dyIjfX6HgiIiIicpHSMzN55Onn+HD8O7z35mu8+d4klq1abXQsERGRWkfFWS3l4e5OWPBgIsJDCAwIoLCoiGWrVhO5JJp9Bw4aHU9EREREKulEbi6PPfcCk95+gzdfHINrAxfmL1pidCwREZFaRcVZLWJnZ0e3zp2ICA+lf6+eODo6suf3/Yyd9CEr1qylsKjI6IgiIiIicgkVFhby1IuvMPa1l3n+ydG4NmjAdz/8ZHQsERGRWkPFWS3g6+PNDSFDGRYaQpPGfpzIzeXnyIUsjIrhSHyC0fFERERE5DIqPnWK519/i9eef5ZRD9yPq6srn309w+hYIiIitYKKsxrKwcGBPj26ExEWQo9uXTGZTGzZvoOpX33Nmg2bKCkpMTqiiIiIiFQRs9nMm+9OpKCggLtvv5WGrg2YOGWqBn8SERGpJBVnNUxAc3+GhYUSPiQYz0YepKWnM+P7OSyKWUrK8VSj44mIiIiIQSwWCxM++oT8ggJG3HYrDVxceGvC+5jNZqOjiYiI1FgqzmqA+vWdGNS3LxHhoVzboT2lpaWs37SFBVHRbNm+A4vFYnREEREREakmPv1qBnn5BYwaeR8NXFx4+e1xFJ86ZXQsERGRGknFWTXW5qrWRISFMnTQAFwbNCAxKYlPvviKJctWkJWdbXQ8EREREammvpv7I/n5+Tw3+jE+GPc2z736hgaKEhER+QdUnFUzDV1dCRk8kIiwUK5qdSUni4tZuXYdkUui2bVnr9HxRERERKSGmL9oCQWFhbz2/HN8Muk9vvhmJn/EJZCekWF0NBERkRpDxVk10bnjNUSEhTKwXx+c6tXjwKHDTPjoY5auXE1+QYHR8URERESkBlq6cjUFBYWMffUlJo8fC0Befj5x8Qn8EZ9w+nM8R+Ljyc45YXBaERGR6qfSxdnIEXcSER5Kzun/aD+b/g2btm7D3t6el555kqDWrXAw2bNk+Qq+nfPDWes3aezHOy+/iJubGwcPH+aNdydSWlqKo6Mjrz//LEGtW5Obm8sr74wnJbV23fzey9OT8KHBRISG0Ny/GXn5+SyMimFhVAwHY2ONjiciIiIitcCGLVu54dY7aHVFIFcGtuSKlgEEBgQwqF8f3G8Ity2XnZPDkfiE0x/xtsd5+fkGphcRETHWJTnjbO68+cz+aV65aYP796WeoyN3PvAITk5OzP36C5atXH1W+TXqgfuZM28+y1evYcwTjxMRFsIvCxcTERZCbl4+N999H8ED+jPqgft45Z3xlyKuoexNJnpe142I8FB6db8OB3t7fv1tN1/Pms2qdespLi42OqKIiIiI1DL5BQXs2rP3rFt/eDZqZCvTyj5aEj5kMA0aNLAtk5aRQZytUCsr1eISEnXPNBERqRMu26WaVis416+PvcmEk1M9SkpLKCg8+5LDrtd25PWx7wKwZOlyRt59J78sXEzfXj35auYsAFatXcezjz96uaJWiWZNmjAsdCjXhwzFx9uLzKws5vw0j8ioGI4eO2Z0PBERERGpg7Kys8nKzmbbrzvLTffz9eGKgACu+LNUCwjgphvCqV+/vm2ZlOOp/BEfX+6yz4Sko5w8qT8Ei4hI7XFJirObh0cQPiSY/YcOMWXal+Tl57Ny7Tr69erBoh9nU9+pPpOnfU5uXvnTvN3d3MjLL8BssQCQlpGOj5cXAD5eXqSmpwNgtljILyjA3c2NE7m5lyJylajn6Ej/Pr2JCAuhW+dOmM1mNm3bzsQpn7Bhy1bMZrPREUVEREREzpKalk5qWjqbtm23TTOZTDTx8+OKwACubNmSwJZln7t36Yyjo6NtuYLCQrKyssnKySEzK4vMrGyysnPIyv7zcTaZ2WXTSkpKjNg8ERGRC3ZBxdnHE8bj2ajRWdOnzfiGXyIXMX3WbKxWKw/dM4LRDz/A2Ekf0q5NEBaLhRtuvQO3hq5M+/B9tv26k+SU45d0A4ZfH8aN4WEAeHi4X9LX/qdaBQYyLCyE0ODBuLs1JDnlOJ/PmMmimGUaxUhEREREaiSLxcKxlBSOpaSwbuNm23R7e3uaN2tKYEAA/s2a4tnIA69Gnng28iAwoAVdrr0Wd7eGFb7midw8sk8XaeVKtaxsMrOzyMrOITMrm5ycHNsf20VERKrSBRVnj4958YJebMGSaCa98yYAQwcNZNO2HZjNZrJzTrB73z7aXtW6XHF2IjeXhq4NsDeZMFss+Hr7kJ6ZCUB6ZiZ+Pj6kZ2RgbzLh2qBBhWebLVgcxYLFUQDMmDrlgnJeDi4uLgwZ2J+IsFDatQni1KlTrNmwkcioGLbv3IXVajUsm4iIiIjI5WI2m4lPPEp84tFzLuPo6EgjD3e8PD3xatQIz0aN8PIs+/zn47ZXtcazkUe5+6v9yWKxkHMi13Zpac6JXHLz8v76yM07+3l+PqWlpZdz00VEpA6o9KWaXp6eZGZlAdC/Ty+OxMcDkJqWRtdrOxK9fAX16zvRvm0bfpj337PW37FrNwP79WX56jWEDw1m3cZNAKzbuJnwocHs3b+fgf36sn3Xb5WNell0uLotEeGhBPfvj7Nzff6Ii+eDqZ8RvWIlubl5RscTERERqRZ6dOvCU48+gslkIjIqmu/m/lhufl0YUb0uKykpIS09g7T08199Ub++U1mZdo6CzcuzEY39fHFr2JCGrq7Y29uf87UKCgv/p1zLP3/hlpdH8alTl3LzpZpwc2uIVyPPsn9HjRrh5VVW5Hp5etr+nXl5NsKtYUNO5OaSnpFJWkbZv9uyx+mkp2eQdnp6YWGh0ZskIlWg0sXZYw/cT+tWV4C17Aah704uO+vr5wULeeW5Z5j91efY2cGimGXExsUB8MHYtxj3wWQyMrOY+tXXvP3yizx0790civ2DyKgYABZGRfP6C2P4aeZ0cvPyeHVs9RlR08PdnbDgwUSEhxAYEEBhURFLV60ickk0+w4cNDqeiIiISLViMpl49vFRjH7+JdLSM5gxdQrrNm4mPjHRtkxtHVFdLt7Jk8Ukpxy/oFu82NnZ0cDFBTe3hrg1/OvD/X+euzVsSMOGrgQGtLDNP/O+bGdlKC6msLCIkpISSkpLKC0ppaS07KO0pOTsxyWltmVLSkopLS0t//j0cqWnlz1VUkJpaQmlpWZKS0sxWyyYzWbbh+X089IzHts+bM8tp5c1U2p7/NfnusLJyemvIszT01aC/TnN03aWo0eF+/xkcbHtXnxHk46xa88ecvPycXdriK+3Nz4+3lwdFIRnI4+z1i0oKCAt43Splp5x+nGG7XFaekaNuke3iFSs0sXZm+9NrHB60cmTvPz22ArnPf3ya7bHySnHuf+xJ85a5lRJyTnXN4KdnR3dOnciIjyU/r164ujoyJ7f9zN20oesWLNWw3GLiIiInMPVQUEkJafYipBlq9fQr3fPcsVZbRtRXaqG1Wolv6CA/IKCi76Xcv36Tn9btjVo4IKDvQMOjg44Ojjg6OhY9uHggIODAw1cXMoeOzrg6OB4xjIOp6c74lSv3mXa8vM7s5CzWCxYLBasVuvpz2C1WrBYrGWfrVasVitWixWL1XL68+lptuVOr4v1r/mWM9a1vXbZLWr+fGybV/am5d7LSvnHlj+nnZHRaqUsk9UKVnCq73S6JCsrxiq6tPfP2wVlZmeRlZXNkbh4MrKybPfOy8wqu6deZnb2BZ81Vs/REW9vL3y8vPD18cHXx7usWPP2wtfHm+u6dMLL0/OsMyCLT52ylWnpmZllZ6ylZ5CemUFefsFF39Knqm4B9E/epyqy6RZIF6c2fb1O5OaScDTJkPe+JKNq1gXdOl3LlPfGkXPiBD8vWEhkVAxxCQlGxxIRERGp9ny8vUhLS7c9T0vPoF2boPLL1IIR1aVmOXmymJMniy/o8tHKsDeZcDhduNVzdPyraHMsK9pMJhMO9vaYTCbs7e3/+vif538uZ3/WsibsTac//82ydnZ2mEx22NmZMJnsgLLnJjsTdqc/Ywem0/Nty9nZYbIrv5ydHdiZTJjs7P76fPrDZDKVbffp97ajbFk7O7CzM5Utc8by//thsjv9niY77LDDzvTn9LJ8xcWnyMrO5uDh2L/KsKw/B5goe56Tm3vJz7o7VVJy3jMh7U0mPBs1KivVfLzx8Tr92ccbHy8v2rUJwrdPb+oZWKiK1FTL16zllbfHGfLeKs4u0PZdv/Him2+zfvNWDZstIiIiYpDqOKK6yN8xWyyYi4spLi42OopcZmaLpeyssszMv72Fj7ubG36+Prg4O1/U69vZ2V10pn+yzj9RFe9TW96jqtSeLSmTlZ1j2HurOLtAFouFVes2GB1DREREpMZJz8jE19fH9tzXx9s2krptmRo2orqIyD91IjdXZ9OK1CAmowOIiIiISO22/+BBmjdrSpPGfjg4ODBkQH/Wbdxcbpk/R1QHqvWI6iIiIlK3qDgTERERkcvKbLEw6eNP+ejdscyd/gUr1qwlLiGBB+6+i749ewBlI6q7u7nx08zp3P6ff/HpV9MNTi0iIiKiSzVFREREpAps2rqNTVu3lZv25czvbI+r24jqIiIiIqAzzkRERERERERERCqk4kxERERERERERKQCKs5EREREREREREQqoOJMRERERERERESkArVqcIAmjf2YMXXKZX0PDw93cnJOXNb3kHPT19942gfG0z4wnvaB8S73PmjS2O+yvbZcOjr2u7zq8raDtl/bX3e3vy5vO2j76+r2/92xn52db5C1CrPUeDOmTuHeUaONjlFn6etvPO0D42kfGE/7wHjaB1JV6vK/tbq87aDt1/bX3e2vy9sO2v66vv0V0aWaIiIiIiIiIiIiFVBxJiIiIiIiIiIiUgEVZxfpv0uijI5Qp+nrbzztA+NpHxhP+8B42gdSVeryv7W6vO2g7df2193tr8vbDtr+ur79FdE9zkRERERERERERCqgM85EREREREREREQq4GB0gOqoR7cuPPXoI5hMJiKjovlu7o/l5js6OvL6888S1Lo1ubm5vPLOeFJSUw1KWzudbx/c/u9/EREegtlsITsnh7GTPuR4WppBaWun8+2DPw3s25vxr7/KPY8+zoFDh6s4Ze12IftgcP++jBxxJ1YrHD5yhNfHvWdA0trrfPvAz9eH18Y8i6trA+xN9kz9ajqbtm4zKG3t8/KzT9G7e3eyc3K444GHK1zm6VGP0PO6bhQXF/P2hPc5GBtbxSmlNqjLx36+Pt68/vxzeDbywGqF/y5ewo/zF5RbpnPHa5jw1uskpxwHYPX6DUyfNduIuJfF/FkzKSgqxGK2YDabKxxNrrb+rGnh7887r7xoe96sSWO+mPkdP/zyX9u02rb/K/q/xa2hK++88hJN/PxISU3l5bfHkZeff9a64UOCufeO2wGY8f0clixbXqXZK6uibX/swZH06dGd0tJSkpKTeWfiB+QXFJy17oV8n1R3FW3/yBF3EhEeSk7OCQA+m/5NhcdyF/q7UXVW0fa/88qLtPD3B6Chqyt5+fmMeHjUWevWhv1fGSrO/ofJZOLZx0cx+vmXSEvPYMbUKazbuJn4xETbMhFhIeTm5XPz3fcRPKA/ox64j1feGW9g6trlQvbBwdhY7nl0McXFxfxr2PU89uD92geX0IXsAwAXZ2duuelG9u7fb1DS2utC9kHzZk0ZcfutPPjEM+Tl59PIw93AxLXPheyDe++4nRVr1vLLwsW0bNGCD8e9zU133m1g6tplccwyfv7vQl57/tkK5/e8rhvNmzXl5rvvo13bNox54jHuf/zJqg0pNV5dP/Yzmy1MmfYlB2NjcXF25pvPPmbrjp1n/Z+/a89enn3ldYNSXn6jnnmeE7m5Fc6rzT9rEpOSbL8km0wmFs6dxZr1G89arjbt/4r+bxlx261s27mL7+b+yF233cKI225h6lfTy63n1tCV+0fcwb2PPo7VCt989jHrNm2usGCrrira9q07fuWzr6ZjtlgYNfI+7r791rO2/U9/931SE5zruGLuvPnM/mneOde70N+NqruKtv/M/8tGP/RAhaXpn2r6/q8MXar5P64OCiIpOYXklOOUlpaybPUa+vXuWW6Zvr16smRp2V8XVq1dR9dO1xqQtPa6kH3w62+7KS4uBmDv/gP4ensbEbXWupB9APDgPSP47oefOHWqxICUtduF7IPh4WHMW7DIdsCWffovZXJpXMg+sFqhgYsLAK4NGpCemWlE1Fpr15695OblnXN+v149WbJsBQD79h/A1dUVL0/PqoontURdP/bLzMqynT1VWFREfOJRfL29DE5VvdSVnzVdO13LseSUWn8VR0X/t5z5Pb5k6XL69e511nrdu3Zl646d5Oblk5efz9YdO+nRrWuVZL5UKtr2rTt+xWyxAKd/r/Kpvb9Xne+44lwu9Hej6u582z+4fz+WrVpddYFqEBVn/8PH24u0tHTb87T0DHy8yh88+Hh5kZpetozZYiG/oAB3N7cqzVmbXcg+ONOw0BA2bdteFdHqjAvZB0GtWuHn68PGLVurOl6dcCH7oLl/M1r4N+OLye/z1ccf0qNbl6qOWatdyD746ttZhAQPInLOd3ww7i3e/+TTqo5Zp/l4e5GWfuY+SsdHv/DLRdKx31+a+PlxVasr2Xvg4FnzOlzdlu8+/5QPx71NYECAAekuH6vVypT3xvHNpx8z/Pqws+bXlZ81Qwb2Z+k5fmmuzfsfwLORB5lZWUBZmezZyOOsZc7+d5BR6/4dDAsdyqatFf9edb7vk5rs5uERzPriM15+9ikaurqeNf9ifz+tia7t0J6s7GyOHkuucH5t3v8XQpdqSo0WOngQbYNa88jTY4yOUqfY2dnxxCMP8vaE942OUqfZ29vj36wpjzwzBl8fb6Z9MIk7Hnj4b0+xlktr6MABLIlZxuyff6F927a88cJz/N/Ih7FaNWC1iNQszvXrM/71V5j86ecUFhaWm3fgcCw3/t8Iik6epOd13Zjw5mvcfM/9BiW99B568hnSMzNp5OHOlPfGk5B4lF179hodq0o5ODjQt2cPPvtqxlnzavv+r0hd/H/8nv+7jVKzmegVKyucX1u/T36JXMT0WbOxWq08dM8IRj/8AGMnfWh0rCo3dNCAvz3brLbu/wulM87+R3pGJr6+Prbnvj7eZ116k56ZiZ9P2TL2JhOuDRrU2Wt9L4cL2QcA3Tp34p7/u43nXn2DkhJdKngpnW8fuLg4c0XLAD59fwLzZ82kXds2THzrDdpc1dqAtLXThXwfpKVnsG7TZsxmMynHU0lMSqK5f7OqjlprXcg+GBYWwvI1awHYu38/9erVw8O99p2FUl2lZ2Ti63PmPvIhPUOXy8rF0bFf2R9ixr/xKjErVrF6/Yaz5hcWFlJ08iQAm7Zuw8HBoVadcffn/s7OOcGaDRu5uk1Q+fl14GdNz+u6cvBwLFk5OWfNq+37HyArO8d2+a2Xp2eFt784+9+Bd635d3D90CH07tGd18dPOOcy5/s+qamycnKwWCxYrVYWLInm6qCzt+tCfz+tqexNJgb06c2y1WvPuUxt3f8XSsXZ/9h/8CDNmzWlSWM/HBwcGDKgP+s2bi63zLqNmwkfGgzAwH592b7rNyOi1loXsg+uanUlzz/5OM+99obu63QZnG8fFBQUEvrvW7npzru56c672bf/AM+99oZG1byELuT7YO3GjXTueA0A7m5utPD351hKihFxa6UL2QepaWl069QJgJYtmlPPsZ5+JlWhdZs2Ez5kMADt2rYhv6DAdqmNyIXSsV/ZSGvxCYnMmfdLhfM9GzWyPb466CrsTHa1pjisX98JF2dn2+PrunTmSHx8uWXqws+aoQMHnPMyzdq8//+0btNf3+PhQ4NZt3HTWcts2b6d7l0609DVlYaurnTv0pkt22v+7WJ6dOvCnbf+h+defcN2D+n/dSHfJzXVmfcr7N+nV4XbdSH/T9Rk3bp0Ij7xKOkZGRXOr837/0LZ2fkG1b3zUM+j53XdeOrRhzCZTCyKXso3s+fywN13ceDQYdZt2kw9R0def2EMV7W6kty8PF4dO942PLNcGufbBx9PGM+VgS3JyCw7aElNS+e5194wNnQtc759cKZP35/AlM+/VHF2iV3IPnji4Qfp0a0LZouFb76fy/LVawxOXbucbx+0bNGCl55+AmdnZ6xWK598+TVbd/xqdOxa462XXqBzx2vwcHcjKzubL2fOwsHBHoD5i5YA8Ozjo+jRrQsni4t5Z+IH+jkk/0hdPvbr2L4dn09+n9gjcVhO3yD8s+nf0Pj02RXzFy3hP8OH8a9hN2A2myk+VcxHn33Bnt9rx4jaTZs05r03XgPKzrxbunIV38yey003hAN142dN/fpOLJj9Hf+66x4KCsou0z1z+2vb/q/o/5Y1Gzcy9pWXaOzry/G0NF5+eyy5efm0uao1/7rhesZ9MBmAG0KHcvfttwHwzew5LI5ZZuCWXLyKtn3E7bdSz9HRVobu3X+ACR99jLeXJy89/SRPv/zaOb9PapqKtr9zx2to3eoKsELK8VTenTyFzKysctsPFf8/UdNUtP0Lo2N49bln2Lt/v+3nHVAr939lqDgTERERERERERGpgC7VFBERERERERERqYCKMxERERERERERkQqoOBMREREREREREamAijMREREREREREZEKqDgTERERERERERGpgIozERERERERERGRCqg4ExERERERERERqYCKMxERERERERERkQqoOBMREREREREREamAijMREREREREREZEKqDgTkWrl1eee4aF77zY6hoiIiIiIiIiKMxGRP336/gSWzv8JR0dHo6OIiIiI1EnzZ82kW+dOhr+GiMifVJyJiABN/Pzo2L4dViv07dnD6DgiIiIicpHsTfr1VkQuPQejA4hI3XZVqyt5+Zmn8G/WlE1bt2G1Wm3zene/jofuvZsmjf2IS0hkwuSPiY2LA8r+kvhz5ELCggfTrEkTlq1ew7SvZ/DqmGe4pn07fj9wkJfeGktefj5QVoY9cv+9+Hh7cfiPI0z46GPiE4/a3itsyGD27T/AvgMHCR8azMq162zzfH28efrRR+jYoR0mk4mlK1fz/iefAjA8PJTb//0vfHy8SUtP543xEzkYG8tdt97MzTcNp4GLCxmZWUyc8gnbd+6qgq+oiIiISPVw1223MDw8lEYeHqSlpzNt+kzWbNgIVHwMddu/b8LP14eJb7+BxWJh+nffM+vHn//2OG7+rJn8snARIYMH0cK/GQNvuPGceRwdHRk18j4G9+8HwIo1a5n61XRKSkpwd3Pj1THP0LF9OywWK3EJCTzy9HNYrdZzHtfZ2dlx5603Mzw8lIaurmzbuYsJk6eQm5dPPUdHXnrmKXpe1xWTycTRY8k8+/JrZOXkXO4vu4hcYirORMQwDg4OvPfma/zwy3/56b+R9OvVk7dffoHvfviprFB79mmee/V19h86TOjgQUx8+w1uuXckJSUlAAzs25vRY17E3t6emdOmEtTqSsa+/yHxCYl8MO5tbrlpOF9/9z3NmzXjrZde4PnX32THb7u5/d83MfHtN7n9/gcpLS0FIGxIMHPn/cLe/Qf4+uPJeHp4kJWTg8lk4v133mL7rl28cecELGYLbYOuAmBQv77cP+JOnn/9LfYfPIR/0yaUlppp4e/Pf4ZHcN+o0WRkZtHEzw+T/gIqIiIidcyx5BQefupZMrOyGdy/L2+8MIb/3H0fHdu3q/AY6s33JnJth/aM+2Ay237dCXBBx3FDBg7g6Zdf5cSJXMwWyznz3PN/t9H+6jaMePhRrFaY8Nbr3HvH7Xzxzbf8383/Ji09g9B/3wpA+7ZtsFqtf3tcd/ONEfTv3ZNHnh5DzokTPD3qEZ59/DFeG/cu4UOH4NrAhYjb76KkpITWV17ByVOnLu8XXEQuC/0mJyKGad+2DQ72DsydNx+z2cyqdev5/eAhAG68Poz/Ll7CvgMHsVgsLFm2nFMlJbRv28a2/k/zI8nKySE9M5Pf9u5l34EDHIr9g1MlJazZsJGrWl0JQPCA/mzYupWtv+7EbDbz/U/zcHKqxzXtrgagY/t2NPHzZfnqtRw8HEtScgpDBw8E4OqgILy9PPnk8684ebKYUyUl/LZ3HwAR4aHM+uEn9p/OnJScwvG0NCwWM46OjgQGBGBvb09KairHUlKq7OsqIiIiUh2sXLuOjMwsrFYry1ev5eixY1zd5qpzHkNV5HzHcQA//ncBaekZFJ+nmAoZPJCvv5tNds4Jck6c4OvvvicseDAApaWleHt50sTPF7PZbDve+7vjun/dcD3Tps8kPSODkpISvvp2FoP69cHeZKLUXIqbmxvNmzbFYrFw8HAshYWFlf6aikjV0xlnImIYby8v0jMzy007nlp20NTY15fwIcHcfGOEbZ6jgwPeXl6252ee6l5cfIqs7PLPXZydAfDx9rS9LoDVaiUtPR2f068VPjSYLTt+5URuLgBLV64ifEgwc+fNx8/Xm+OpaRX+9dLPx5tjyWcXYknJKUz+bBojR9xJYEALtmzfwUfTviAjM+tCvzQiIiIiNV7YkMHc/u9/0aSxHwDOzs54uLmf8xiqIuc7jgNIS0u/oNfy9vIq91rHU1Px9vIE4Psff2bkiDv56L1xAPx3cRTfzf3xb4/rGvv58t4br2I541YjZosFz0aNiFq2Aj8fH95+5QVcG7gSs2Iln03/BrPZfEFZRaT6UHEmIobJzMoqd9AD0NjXh2MpKaSmZ/DN7Ll8M3tupd8nPSOLK69oWW6ar48P6ZmZONWrx+D+/TCZTCz+cTZQdv8Lt4YNaXVFIKlpGfj5+mJvMp1VnqWmZ9CsaZMK33PpytUsXbkaFxcXXnhyNKNG3s+b702s9LaIiIiI1ASNfX158akneHzMi+z5fT8Wi4Vvp03Fzs7ub4+hzrzfLfz9cZxtnQvMlJGZSWM/X+ISEgDw8/W1/WGzsKiIKZ9/yZTPv+SKlgF8MvE99h88xPadu855XJeansHYSR+we9/vFb7f1999z9fffU8TPz8+GPcWCUeTWBgdc4FpRaS60KWaImKYPb/vx2wxc8tNw7G3t2dAn95c3SYIgAVLorjphutpd/p5/fpO9Op+ne0ssouxYs1ael93HV07XYu9vT3/d/O/KSkpYfe+3+nXuxdms4Xb73+Qux4axV0PjeK2+x5k5+49hA8J5veDB8nMyuLRkfdRv74T9RwdbZcGRC6J5o6b/0NQ61YA+DdtQmNfX1r4+9Pl2o44Ojpy6tQpik8VY7Ge+34bIiIiIrVN/fr1sQLZOScAuD5kCFcEtgTOfQwFkJWTTdMmjW2v83fHcX/Hwd6eeo6Otg97k4llq9Zw7x234+HujrubG/ffdQfRK1YCZYNS+Z8u8/ILCrBYLFgslr89rpu/aDEP33ePLbuHuzt9e5WNzt654zVcGdgSk8lEQWEBpaVmHQ+K1FA640xEDFNaWsoLb7zNi08/wUP33s2mrdtYvX4DAAcOHWb8h5N55vFRNG/WlOLiU/y2dx+7du+56PdJTErijXcn8Mxjj+Dj7c2h2D949pU3KC0tJXxoMItjlpL6P6f4/7wgkqdHPcLUL7/m2Vdf5+lRj7Bg9ndYrVaWrlzN7n2/s3LtOtzdGvLWSy/g4+1FyvFU3nxvIhazhUdH3kfLFs0pLTWz5/ffeffDjy7J10xERESkJohPTGTOT/P4csqHWK0WopatYPfp+4ad6xjqeFoaM+f8wDOPPcpjD9zPjO/nMPuneec8jvs7H45/p9zzGd/PYcas2TRwcWHWF5/ZcsyYVXbFQXP/Zjz7+Cg83N3Jy89jXuQifv1tN60CA895XPfDL//FDjs+em8c3l6eZOfksHz1WtZt3IyXpyfPPzkaX29vik4WsXz1WqKXrbjUX2YRqQJ2dr5BF3pmq4iIiIiIiIiISJ2hSzVFREREREREREQqoOJMRERERERERESkAirOREREREREREREKqDiTEREREREREREpAIqzkRERERERERERCrgYHSASyl63g+kHE81OoaIiIjUcE0a+xH671uNjiHnoWM/ERERuRT+7tivVhVnKcdTuXfUaKNjiIiISA03Y+oUoyPIBdCxn4iIiFwKf3fsp0s1RUREREREREREKnBRZ5y9/OxT9O7eneycHO544OEKl3l61CP0vK4bxcXFvD3hfQ7GxgIQPiSYe++4HYAZ389hybLlAAS1bsWrY57BqZ4Tm7Zu44OpnwHg1tCVd155iSZ+fqSkpvLy2+PIy8//xxsqIiIiIiIiIiJyMS7qjLPFMct46sVXzjm/53XdaN6sKTfffR/jP/yIMU88BpSVYPePuIP7H3+C+x57gvtH3EFDV1cAxjzxOOM/+Iib776P5s2a0rNbVwBG3HYr23bu4uZ77mfbzl2MuO2Wf7qNIiIiIiIiIiIiF+2iirNde/aSm5d3zvn9evVkybIVAOzbfwBXV1e8PD3p3rUrW3fsJDcvn7z8fLbu2EmPbl3x8vSkgYsL+/YfAGDJshX0690LgL69erJkadlZaUuWLrdNFxERERERERERqQqX9B5nPt5epKWn256npafj4+1VwfQM2/T0jIyzlgfwbORBZlYWAJlZWXg28riUUUVERERERERERP5WjRlV02q1Vjh9+PVh3BgeBoCHh3tVRhIRERERERERkVrskhZn6RmZ+Pr42J77+viQnpFJekYmnTtec8Z0b379bTfpGZn4eHuftTxAVnYOXp6eZGZl4eXpSXbOiQrfc8HiKBYsjgI0dLyIiEht5ujoSL9ePYgICyVq2QqiV6w0OpKISJ3l4OBAYEALglq3IqjVlbTw9+fwH0fYuHUbu/f9TmlpqdERRUQuiUtanK3btJmbhw9j2arVtGvbhvyCAjKzstiyfTuP3HePbUCA7l0689nX08nNy6egsJB2bduwb/8BwocM5sf/RtpeK3xoMN/N/ZHwocGs27jpUkYVERGRGiIwIICI8BDCggfj4e5OSmoqdiY7o2OJiNQZ9es70SrwCltJFtS6FVe0DMDR0RGAgsJCjiWncOu/buTOW2+moKCAbTt3sWnbdjZt3UZaesZ53kFEpPq6qOLsrZdeoHPHa/BwdyNyznd8OXMWDg72AMxftISNW7bS67pu/PztdE4WF/POxA8AyM3LZ/r3s5l++oywr2d9T25ePgATp3zCq889g5NTPTZtLfvBCvDt3B8Y+8pLRISGcDwtjZffHnvJNlpERESqN+f69Qke0J+I8FA6XN2WkpIS1mzcxMKoGLb9uhOLxWJ0RKnl7OzsCB8aTG5eHrm5eZzIzeVEbi65uXmY9e9ParGGrq5c1epKW0F21emzyezty37vyzlxgoOHY5k7bz4HY2M5ePgPkpKTsVqtuDg707XTtfS8rhs9r+vKgD69AfgjLt5Wov22d5/ORhORGsXOzjeo4puH1UAzpk7h3lGjjY4hIiIi/1C7NkFEhIcSPKA/DVxciEtIIHJJDFHLV5BzouLbNlwOOqaoGS7nfmro6sqy//5c4by8/PzTRVoeuac//1ms/fn4f6cXnTx5WXKKVIaXp6ftLLKysqwVTZs0ts1PTUvnYGwsh2L/4ODhWA7Gxl7U2WMtW7Sg13Vd6XldN67t0B5HR0cKCgvZvnNX2UkT27aRmpZ+/hcSEbnM/u6YosYMDiAiIiK1k5tbQ0IHDyIiPJRWgYEUFZ1k+Zo1RC6JZs/v+42OJ3VUQWEhN915N+5ubqc/GpZ77Hb6sYe7OwHNm+Pu1pAGDRqc8/WKT50qO3MtL7fcGWwncvPIzMoiKTmFo0nHSD5+HLPZXIVbWv05ODjQyMMDTw8PPBt54O7mxsni4rKC8owzAk+VlBgdtVpr2qRx2VlkrcrOIgtq3QovT0/b/MSkJH4/eJD5ixZzMPYPDsX+Uek/WMQnJhKfmMjsn3/BuX7902ejdaVnt270790LgCPxCWzauo1N27bz2959lGg/ikg1o+JMREREqpydnR1dO11LRFgI/Xv3ol69euzbf4DxH0xm2eq1FBYWGh1R6jiLxULK8VRSjqde8DoODg64NWxYYdHm7uZ2umwre9yyRfOyaQ0b4uDw1yF5qdnM8dRUjh5LJulYMkePHbM9rk2lmouzM56NPMoKsUaNTn8+87m7bbq7W8MLes2TJ0+WnQWYl/dXqZaXV37an2cE5uXbplX3ywYdHBxwcXGmgbMLzs71cXF2wcXFGRfn0x8uzjg7O9PA2RkXFxdcnMuel61T9tjP1we3hmVfx9LSUuISEtm8bYftLLLDR+Iu+8/dopMnWbdpM+s2bQagZYvm9OxWdknnzTdGcMct/6GwqIgdO39j07ZtbNq6nZTUC//+ExG5XFSciYiISJXx8fbmhpAhDAsNoWmTxpzIzWP+oiUsjIohNi7O6HgilVJaWkpWdjZZ2dkXtV4jD3f8mzalebNm+DdrSvNmTfFv2pQOV7fF9Yyz2ErNZlKOp5KUnEzS6UKtOpRqdnZ2tuKmYUPXs8ovzwpKsfr161f4Widy88jOziYrJ4fYuDiys3PIys4hOyeHrOxssnNyyDmRi5NTPVvxWFZKNsS9YUPc3BraprVs0aLs7MCGDW03sa9IYVFR+bPXTn8uKiqybd+Z2/rXkzMfVrzMn4/tyo1n8tcTk8mO+k71/yrCXMrKsQanyzHn+vWpV6/e3335bUrNZgoLCykqOklhUSGFhUUUFhWRfeIEe37//XRJ9gdH4uKrxdl58YlHiU88ypx5ZWejdbm2o+1stL69epQtk5Bouzfazj17dTaaiBhCxZmIiIhcVvb29vTufh0R4aH07NYVe3t7tv26k8+mf8Oa9RuqxS9wIkbKzjlBds6JCi9NPrNUa+7fDP+mTWjerBnXXN223KWh5y7VjpF8PPWsUs3R0dF2ttKfJU0Dlz/PZPrrcwOXsjKnQbnpf53Z1KBB2edzKS0tJSvnz+Irh4SkpLOKsKzsHLKzc8g+ceKynf3l4uyM2+liraxwcy1fvDX8s3BrSKuWLXFzc8O5fn2slN0O2mo947bQZzw+82bRZy5T/vHZS585v+jkSVvJVVRURGZmFgVF/1OAFRZRePL059PzCgoLKSwqm1ZUVETxqVOX5GtlhKKTJ1m/eQvrN28BIKC5Pz26ld0b7V8RN3D7f/5FUdFJduzaxbzIRWzatt3gxCJSl6g4ExERkcuiebOmDAsL4fqhQ/Dy9CQ9I5Pv5v7IwuilHEtJMTqeSI1wvlKtebNmZcXaeUq11LQ07LCzFV1/dwbWmU4WF9vKmsLCIgoKC8nKySEpOdlW2hQUFlJYWEhBUREFBQVkZZeVYlk5OeTl5ZcvnQxSWFRWTB1PSzM6ilyAhKNJJBxN4odf/kv9+k507tiRnt26Mrh/PwKaN+fme+43OqKI1CEqzkREROSScXJyYmDfPkSEhdC54zWUms1s2LyFyCXRbN62HbPFYnREkVrjz1Jt977fz5r3v6Va0yaNMZvN5UqwwsIiCopOl16FReXnFZVNqy33VJOa6+TJYjZu2crGLVspKCjgzltvxt7eXv82RaTKqDgTERGRSruq1ZVEhIUSMnggDV1dOXosmalfTWfJ0uVkZmUZHU+kzvm7Uk2kpjqanIyDgwONfX115rKIVBkVZyIiIvKPuDZowNBBA4gIC6XNVa0pPnWKVWvXExkVzc7de6rF5VkiIlJ7JB1LBsC/WVMVZyJSZVSciYiIyEXp2L4dEeGhDO7Xl/r163Mo9g8mfTyVmBWryMvPNzqeiIjUUknJZWWZf9OmbGGHwWlEpK5QcSYiIiLn5enhQfjQYIaFhRLQ3J+CggKWLFvOgiXRHDwca3Q8ERGpAzKzsigqOol/syZGRxGROkTFmYiIiFTIZDLRvWsXhoeF0qdndxwcHNi1Zy8zZ89l5bp1nDxZbHREqYZ6dOvCU48+gslkIjIqmu/m/lhu/k03hPPv4cOwmC0UnTzJ+A8+Ij4xkSZ+fsyZ/gWJR5MA2Lv/ABM++tiITRCRaiwpOZnmTZsaHUNE6hAVZyIiIlJOEz8/bggdyg0hQ/Hz9SErO4e5v/yXhVHRJJwuNUQqYjKZePbxUYx+/iXS0jOYMXUK6zZuJj4x0bZMzMrVzF+0BIC+PXvwxCMP8tSLrwBwLDmFEQ+PMiS7iNQMScnJBLZoYXQMEalDVJyJiIgIjo6O9OvVg4iwULp17gTAlu07mPzZNNZt2kJpaanBCaUmuDooiKTkFJJTjgOwbPUa+vXuWa44KywstD2uX78+aBAJEbkISceS6d39OkwmExaLxeg4IlIHqDgTERGpwwIDAogIDyEseDAe7u6kpKby1bezWLx0Galp6UbHkxrGx9uLtDP+3aSlZ9CuTdBZy/07Yhi3/+cmHB0ceey5523TmzZuzMxpn1BQUMjnM2by2959Z607/PowbgwPA8DDw/0ybIWIVGdJycnUq1cPX29vjqelGR1HROoAFWciIiJ1jHP9+gQP6E9EeCgdrm5LSUkJazZuInJJNNt37tJf8OWymxe5kHmRCxk6aAD33HE7b094n4ysLIbfcRe5uXkEtW7FhDdf5/aRD5U7Qw1gweIoFiyOAmDG1ClGxBcRAx09lgyAf7OmKs5EpEqoOBMREakj2rUJIiI8lOAB/Wng4kJcQgIfffYFUctXkHPihNHxpBZIz/h/9u48Lqp68f/4ixk2BQEVUBY1y8Sl3FBx35EZTLpbt9vta9Ytr6XZzbLstljddrVMTVusrCwzb5uoLKK55oKiphaa5gqDsskq6wy/PzRu/KTCQg/L+/l49HjAmXNm3h9O4PDmc84nC39/v8rP/f18ycjK+tn9E9Zv5OF/TeEZXqasrIyysjIADh0+QmpaGm2Dgzj4/eHLnltE6o/UtDQAggID2LVnr7FhRKRRUHEmIiLSgHl5NcMycgRRkRY6tG9PUVExazduJDomjv3fJRsdTxqY5EOHaBMUSEDrVmRkZhE+bCgznn+pyj5tggIrZ4wMDOvLqZRUAHy8vcnLz8fhcBAY0JrgoEBsF35BFhH5UXpGJiWlpVpZU0SuGBVnIiIiDYyTkxO9e/YgyhrB0IEDcHV15dvkg7zwyqskbNh00aVvIrXF7nAwe/5C5r74HCaTiVVxazh24gQTxo/j4PeH2bxtO3+5MYo+vXpSXl5OfkEB/5n5MgA9u13HhPG3UV5eTkVFBTNfnU9efoHBIxKRuqaiogJbWhrBKs5E5Aq5pOKsX59Qpk66B5PJRHRsHEuWLa/yeGt/fx6bNpXmPj7k5efz5AszycjMBGDyXf9gQFhfABZ/tJS1GzYBENqjO/dNnICzszMHDx/m+dlzsDscNPP05LFpUwkODKSktJTnZr/C0eMnamPMIiIiDZKfry83RIQz1hJBYEBrcvPy+WJVDCtj4zly7JjR8aSR2Ja4k22JO6tsW/T+ksqP5yx8o9rj1m/+mvWbv76s2USkYUhJTSM4SMWZiFwZNS7OTCYT06ZM5r7pj5KekcniBfPYvHV7leXFp0ycQGzCOmIS1hLaozuT7ryDp1+axYCwvoRc24HbJk7CxdWFhS/PYmviLoqKipjx8DTufegRTqWmMmH8OCJHh7MyLp7xf/8bh384yiNPPUO7NsFMmzKZKQ//+7J8EUREROors9nMwLC+REVa6N+nN2azmZ279/D6u++xccvXlF64Z5SIiEhDkWKz0adXD6NjiEgjYarpjl1CQkixpWFLO015eTkJGzYyZGD/Kvu0b9eWXXv3ApC09xuGDOhXuX3PvgPYHQ6Ki0s4cvQY/fuE4u3lRVl5GadSz9/bIjFpN8MHD/zfc1242eOJUykEtG5FCx+f3zlcERGRhqFNUCCT7rqD6I+XMPM/TxLSoQNLli3nz+PuYMrD/yZh/QaVZiIi0iCl2Gy4u7vj27KF0VFEpBGo8YwzP9+WpKdnVH6enpFJ104hVfY5fPQowwYNZPkXKxg2aCAeHh54eTXj8A9HuWvcrSz99DPc3dwI7dGN4ydOkJObi9lsplPHazn4/WFGDBlcuRLT4R+OMmzwQL458C1dQjrSulUr/Px8yc7JqZ2Ri4iI1DNubm4MHzyIKGsEvbp3o9xu5+vtO4iOiWP7zl3YHQ6jI4qIiFx2Py4wEhwYSGZWtsFpRKShq9XFAea/uYhp905mTEQ4e/cdID0jA4fdQWLSbrqEdGTR3FfIyc3lwHfJlW/un3j2Re6/ZyIuLi4kJu3GYT+//YNly3lg0t188MYCfjh2nO+P/ICjml8Ibhxj5Q+RVgB8fLxrczgiIiJ1QscO1xBltRAxcjjNPD05lWpjwdvvErNmLVnZ+oVBREQalxTbheIsKJC9+w8YnEZEGroaF2cZmVmVs8EA/P18ycjKqrJPZlY2jzz9DABN3N0ZPnggBYWFALy3dBnvLV0GwNOPTufkhaXHDyQnc/fUaQD0De1Fm+AgAM6dO8ezs1+pfO4vPnyf1LTTF+VasTqWFatjAVi8YF5NhyMiIlKneXp4EDFyOGMtEXTqeC0lpaWs37SF6Ng49uzbT0VFhdERRUREDHHmTDrl5eVaWVNErogaF2fJhw7RJiiQgNatyMjMInzYUGY8/1KVfby9vMjLz6eiooLxt9zMyrg1wPmFBTw9PcjLy6dD+/Z0aN+exF1JADT38eZsTi4uLi6Mu/mmynLN08OD4pISysvLuTHSwp79+zl37lxtjVtERKRO6nH9dURFWhgxZDDubm58f+QHZs9fQPy69eQXFBgdT0RExHB2hwPb6dNaWVNErogaF2d2h4PZ8xcy98XnMJlMrIpbw7ETJ5gwfhwHvz/M5m3b6dW9G5PuvIMKKti77wCz5i84/yJmM2/OmQ1A4blzPPXizMpLNW/9600MCuuLk8nE5ytXkbT3GwCuatuWGdMfpKICjh0/wXMvz6ntsYuIiNQJLXx8iBw9irFWC+3aBFNQWEjMmgRWxMRx6PARo+OJiIjUOSmpNtpoxpmIXAGXdI+zbYk72Za4s8q2Re8vqfx4/eYtrN+85aLjSsvKuOXOidU+52tvvc1rb7190fYDycn89fa7LiWeiIhIvWEymQjrHcqNVguD+ofh7OzM3v0HeH/pMr7avJni4hKjI4qIiNRZqWlpdLuuq9ExRKQRqNXFAUREROSXBbRuxQ0RoxlrGY2/nx/ZZ3NY9tkXrIyL58SpFKPjiYiI1AunUm14enjg4+1NTm6u0XFEpAFTcSYiInKZubi4MHRgf6KsFvqG9sLhcLB95y5eWfAGW7bvoLy83OiIIiIi9UqKLQ2A4MBAFWciclmpOBMREblMrr6qHWOtEVhHjcTH25u0M2d4670PWL0mgTPpGUbHExERqbdSUm0ABAcFciA52eA0ItKQqTgTERGpRU3c3Rk1bChRkRau79KZsrIyNm7dRnRMHLv27MVxYXEcERER+e3SzpzBbrcTHBhgdBQRaeBUnImIiNSCrp07EWWNYNSwoXg0bcqxEyeY+/pbxK5dp0tIREREallZWRln0jNoE6SVNUXk8lJxJiIi8ht5e3lhGTWCKKuFa9pfRVFRMQkbNhIdE6fLRkRERC6zFJuN4EAVZyJyeak4ExERuQROTk707tmDKGsEQwcOwNXVlW+TD/LCK6+SsGET586dMzqiiIhIo5BiszFy6BCjY4hIA6fiTEREpAb8fH25ISKcsZYIAgNak5uXzxerYlgZG8+RY8eMjiciItLopKTa8PbywquZJ3n5BUbHEZEGSsWZiIjIzzCbzQzqF8ZYawT9+/TGbDazc/ceXn/3PTZu+ZrSsjKjI4qIiDRap2znV9YMCgwk79D3BqcRkYZKxZmIiMj/p01QEFHWCCJHj6JlixakZ2bywbLlrIyLx5Z22uh4IiIiAqTa0gAIDgwkWcWZiFwmKs5EREQANzc3hg8eRJQ1gl7du1Fut/P19h1Ex8Sxfecu7A6H0RFFRETkJ1Iv/DErODDA4CQi0pCpOBMRkUatY4driLJaiBg5nGaenpxKtbHg7XeJWbOWrOxso+OJiIjIzygpKSE9I0Mra4rIZaXiTEREGh1PDw8iRg5nrCWCTh2vpbikhPWbtrAyLp7d3+wzOp6IiIjUUIotjeAgFWcicvmoOBMRkUajx/XXERVpYcSQwbi7ufH9kR+YNW8Ba75aT36BVuMSERGpb1JSbQzs19foGCLSgKk4ExGRBq2Fjw+Ro0cx1mqhXZtgCgoLiVmTwIqYOA4dPmJ0PBEREfkdTtlstGzRgqZNmnCuqMjoOCLSAKk4ExGRBsdkMhHWO5QbrRYG9Q/D2dmZvfsP8P7SZXy1eTPFxSVGRxQREZFakJJqAyAoMIDDPxw1OI2INEQqzkREpMEIaN2KGyJGM9YyGn8/P7LP5rDssy9YGRfPiVMpRscTERGRWpZiO1+cBQcGqjgTkcvikoqzfn1CmTrpHkwmE9GxcSxZtrzK4639/Xls2lSa+/iQl5/Pky/MJCMzE4DJd/2DAWHnrz1f/NFS1m7YBEBoj+7cN3ECzs7OHDx8mOdnz8HucODh0ZSnH3mYVv7+mM1mPvrvp6yOT6iNMYuISAPi4uLC0IH9ibJa6BvaC4fDwfadu3hlwRts2b6D8vJyoyOKiIjIZZJqSwPQAgEictnUuDgzmUxMmzKZ+6Y/SnpGJosXzGPz1u0cP3mycp8pEycQm7COmIS1hPbozqQ77+Dpl2YxIKwvIdd24LaJk3BxdWHhy7PYmriLoqIiZjw8jXsfeoRTqalMGD+OyNHhrIyL5y9RYzl24iTTnngKH29vPln8NvHr1usXIBERAeDqq9ox1hqBddRIfLy9STtzhrfe+4DVaxI4k55hdDwRERG5As4VFZGVnU2bQBVnInJ51Lg46xISQootDVvaaQASNmxkyMD+VYqz9u3aMveNNwFI2vsNM5+eUbl9z74D2B0O7MUlHDl6jP59Qknau4+y8jJOpaYCkJi0m/G33MzKuHgqgKZNmwDQpIk7efn52O32Whm0iIjUT03c3Rk1bChRkRau79KZsrIyNm7dRnRMHLv27MXhcBgdUURERK6wVFuaZpyJyGVT4+LMz7cl6T/5C356RiZdO4VU2efw0aMMGzSQ5V+sYNiggXh4eODl1YzDPxzlrnG3svTTz3B3cyO0RzeOnzhBTm4uZrOZTh2v5eD3hxkxZDD+/n4AfPplNLOeeYpVnyyladMmPP7sC1RUVNTSsEVEpD7p2rkTUdYIRg0bikfTphw7cYK5r79F7Np15OTmGh1PRH7i127t8ccbIvnzjWNx2B0UFRfzwitzK/8Qe9stNzPWEoHD4eCVBa+zY1eSEUMQkXrmlM1G7x49jI4hIg1UrS4OMP/NRUy7dzJjIsLZu+8A6RkZOOwOEpN20yWkI4vmvkJObi4HvkvGfmFWwBPPvsj990zExcWFxKTdOOznt4f1DuX7H35g8rTpBAcGMO+lF/i//Qc4d+5clde8cYyVP0RaAfDx8a7N4YiIiIG8vbywjBpBlNXCNe2voqiomIQNG4mOieNAcrLR8USkGjW5tUf8Vxv4YlUMAIP79+Nf9/yTqf9+nKvatiV82FD+ftdEfFu2YP7MF/jr7XdpJqmI/KqU1DTGjA7HzdWVktJSo+OISANT4+IsIzOrcjYYgL+fLxlZWVX2yczK5pGnnwHOX04zfPBACgoLAXhv6TLeW7oMgKcfnc7JlPOXZx5ITubuqdMA6BvaizbBQQDcYBnNBx9/AnD+EtHTp7mqTTDfHfq+ymuuWB3LitWxACxeMK+mwxERkTrIycmJ3j17EGWNYOjAAbi6uvJt8kFeeOVVEjZsuuiPJyJSt9Tk1h4//T52d3eHC1cUDBnYn4QNGykrKyPt9BlSbGl0CQlRUS4iv+rHlTUDAwI4duKEwWlEpKGpcXGWfOgQbYICCWjdiozMLMKHDWXG8y9V2cfby4u8/HwqKiou3KtsDXD+r4+enh7k5eXToX17OrRvT+KFqffNfbw5m5OLi4sL426+qbJcO5OeTp9ePfnmwLe08PGhbZtgUi+8CRMRkYbFz9eXGyLCGWuJIDCgNbl5eXy+cjUr4+L54dhxo+OJSA3V5NYeAH+OGsstf/kjLs4u3PvQ9PPHtmzJt8kHqxzr59vy8ocWkXrvx+IsOEjFmYjUvhoXZ3aHg9nzFzL3xecwmUysilvDsRMnmDB+HAe/P8zmbdvp1b0bk+68gwoq2LvvALPmLzj/ImYzb86ZDUDhuXM89eLMyks1b/3rTQwK64uTycTnK1eRtPcbAN79cClPPPQgHy56HSecWLjoXXLz8mp7/CIiYhCz2cygfmGMtUbQv09vzGYzO3fvYeE7i9n09VZKy8qMjigil8ln0Sv5LHolo0cM4/Zbb+GZmS/X+FjdpkNE/n8pqeeLM62sKSKXwyXd42xb4k62Je6ssm3R+0sqP16/eQvrN2+56LjSsjJuuXNitc/52ltv89pbb1+0PTMrm3898tilxBMRkXqgTVAQUdYIIkePomWLFqRnZvLBsuWsjIuvvLxLROqnmtza46cS1m/k4X9N4RleJiOrmmMzLz5Wt+kQkf9ffkEBuXl5WllTRC6LWl0cQEREpDpubm4MHzyIKGsEvbp3o9xu5+vtO4iOiWP7zl2Vs5BFpH6rya092gQFcurC7JCBYX05deG+t5u3buc/j07n408/x7dlC9oEBfLdoUNXfAwiUj+lpNoI1owzEbkMVJyJiMhl07HDNURZLUSMHE4zT09OpaSyYNE7xCSsIys72+h4IlLLanJrj7/cGEWfXj0pLy8nv6CA/1y4TPPYiROs27iJj995E7vdwex5C7SipojUWIrNxvVduhgdQ0QaIBVnIiJSqzw9PIgYOZyxlgg6dbyW4pIS1m/aQnRsHHv27Tc6nohcZr92a485C9/42WN/ugq7iMilOJVqY9Swobi4uFCm+6SKSC1ScSYiIrWix/XXERVpYcSQwbi7ufH9kR+YNW8Ba75aT35BgdHxREREpAFLsaVhNpsJaNWKkykpRscRkQZExZmIiPxmLXx8iBw9irFWC+3aBFNQWEjMmgRWxMRx6PARo+OJiIhII5FqO3/vxOCgQBVnIlKrVJyJiMglMZlMhPUO5UarhUH9w3B2dmbPvv28t/Rjvtq0hZKSEqMjioiISCOT8mNxFhBgcBIRaWhUnImISI0EtG7FWEsEN0SE4+/nR/bZHJZ99gUr4+I5cUp/2RURERHjnM3JpbCwkOAgrawpIrVLxZmIiPwsFxcXhg7sT5TVQt/QXjgcDrbv3MUrC95gy/YdlJeXGx1RREREBDh/n7PgQM04E5HapeJMREQucvVV7RhrjSAyfBTeXl6knT7DW+99wKr4NaRnZBodT0REROQip2w2Ol5ztdExRKSBUXEmIiIANG3ShFHDhhIVGcF1nTtTVlbGxq+3Eh0Tx849e6moqDA6ooiIiMjPSkm1MWzgAMwmE3aHw+g4ItJAqDgTEWnkunbuxI1WC6OGD6VpkyYcPX6CV19/k7i1X5GTm2t0PBEREZEaSbHZcHZ2plUrf2xpp42OIyINhIozEZFGyNvLC8uoEURZLVzT/iqKiopJ2LCR6Jg4DiQnGx1PRERE5JKlpF5YWTMwUMWZiNQaFWciIo2Ek5MTvXv2IMoawdCBA3B1deXb5IO88MqrJGzYxLlz54yOKCIiIvKbpdjOF2dtggJJTNptcBoRaShUnImINHB+vr7cEDGasZbRBAa0Jjcvj89XrmZlXDw/HDtudDwRERGRWpGZlU1xcTHBgYFGRxGRBkTFmYhIA2Q2mxnUL4yoSAv9eodiNpvZuXsPC99ZzKavt1JaVmZ0RBEREZFal5KWpuJMRGqVijMRkQakTVAQUdYIxkSE06J5c9IzM/lg2XJWxsXrXh8iIiLS4KWk2mjXJtjoGCLSgKg4ExGp59zc3Bg+eBBR1gh6de9Gud3O19t3EB0Tx/adu7Qcu4iIiDQaKbY0+vftg5OTExUVFUbHEZEGQMWZiEg9FdKhA1GREUSMHIGnhwenUlJZsOgdYhLWkZWdbXQ8ERERkSsuJdWGm6srfr4tSc/INDqOiDQAl1Sc9esTytRJ92AymYiOjWPJsuVVHm/t789j06bS3MeHvPx8nnxhJhmZ539YTb7rHwwI6wvA4o+WsnbDJgBCe3TnvokTcHZ25uDhwzw/ew52h4Nb//oXIkYMB87fq+eqtm2w/uVm8vILfvegRUTqK08PDyJGDifKaiHk2g4Ul5SwftMWomPj2LNvv9HxRERERAz148qawYGBKs5EpFbUuDgzmUxMmzKZ+6Y/SnpGJosXzGPz1u0cP3mycp8pEycQm7COmIS1hPbozqQ77+Dpl2YxIKwvIdd24LaJk3BxdWHhy7PYmriLoqIiZjw8jXsfeoRTqalMGD+OyNHhrIyL56Pln/LR8k8BGNQvjL/9+Y8qzUSk0epx/XVERVoYMWQw7m5ufH/kB2bNW8Car9aTX6CfjSIiIiJwfsYZQJugQHZ/s8/gNCLSENS4OOsSEkKKLa3y5tIJGzYyZGD/KsVZ+3ZtmfvGmwAk7f2GmU/PqNy+Z98B7A4H9uISjhw9Rv8+oSTt3UdZeRmnUlMBSEzazfhbbmZlXHyV1w4fMYyE9Rt+10BFROqbFs2bExk+kqhIC22DgykoLCRmTQIrYuI4dPiI0fFERERE6pz0zExKS0u1sqaI1JoaF2d+vi1JT8+o/Dw9I5OunUKq7HP46FGGDRrI8i9WMGzQQDw8PPDyasbhH45y17hbWfrpZ7i7uRHaoxvHT5wgJzcXs9lMp47XcvD7w4wYMhh/f78qz+nm5ka/3r15ef6C3zlUEZG6z2wyEdanN1HWCAb1C8PZ2Zk9+/az+KOP+WrTFkpKSoyOKCIiIlJnORwObGmnCQ5ScSYitaNWFweY/+Yipt07mTER4ezdd4D0jAwcdgeJSbvpEtKRRXNfISc3lwPfJVeu8vbEsy9y/z0TcXFxITFpNw571dXfBvcPY/+33/7sZZo3jrHyh0grAD4+3rU5HBGRKyagdSvGWiK4ISIcfz8/ss+eZdlnXxAdG8/JlBSj44mIiIjUGyk2m2aciUitqXFxlpGZVWU2mL+fLxlZWVX2yczK5pGnnwGgibs7wwcPpKCwEID3li7jvaXLAHj60emcTDl/eeaB5GTunjoNgL6hvWgTHFTlOUcNG8qaX7hMc8XqWFasjgVg8YJ5NR2OiIjhXFxcGDqwP1FWC31De2G329m+K4lXFrzBlu07KC8vNzqiiIiISL1zymYjtEcPo2OISANR4+Is+dAh2gQFEtC6FRmZWYQPG8qM51+qso+3lxd5+flUVFRcuFfZGuD8wgKenh7k5eXToX17OrRvT+KuJACa+3hzNicXFxcXxt18U2W5BuDh0ZSe3brx1Isza2OsIiJ1wtVXtWOsNYLI8FF4e3mRdvoMby7+gNVr1mj1JxEREZHfKdWWRpMm7rRs0YKs7Gyj44hIPVfj4szucDB7/kLmvvgcJpOJVXFrOHbiBBPGj+Pg94fZvG07vbp3Y9Kdd1BBBXv3HWDWhfuSOZvNvDlnNgCF587x1IszKy/VvPWvNzEorC9OJhOfr1xF0t5vKl9z2MCBJCYlUVyse/qISP3WtEkTRg0bSlRkBNd17kxZWRkbv95KdEwcO/fspaKiwuiIIiIiIg1Ciu38yprBgQEqzkTkd7uke5xtS9zJtsSdVbYten9J5cfrN29h/eYtFx1XWlbGLXdOrPY5X3vrbV576+1qH1u9JoHVaxIuJaKISJ3StXMnbrRaGDV8KE2bNOHo8RO8+vqbxCasIzcvz+h4IiIiIg1OSuqF4iwokG8OfGtwGhGp72p1cQARETl/2bpl1AiirBauaX8V54qKWLt+I9Gx8RxITjY6noiIiEiDlnYmnXK7XQsEiEitUHEmIlILnJyc6N2zB1HWCIYOHICrqysHkpN5/uVXWbthI+eKioyOKCIiItIo2O120k6fITgwwOgoItIAqDgTEfkd/Hx9uSFiNGMtowkMaE1uXh6fr1zNyrh4fjh23Oh4IiIiIo1Sis2mGWciUitUnImIXCKz2cygfmFERVro1zsUs9nMzt17WPjOYjZ9vZXSsjKjI4qIiIg0aimpNq7r3MnoGCLSAKg4ExGpoTZBQURZIxgTEU6L5s1Jz8zk/Y8/YVX8Gmxpp42OJyJSJ/TrE8rUSfdgMpmIjo1jybLlVR6/5c9/IioyArvdwdmcHJ6bPYfT6ekAfB2/unK27pn0DB6a8dQVTi8iDUWKzUYzT0+8vby0IJOI/C4qzkREfoGbmxvDBw8iyhpBr+7dKLfb2bJtO9Gx8ezYuQu7w2F0RBGROsNkMjFtymTum/4o6RmZLF4wj81bt3P85MnKfQ4dOcLtk1ZTUlLCn8aO4d5/3snjz74AQElpKbfdPdmo+CLSgPx0ZU0VZyLye6g4ExGpRkiHDkRFRhAxcgSeHh6cSkllwaJ3WL1mLdlnzxodT0SkTuoSEkKKLa1yFm7Cho0MGdi/SnG2+5t9lR8fSD6IZeSIK55TRBq+FNv54qxNYCDfJh80OI2I1GcqzkRELvD08CBi5HCirBZCru1AcUkJ6zdtITo2jj379hsdT0SkzvPzbUl6ekbl5+kZmXTtFPKz+4+1RLBt567Kz11dXVm8YB52h50PPl7Opq3bLmteEWm4bKfP4HA4CA7SAgEi8vuoOBORRq/H9dcRFWlhxJDBuLu5cejIEWbNe434despKCw0Op6ISINkGTmCziHXcs8DD1du++PfbyMjK4vAgNYsmPUSPxw7TmpaWpXjbhxj5Q+RVgB8fLyvaGYRqT/Kyso4k5GhlTVF5HdTcSYijVKL5s2JDB9JVKSFtsHBFBQWsjp+DdEx8Rw6csToeCIi9VJGZhb+/n6Vn/v7+ZKRlXXRfn169eT2v/+Nex58iLKfrET84762tNPs/mYfHTtcc1FxtmJ1LCtWxwKweMG8yzEMEWkgUlJtBAcFGB1DROo5FWci0miYTSbC+vQmyhrBoH5hODs7s2fffhZ/9DFfbdpCSUmJ0RFFROq15EOHaBMUSEDrVmRkZhE+bCgznn+pyj4dO1zD9PunMPXfj3M2J7dyezNPT4pLSigrK8Pby4tuXbuw5JP/XukhiEgDkmJLY/jggUbHEJF6TsWZiDR4Aa1bMdYSwQ0R4fj7+ZF99izLPvuC6Nh4TqakGB1PRKTBsDsczJ6/kLkvPofJZGJV3BqOnTjBhPHjOPj9YTZv286Uf95F0yZNeO6JxwA4k57BQzOe4qq2bZg+9T4qHBU4mZz4YNnyKosKiIhcqhSbDR9vbzw9PHT7DRH5zVSciUiD5OLiwtCB/YmyWugb2gu73c72XUm8suANtmzfQXl5udERRUQapG2JO9mWuLPKtkXvL6n8eMrD/672uP3fJfN/E+65rNlEpHFJST2/smZQYACHDutWHCLy26g4E5EG5eqr2jHWGkFk+Ci8vbxIO32GNxd/wOo1a0jPyDQ6noiIiIhcISm288VZm6BAFWci8pupOBOReq9pkyaMGjaUqMgIruvcmbKyMjZ+vZXomDh27tlLRUWF0RFFRERE5Ar7cXERrawpIr+HijMRqbe6du7EjVYLo4YPpWmTJhw9foJXX3+T2IR15OblGR1PRERERAxUXFxCemamijMR+V1UnIlIveLt5YVl1AiirBauaX8V54qKWLt+I9Gx8RxITjY6noiIiIjUISmpNoKDVJyJyG+n4kxE6jwnJyf69OxBVKSFIQP64+rqyoHkZJ5/+VXWbtjIuaIioyOKiIiISB2UYktjQN8+RscQkXrskoqzfn1CmTrpHkwmE9GxcSxZtrzK4639/Xls2lSa+/iQl5/Pky/MJCPz/M24J9/1DwaE9QVg8UdLWbthEwChPbpz38QJODs7c/DwYZ6fPQe7wwFAr+7duP+eiTg7O5OTm8ukBx/+3QMWkfrDz9eXGyJGM9YymsCA1uTm5fH5ytWsjIvnh2PHjY4nIiIiInVcqs2Gb8sWNHF3p6i42Og4IlIP1bg4M5lMTJsymfumP0p6RiaLF8xj89btHD95snKfKRMnEJuwjpiEtYT26M6kO+/g6ZdmMSCsLyHXduC2iZNwcXVh4cuz2Jq4i6KiImY8PI17H3qEU6mpTBg/jsjR4ayMi8fTw4OH7pvM/f9+nDPpGTT38b4sXwARqVvMZjOD+oURFWmhX+9QzGYziUm7WfjOYjZ9vZXSsjKjI4qIiIhIPfHjyppBgQEcOXrM4DQiUh/VuDjrEhJCii0NW9ppABI2bGTIwP5VirP27doy9403AUja+w0zn55RuX3PvgPYHQ7sxSUcOXqM/n1CSdq7j7LyMk6lpgKQmLSb8bfczMq4eCJGDmfDlq2cSc8A4GxObu2MWETqpLbBwURZI4gcPYoWzZuTnpnJ+x9/wqr4NZU/d0RERERELkVK6vmVNdsEBao4E5HfpMbFmZ9vS9IvlFgA6RmZdO0UUmWfw0ePMmzQQJZ/sYJhgwbi4eGBl1czDv9wlLvG3crSTz/D3c2N0B7dOH7iBDm5uZjNZjp1vJaD3x9mxJDB+Pv7AdAmKAhnZ2cWvjyTpk2a8MkXXxKbsK6Whi0idYGbmxsjhgwiymqhZ7frKbfb2bJtO9Gx8ezYuavysm0RERERkd8iJe38jDOtrCkiv1WtLg4w/81FTLt3MmMiwtm77wDpGRk47A4Sk3bTJaQji+a+Qk5uLge+S678hfiJZ1/k/nsm4uLiQmLSbhz289vPF2oduPehR3BzdePteXM48N3BytlpP7pxjJU/RFoB8NHlnCL1QkiHDkRFRhAxcgSeHh6cTElhwaJ3WL1mLdlnzxodT0REREQaiMLCc2SfzSEoMMDoKCJST9W4OMvIzKqcDQbg7+dLRlZWlX0ys7J55OlnAGji7s7wwQMpKCwE4L2ly3hv6TIAnn50OidTzhdgB5KTuXvqNAD6hvaiTXAQAOmZmeTm5VFcXEJxcQl79h/g2muuvqg4W7E6lhWrYwFYvGBezUcuIleUp4cHESOHE2W1EHJtB4pLSvhq02aiY+LYu/+A0fFEREREpIFKsdk040xEfrMaF2fJhw7RJiiQgNatyMjMInzYUGY8/1KVfby9vMjLz6eiouLCvcrWAOcXFvD09CAvL58O7dvToX17EnclAdDcx5uzObm4uLgw7uabKsu1zVu38eC9kzCbTDi7uNC1UwjLPvu8tsYtIldIz27XM9YawYghg3F3c+PQkSPMmvca8evWVxbrIiIiIiKXS4rNRq/u3YyOISL1VI2LM7vDwez5C5n74nOYTCZWxa3h2IkTTBg/joPfH2bztu306t6NSXfeQQUV7N13gFnzF5x/EbOZN+fMBqDw3DmeenFm5aWat/71JgaF9cXJZOLzlatI2vsNAMdPnmL7riQ+XPQ6DkcF0bFxHD1+orbHLyKXQYvmzRkzehRjrRG0DQ6moLCQ1fFriI6J59CRI0bHExEREZFGJCXVRmT4KNxcXSkpLTU6jojUM5d0j7NtiTvZlrizyrZF7y+p/Hj95i2s37zlouNKy8q45c6J1T7na2+9zWtvvV3tYx8t/5SPln96KRFFxCBmk4mwPr2JskYwqH8/nM1m9uzbz+KPPuarTVsoKSkxOqKIiIiINEIptvMLBAS0bs3xkycNTiMi9U2tLg4gIo1PQOtWjLVEcENEOP5+fmSfPcvH//2MlXFrOJmSYnQ8EREREWnkUlLPF2dtggJVnInIJVNxJiKXzNXFhSEDBxBljaBvaC/sdjvbdyXx8muvs2X7Dux2u9ERRURERESA/8040wIBIvJbqDgTkRq7+qp2RFktWMNH4u3lRdrpM7y5+ANWr1lDekam0fFERERERC6Sl19AXn4+wUEBRkcRkXpIxZmI/KKmTZowathQoiIjuK5zZ0pLS9m0dRvRMXHs3LOXiooKoyOKiIiIiPyiFFuaZpyJyG+i4kxEqtW1cydutFoYNXwoTZs04ejxE8xZ+AZxa78iNy/P6HgiIiIiIjWWkmqja+cQo2OISD2k4kxEKnl7eWENH8lYSwTXtL+Kc0VFrF2/kRWxcXybfNDoeCIiIiIiv0mKzcbIoYNxdnamvLzc6DgiUo+oOBNp5JycnOjTswdRkRaGDOiPq6srB5KTef7lV1m7YSPnioqMjigiIiIi8rukpNowm80EtPLn1IVVNkVEakLFmUgj5efryw0RoxlrGU1gQGty8/L4fOVqVsbF88Ox40bHExERERGpNT9dWVPFmYhcChVnIo2I2WxmUL8woiIt9OsditlsJjFpNwvfeZdNX2+jtKzM6IgiIiIiIrUu5UJZFhwUCDsNDiMi9YqKM5FGoG1wMFHWCCJHj6JF8+akZ2by/sefsCp+Dba000bHExERERG5rLJzcig8d04ra4rIJVNxJtJAubm5MWLIIKKsFnp2u55yu50t27YTHRvPjp27sDscRkcUEREREbliUmy28zPOREQugYozkQYmpEMHoiIjiBg5Ak8PD06mpLBg0TusXrOW7LNnjY4nIiIiImKIlNQ0Olzd3ugYIlLPqDgTaQA8PTyIGDmcKKuFkGs7UFxSwlebNhMdE8fe/QeMjiciIiIiYrjUNBtDBvTDbDLp6gsRqTEVZyL1WM9u1zPWGsGIIYNxd3Pj0JEjzJr3GvHr1lNQWGh0PBERERGROiMlNQ0XFxf8/f1IO33G6DgiUk+oOBOpZ1o0b86Y0aMYa42gbXAwBYWFrI5fQ3RMPIeOHDE6noiIiIhInZRiO7+yZpvAQBVnIlJjKs5E6gGzyURYn95EWSMY1L8fzmYze/btZ/FHH/PVpi2UlJQYHVFERASAfn1CmTrpHkwmE9GxcSxZtrzK47f8+U9ERUZgtzs4m5PDc7PncDo9HYDI8FHccestACz+6GNiEtZe8fwi0nCdSj1fnAUHBZK4e4/BaUSkvlBxJlKHBbRuxVhLBDdEhOPv50f22bN8/N/PWBm3hpMpKUbHExERqcJkMjFtymTum/4o6RmZLF4wj81bt3P85MnKfQ4dOcLtk1ZTUlLCn8aO4d5/3snjz76AVzNP7rztVu6YNIWKCnjv9fls3rad/IICA0ckIg1JZlYWxSUlBAUEGB1FROoRFWcidYyriwtDBg4gyhpB39Be2O12tu9K4uXXXmfL9h3Y7XajI4qIiFSrS0gIKbY0bGmnAUjYsJEhA/tXKc52f7Ov8uMDyQexjBwBQFjv3iQm7SEv/3xRlpi0h359epOwfsOVG4CINGgVFRWk2tIIDgo0OoqI1COXVJz92tT71v7+PDZtKs19fMjLz+fJF2aSkZkJwOS7/sGAsL4ALP5oKWs3bAIgtEd37ps4AWdnZw4ePszzs+dgdzjo1b0bM//zZOUbrw1bvubdD5f+7gGL1FVXX9WOKKsFa/hIvL28sKWd5s3F77MqPqHy+0hERKQu8/NtSXp6RuXn6RmZdO0U8rP7j7VEsG3nrv8dm1H1WD/flpcvrIg0Sik2G8GBKs5EpOZqXJzVZOr9lIkTiE1YR0zCWkJ7dGfSnXfw9EuzGBDWl5BrO3DbxEm4uLqw8OVZbE3cRVFRETMensa9Dz3CqdRUJowfR+TocFbGxQOwd/8Bpj3+ZO2PWqSOaNqkCaOGDSUqMoLrOnemtLSUjV9vJTo2nl179lJRUWF0RBERkcvCMnIEnUOu5Z4HHr6k424cY+UPkVYAfHy8L0c0EWnAUlJthPUOxcnJSe+1RaRGalyc1WTqfft2bZn7xpsAJO39hplPz6jcvmffAewOB/biEo4cPUb/PqEk7d1HWXkZp1JTAUhM2s34W26uLM5EGqqunTtxo9XCqOFDadqkCT8cO86chW8Qt/YrcvPyjI4nIiLym2RkZuHv71f5ub+fLxlZWRft16dXT27/+9+458GHKCsrqzy2V/duVY796WWdP1qxOpYVq2MBWLxgXm0PQUQauBSbDXc3N3xbttRVHSJSI6aa7ljd1Hu/llWnzx8+epRhgwYCMGzQQDw8PPDyasbhH47Sv08obm5ueHt5EdqjG638/MjJzcVsNtOp47UAjBgyuMqbreu7dGbJmwuZ8/wztG/X7ncNVMRo3l5e/O3Pf+SjRW/wzvxXGTV8KGvXb+TOKfdz64S7+eTzL1WaiYhIvZZ86BBtggIJaN0KZ2dnwocNZfPW7VX26djhGqbfP4WHZjzF2Zzcyu07du0iLLQXzTw9aebpSVhoL3bs2nWlhyAiDVyKLQ2ANrrPmYjUUK0uDjD/zUVMu3cyYyLC2bvvAOkZGTjsDhKTdtMlpCOL5r5CTm4uB75Lxu5wAPDEsy9y/z0TcXFxITFpNw77+e0HDx/hD3+/jaLiYvr37cPMp2dw0+13XvSamq4vdZmTkxN9evYgKtLCkAH9cXV1Zf93yTw3ew7rNm7iXFGR0RFFRERqjd3hYPb8hcx98TlMJhOr4tZw7MQJJowfx8HvD7N523am/PMumjZpwnNPPAbAmfQMHprxFHn5Bbz70VLevTCL7J0PP6pcKEBEpLak2GwABAcGVjurVUTk/1fj4qwmU+8zs7J55OlnAGji7s7wwQMpKCwE4L2ly3hv6TIAnn50OidTzl+eeSA5mbunTgOgb2gv2gQHAXDu3LnK592WuBPn++7F28vrohk5mq4vdZGfry83RIxmrGU0gQGtyc3L47OVq1gZG8/R4yeMjiciInLZbEvcybbEnVW2LXp/SeXHUx7+988euypuDavi1ly2bCIi6ekZlJWVERwUYHQUEaknalyc/XTqfUZmFuHDhjLj+Zeq7OPt5UVefj4VFRUX7lV2/o2PyWTC09ODvLx8OrRvT4f27UnclQRAcx9vzubk4uLiwribb6os11o0b0722bMAdAnpiJPJSZexSZ1mNpsZ1C+MqEgL/XqHYjabSUzazcJ33mXj19sq7+EiIiIiIiLGsDsc2E6f0cqaIlJjNS7OajL1vlf3bky68w4qqGDvvgPMmr/g/IuYzbw5ZzYAhefO8dSLMysv1bz1rzcxKKwvTiYTn69cRdLebwAYMWQQfxp7A3a7nZLSEp549oXaHrtIrWgbHEyUNYLI0aNo0bw56RkZvP/xJ6yMiyft9Bmj44mIiIiIyE+k2GwqzkSkxi7pHme/NvV+/eYtrN+85aLjSsvKuOXOidU+52tvvc1rb7190fZPV6zk0xUrLyWeyBXj5ubGiCGDiLJa6NntesrLy9mybQcrYuPYsSsJx4ViWERERERE6paUVBs9u11vdAwRqSdqdXEAkYYupEMHoiIjiBg5Ak8PD06mpPDaW28Tk7Cu8tJiERERERGpu1JsNpo2aUILHx+yc3KMjiMidZyKM5Ff0czTk9EjhhNljSDk2g4Ul5Tw1abNRMfEsXf/AaPjiYiIiIjIJUhJvbCyZlCgijMR+VUqzkR+Rs9u1xNltTB8yCDc3dw4dPgIs+a9Rvy69ZWrxYqIiIiISP2SYrtQnAUGsu/b7wxOIyJ1nYozkZ9o0bw5Y0aPYqw1grbBweQXFLAqbg0rY+M5dOSI0fFEREREROR3SjuTTrndTnCQFggQkV+n4kwaPbPJRFif3kRZIxjUvx/OZjO7v9nHux9+zPrNWygpKTE6ooiIiIiI1JLy8nJOnzmjlTVFpEZUnEmjFRjQmrGWCMZEhOPv60v22bN8/N/PWBm3hpMpKUbHExERERGRyyTFlqYZZyJSIyrOpFFxdXFhyMAB3BhpoU+vntjtdrbvSuLl+QvZsn0Hdrvd6IgiIiIiInKZpdpsdB0x3OgYIlIPqDiTRuGa9lcx1hKBNXwk3l5e2NJO8+bi91kVn0BGZqbR8URERERE5ApKSU2jmacnXl7NyMvLNzqOiNRhKs6kwWrapAmjhg0lKjKC6zp3prS0lI1fbyU6Np5de/ZSUVFhdEQRERERETHAjytrtgkM5Nu8QwanEZG6TMWZNDjXde5MVKSFUcOG0LRJE344dpw5C98gbu1X5OblGR1PREREREQMdir1fHEWHBjItwdVnInIz1NxJg2Ct5cX1vCRRFktXH1VO84VFbF2/UZWxMbxbfJBo+OJiIiIiEgdYktLw+FwaIEAEflVKs6k3nJycqJPzx5ERVoYOnAALi4u7P8umedmz2Hdxk2cKyoyOqKIiIiIiNRBpWVlpGdmEhQYYHQUEanjVJxJvePv58uY0aOJskYQ0LoVuXl5fBq9kpWx8Rw9fsLoeCIiIiIiUg+kpNoIDtSMMxH5ZSrOpF4wm80M7t+PKGsEYb1DMZvNJCbtZsHb77Dx622UlZUZHVFEREREROqRFJuNIQMGGB1DROo4FWdSp7UNDibKGkHk6FG0aN6c9IwM3v/4E1bGxZN2+ozR8UREREREpJ5KSbXRorkPHh5NKSw8Z3QcEamjVJxJnePm5saIIYOIslro2e16ysvL2bJtByti49ixKwmHw2F0RBERERERqedSbGkABAcEcujIEYPTiEhdpeJM6oyQDh2IiowgYuQIPD08OJmSwmtvvU1Mwjqyz541Op6IiIiIiDQgKTYbAMFBASrORORnqTgTQzXz9GT0iOFERUYQ0qEDxSUlfLVpM9Excezdf8DoeCIiIiIi0kCl/jjjTAsEiMgvuKTirF+fUKZOugeTyUR0bBxLli2v8nhrf38emzaV5j4+5OXn8+QLM8nIzARg8l3/YEBYXwAWf7SUtRs2ARDaozv3TZyAs7MzBw8f5vnZc7D/5FK8ziEdWTRvDk88+wLrN2/5XYOVuqNnt+uJsloYPmQQ7m5uHPz+MDPnzmfNVxsoKCw0Op6IiIiIiDRwRcXFZGZlExyk4kxEfl6NizOTycS0KZO5b/qjpGdksnjBPDZv3c7xkycr95kycQKxCeuISVhLaI/uTLrzDp5+aRYDwvoScm0Hbps4CRdXFxa+PIutibsoKipixsPTuPehRziVmsqE8eOIHB3Oyrj4ytecfNc/SNyVVPsjlyuuRfPmjBk9irHWCNoGB5NfUMCquDWsjI3X1GgREREREbniUmw2zTgTkV9U4+KsS0gIKbY0bGmnAUjYsJEhA/tXKc7at2vL3DfeBCBp7zfMfHpG5fY9+w5gdziwF5dw5Ogx+vcJJWnvPsrKyziVmgpAYtJuxt9yc2VxdtMfoli/+Wu6hHSsndHKFWc2mQjr05soawSD+vfD2Wxm9zf7ePfDj1m/eQslJSVGRxQRERERkUYqJdVGWO9Qo2OISB1W4+LMz7cl6ekZlZ+nZ2TStVNIlX0OHz3KsEEDWf7FCoYNGoiHhwdeXs04/MNR7hp3K0s//Qx3NzdCe3Tj+IkT5OTmYjab6dTxWg5+f5gRQwbj7+93/vVatmTowAFMnjadLiEP/GyuG8dY+UOkFQAfH+9LGrxcPoEBrRlriWBMRDj+vr5kZWfz8X8/Izo2vrIoFRERERERMVKKzcYNvqNxd3ejuFh/1BeRi9Xq4gDz31zEtHsnMyYinL37DpCekYHD7iAxaTddQjqyaO4r5OTmcuC75Mr7mD3x7Ivcf89EXFxcSEzajcN+fvv9k+5mwdvvUlFR8YuvuWJ1LCtWxwKweMG82hyOXCJXFxeGDBzAjZEW+vTqid1uZ9vOXcyet4CvdyRit9uNjigiIiIiIlLpx5U1gwIC+OHYcWPDiEidVOPiLCMzq3I2GIC/ny8ZWVlV9snMyuaRp58BoIm7O8MHD6y80ft7S5fx3tJlADz96HROppyfdXQgOZm7p04DoG9oL9oEBwHQueO1PPvYvwHw9vaif98+2O12Nm3d9psGKpfPNe2vYqwlAmv4SLy9vLClnebNxe+zKj6hcnEIERERERGRuuZU6vniLDgwUMWZiFSrxsVZ8qFDtAkKJKB1KzIyswgfNpQZz79UZR9vLy/y8vOpqKi4cK+yNcD5m/x7enqQl5dPh/bt6dC+feUN/5v7eHM2JxcXFxfG3XxTZbn2p3G3Vz7vEw89yJbtO1Sa1SFNmzRh1LChREVGcF3nzpSWlrLx661Ex8aza8/eX50pKCIiIiIiYrRUWxqAVtYUkZ9V4+LM7nAwe/5C5r74HCaTiVVxazh24gQTxo/j4PeH2bxtO726d2PSnXdQQQV79x1g1vwF51/EbObNObMBKDx3jqdenFl5qeatf72JQWF9cTKZ+HzlKpL2fnMZhim15brOnYmKtDBq2BCaNmnCD8eOM2fhG8St/YrcvDyj44mIiIjB+vUJZeqkezCZTETHxrFk2fIqj/e4/jqmTrqba65uzxPPvsD6zVsqH/s6fnXljI8z6Rk8NOOpK5hcRBqjgsJCzubkaGVNEflZl3SPs22JO9mWuLPKtkXvL6n8eP3mLVXe/PyotKyMW+6cWO1zvvbW27z21tu/+LrPzHr5UmJKLfP28sIaPpIoq4Wrr2rHuaIiEtZvIDomjm8PHjI6noiIiNQRJpOJaVMmc9/0R0nPyGTxgnls3rq9yirsZ9IzeGbmy/z9r3++6PiS0lJuu3vylYwsIkKKLY3gwACjY4hIHVWriwNIw+Hk5ESfnj2IirQwdOAAXFxc2P9dMs/NnsO6jZs4V1RkdEQRERGpY7qEhJBiS8OWdhqAhA0bGTKwf5XiLO3MGQAqHLqtg4jUDam2NLpf39XoGCJSR6k4kyr8/XwZM3o0UdYIAlq3Ijcvj0+jV7IyNp6jx08YHU9ERETqMD/flqSnZ1R+np6RSddOITU+3tXVlcUL5mF32Png4+W6v62IXBEpNhujRwzD1cWF0rIyo+OISB2j4kwwm80M7t+PKGsEYb1DMZvN7NiVxIK332Hj19so0z8eIiIicgX88e+3kZGVRWBAaxbMeokfjh0nNS2tyj43jrHyh0grAD4+3kbEFJEGJiXVhslkIjCgNcdPnjI6jojUMSrOGrG2wcFEWSOIHD2KFs2bk56RwXtLl7Eqfg1pp88YHU9ERETqmYzMLPz9/So/9/fzJSMrq+bHX9jXlnaa3d/so2OHay4qzlasjmXF6lgAFi+YVwupRaSxO2WzARAcGKjiTEQuouKskXFzc2PEkEFEWS307HY95eXlbNm2gxWxcezYlYTjwmqnIiIiIpcq+dAh2gQFEtC6FRmZWYQPG8qM51+q0bHNPD0pLimhrKwMby8vunXtwpJP/nuZE4uInJ9xBhAcpJU1ReRiKs4aiZAOHYiKjCBi5Ag8PTw4mZLCa2+9TUzCOrLPnjU6noiIiDQAdoeD2fMXMvfF5zCZTKyKW8OxEyeYMH4cB78/zOZt2+kc0pGXnnqCZp7NGNQ/jAnjx/H3uyZyVds2TJ96HxWOCpxMTnywbHmVRQVERC6X3Lw88gsKCA5UcSYiF1Nx1oA18/Rk9IjhREVGENKhA8UlJXy1aTPRMXHs3X/A6HgiIiLSAG1L3Mm2xJ1Vti16f0nlx8mHvifqlnEXHbf/u2T+b8I9lz2fiEh1UlJtBAUGGB1DROogFWcNUM9u1xNltTB8yCDc3dw4+P1hZs6dz5qvNlBQWGh0PBERERERkTolxWajU8eORscQkTpIxVkD0aJ5c8aMHsVYawRtg4PJLyhgVdwaVsbGc+jIEaPjiYiIiIiI1FkptjSGDxmM2WzGbrcbHUdE6hAVZ/WY2WQirE9voqwRDOrfD2ezmd3f7OPdDz9m/eYtlJSUGB1RRERERESkzktJteFsNhPQyp8UW9qvHyAijYaKs3ooMKA1Yy0RjIkIx9/Xl6zsbD7+72dEx8ZzKjXV6HgiIiIiIiL1SortfytrqjgTkZ9ScVZPuLq4MGTgAG6MtNCnV0/sdjvbdu5i9rwFfL0jUdOJRUREREREfqMfy7LzK2smGRtGROoUFWd13DXtr2KsJQJr+Ei8vbywpZ3mzcXvsyo+gYzMTKPjiYiIiIiI1HtZ2dkUFRVfKM5ERP5HxVkd1LRJE8KHDyXKaqFr506Ulpay8eutRMfGs2vPXioqKoyOKCIiIiIi0qCk2GwEB6k4E5GqVJzVIdd17kxUpIVRw4bQtEkTfjh2nFcWvE7cuq/Iy8s3Op6IiIiIiEiDlWKz0b5dO6NjiEgdo+LMYN5eXljDRxJltXD1Ve04V1REwvoNRMfE8e3BQ0bHExERERERaRRSUm0M6heGyWTC4XAYHUdE6ggVZwZwcnKiT6+eREVaGDqgPy4uLuz/LpnnZs9h3cZNnCsqMjqiiIiIiIhIo5Jis+Hi4kIrPz/SzpwxOo6I1BGXVJz16xPK1En3YDKZiI6NY8my5VUeb+3vz2PTptLcx4e8/HyefGFm5Q3sJ9/1DwaE9QVg8UdLWbthEwChPbpz38QJODs7c/DwYZ6fPQe7w8HgAf2YePt4HA4HdrudV19/k28OfFsbYzaMv58vN0SMZqwlgoDWrcjJzeXTFSuJjo3n2IkTRscTERERERFptE6l2gAICgxQcSYilWpcnJlMJqZNmcx90x8lPSOTxQvmsXnrdo6fPFm5z5SJE4hNWEdMwlpCe3Rn0p138PRLsxgQ1peQaztw28RJuLi6sPDlWWxN3EVRUREzHp7GvQ89wqnUVCaMH0fk6HBWxsWza/deNm/dDkCH9u159olH+ds/JtT+V+Ayc3Z2ZlC/MKKsEfTr0xuTycSOXUm8tuhtNm3dTllZmdERRUREREREGr0U2/niLDgwkF179hobRkTqjBoXZ11CQkixpWFLOw1AwoaNDBnYv0px1r5dW+a+8SYASXu/YebTMyq379l3ALvDgb24hCNHj9G/TyhJe/dRVl7GqdRUABKTdjP+lptZGRdPUXFx5fO6u7tDPVtJsm1wMFHWCCJHj6JF8+akZ2Sw+KOPWRW/hrTT+uuFiIiIiIhIXZKRmUVJaalW1hSRKmpcnPn5tiQ9PaPy8/SMTLp2Cqmyz+GjRxk2aCDLv1jBsEED8fDwwMurGYd/OMpd425l6aef4e7mRmiPbhw/cYKc3FzMZjOdOl7Lwe8PM2LIYPz9/Sqfb+jAAdxz5x009/Hhwcdm1MJwLy83NzdGDhnMWGsEPbtdT3l5OVu27WBFbBw7diXpBpMiIiIiIiJ1VEVFBba0NIIDA4yOIiJ1SK0uDjD/zUVMu3cyYyLC2bvvAOkZGTjsDhKTdtMlpCOL5r5CTm4uB75Lxn6hRHri2Re5/56JuLi4kJi0G4f9f+XSxq+3svHrrfS4/jom3nEbUx7+90WveeMYK3+ItALg4+Ndm8OpsZBrO3BjpIXRI4bj6eHBiVMpzH/rbWLXrCU7J8eQTCIiIiIiInJpUlLTNONMRKqocXGWkZlVZTaYv58vGVlZVfbJzMrmkaefAaCJuzvDBw+koLAQgPeWLuO9pcsAePrR6ZxMOX955oHkZO6eOg2AvqG9aBMcdNFr791/gMCA1nh7eZGbl1flsRWrY1mxOhaAxQvm1XQ4v1szT09GjxhOVGQEIR06UFxczLpNm4mOiav3ixiIiIiIiIg0Rik2G3169cDJyYmKena7IBG5PGpcnCUfOkSboEACWrciIzOL8GFDmfH8S1X28fbyIi8/n4qKigv3KlsDnF9YwNPTg7y8fDq0b0+H9u1J3JUEQHMfb87m5OLi4sK4m2+qLNeCAwNIsaUBENKhAy4uLheVZkbo2e16oqwWhg8ZhLubGwe/P8zMufNZ89WGypJQRERERERE6p8Umw13d3d8W7S4aKKIiDRONS7O7A4Hs+cvZO6Lz2EymVgVt4ZjJ04wYfw4Dn5/mM3bttOrezcm3XkHFVSwd98BZs1fcP5FzGbenDMbgMJz53jqxZmVl2re+tebGBTWFyeTic9XriJp7zcADB88CGv4KMrLyykpLeWJZ1+o7bFfks4hHfnPv6fTJjiI/IICVsWtYWVsPIeOHDE0l4iIiIiIiNSOU6nnV9Zc8PJLFBeXGJxGRH6UuHsPr731tiGvfUn3ONuWuJNtiTurbFv0/pLKj9dv3sL6zVsuOq60rIxb7pxY7XO+9tbb1Q5+ySf/Zckn/72UeJeVLe00p9PTeWfJR6zf8jUlJfohKiIiIiIi0pDs+/ZbVq9JwNPDw+goIvITZw28f3ytLg7QkOXm5VW7OIGIiIiIiIg0DMXFJTwz82WjY4hIHWIyOoCIiIiIiIiIiEhdpOJMRERERERERESkGirOREREREREREREqqHiTEREREREREREpBoqzkRERERERERERKqh4kxERERERERERKQaKs5ERERERERERESq4Wx0gNoU0LoVixfMu6yv4ePjTU5O7mV9Dfl5+vobT+fAeDoHxtM5MN7lPgcBrVtdtueW2qP3fpdXYx47aPwaf+Mdf2MeO2j8jXX8v/Tez8nJP6TiCmap9xYvmMcdk+8zOkajpa+/8XQOjKdzYDydA+PpHMiV0pj/X2vMYweNX+NvvONvzGMHjb+xj786ulRTRERERERERESkGirOREREREREREREqqHi7BJ9GRNrdIRGTV9/4+kcGE/nwHg6B8bTOZArpTH/v9aYxw4av8bfeMffmMcOGn9jH391dI8zERERERERERGRamjGmYiIiIiIiIiISDWcjQ5QF/XrE8rUSfdgMpmIjo1jybLlVR53cXHhyenTCLn2WvLy8nj82RdIO3PGoLQN06+dg1v+/CeiIiOw2x2czcnhudlzOJ2eblDahunXzsGPhg8eyAtPPsHtk6Zw8PvDVzhlw1aTczBy6GDuuu3/qKiAw0eP8uTzLxmQtOH6tXPQyt+PGQ9Pw9PTA7PJzIK332Vb4k6D0jY8j02bysCwMM7m5HDrhLur3eeByffQv28fSkpKeGbmyxw6cuQKp5SGoDG/9/P38+XJ6Q/RorkPFRXw5eoYln+xoso+vbp3Y+Z/nsSWdhqADVu+5t0PlxoR97L44sP3KSw6h8PuwG63V7uaXEP9WdM2OJhnH/935edBAa156/0lfPL5l5XbGtr5r+7fFq9mnjz7+KMEtGpF2pkzPPbM8+QXFFx0bGT4KO649RYAFn/0MTEJa69o9t+rurHf+8+7GNQvjPLyclJsNp6d9QoFhYUXHVuT75O6rrrx33Xb/xEVaSEnJxeA1999r9r3cjX93aguq278zz7+b9oGBwPQzNOT/IICbrt78kXHNoTz/3uoOPv/mEwmpk2ZzH3THyU9I5PFC+axeet2jp88WblPlDWCvPwCbhr/D0YNG8rkCf/g8WdfMDB1w1KTc3DoyBFun7SakpIS/jR2DPf+806dg1pUk3MA0LRJE/76xz9wIDnZoKQNV03OQZugQG675Wb++a8HyS8ooLmPt4GJG56anIM7br2FdRs38fnK1VzVti1znn+GP/7feANTNyyr4xP49MuVzJg+rdrH+/ftQ5ugQG4a/w+6du7Ew/+6lzun3H9lQ0q919jf+9ntDua9sYhDR47QtEkT3nt9PolJey76N3/v/gNMe/xJg1JefpMfnE5uXl61jzXknzUnU1Iqf0k2mUysXPYhG7dsvWi/hnT+q/u35ba/3czOPXtZsmw54/72V277219Z8Pa7VY7zaubJnbfdyh2TplBRAe+9Pp/N27ZXW7DVVdWNPTFpN6+//S52h4PJd/2D8bfcfNHYf/RL3yf1wc+9r1j22Rcs/e9nP3tcTX83quuqG/9P/y27b+KEakvTH9X38/976FLN/0+XkBBSbGnY0k5TXl5OwoaNDBnYv8o+gwf0J2bN+b8urN+0md49exiQtOGqyTnY/c0+SkpKADiQfBB/X18jojZYNTkHAP+8/TaWfPJfSkvLDEjZsNXkHNwYaeWzFasq37CdvfCXMqkdNTkHFRXg0bQpAJ4eHmRkZRkRtcHau/8Aefn5P/v4kAH9iUlYB8C3yQfx9PSkZYsWVyqeNBCN/b1fVnZ25eypc0VFHD95Cn/flganqlsay8+a3j17kGpLa/BXcVT3b8tPv8dj1qxlyMABFx0X1rs3iUl7yMsvIL+ggMSkPfTr0/uKZK4t1Y09MWk3docDuPB7lV/D/b3q195X/Jya/m5U1/3a+EcOHULC+g1XLlA9ouLs/+Pn25L09IzKz9MzMvFrWfXNg1/LlpzJOL+P3eGgoLAQby+vK5qzIavJOfipsZYItu3cdSWiNRo1OQchHTrQyt+PrTsSr3S8RqEm56BNcBBtg4N469WXeXv+HPr1Cb3SMRu0mpyDtz/4kIhRI4j+eAmvPP8fXn5t4ZWO2aj5+bYkPeOn5ygDP/3CL5dI7/3+J6BVKzp2uIYDBw9d9Nj1XTqz5M2FzHn+Gdq3a2dAusunoqKCeS89z3sL53PjGOtFjzeWnzXhw4ey5md+aW7I5x+gRXMfsrKzgfNlcovmPhftc/H/B5kN7v+DsZbRbEus/veqX/s+qc9uujGKD996ncemTaWZp+dFj1/q76f1UY/rryP77FlOpdqqfbwhn/+a0KWaUq9ZRo6gc8i13PPAw0ZHaVScnJz41z3/5JmZLxsdpVEzm80EBwVyz4MP4+/nyxuvzObWCXf/4hRrqV2jhw8jJj6BpZ9+znWdO/PUIw/x97vupqJCC1aLSP3SxN2dF558nFcXvsm5c+eqPHbw8BH+8PfbKCoupn/fPsx8egY33X6nQUlr38T7HyQjK4vmPt7Me+kFTpw8xd79B4yOdUU5OzszuH8/Xn978UWPNfTzX53G+O/47X//G+V2O3Hrvqr28Yb6ffJ59Cre/XApFRUVTLz9Nu67ewLPzZ5jdKwrbvSIYb8426yhnv+a0oyz/09GZhb+/n6Vn/v7+V506U1GVhat/M7vYzaZ8PTwaLTX+l4ONTkHAH169eT2v/+Nh554irIyXSpYm37tHDRt2oSrr2rHwpdn8sWH79O1cydm/ecpOnW81oC0DVNNvg/SMzLZvG07drudtNNnOJmSQpvgoCsdtcGqyTkYa41g7cZNABxITsbV1RUf74Y3C6WuysjMwt/vp+fIj4xMXS4rl0bv/c7/IeaFp54gft16Nmz5+qLHz507R1FxMQDbEnfi7OzcoGbc/Xi+z+bksvHrrXTpFFL18Ubws6Z/394cOnyE7Jycix5r6OcfIPtsTuXlty1btKj29hcX/3/g22D+PxgzOpyB/cJ48oWZP7vPr32f1FfZOTk4HA4qKipYERNHl5CLx1XT30/rK7PJxLBBA0nYsOln92mo57+mVJz9f5IPHaJNUCABrVvh7OxM+LChbN66vco+m7duJ3L0KACGDxnMrr3fGBG1warJOejY4Rqm3z+Fh2Y8pfs6XQa/dg4KC89h+fPN/PH/xvPH/xvPt8kHeWjGU1pVsxbV5Ptg09at9OreDQBvLy/aBgeTmpZmRNwGqSbn4Ex6On169gTgqrZtcHVx1c+kK2jztu1Eho8EoGvnThQUFlZeaiNSU3rvd36lteMnTvLxZ59X+3iL5s0rP+4S0hEnk1ODKQ7d3d1o2qRJ5cd9Q3tx9PjxKvs0hp81o4cP+9nLNBvy+f/R5m3/+x6PHD2KzVu3XbTPjl27CAvtRTNPT5p5ehIW2osdu+r/7WL69Qnl/27+Cw898VTlPaT/fzX5Pqmvfnq/wqGDBlQ7rpr8O1Gf9QntyfGTp8jIzKz28YZ8/mvKyck/pPHNQ/0V/fv2YeqkiZhMJlbFreG9pcuYMH4cB78/zOZt23F1ceHJRx6mY4dryMvP54nnXqhcnllqx6+dg/kzX+Ca9leRmXX+TcuZ9AwemvGUsaEbmF87Bz+18OWZzHtzkYqzWlaTc/Cvu/9Jvz6h2B0O3vtoGWs3bDQ4dcPya+fgqrZtefSBf9GkSRMqKip4bdE7JCbtNjp2g/GfRx+hV/du+Hh7kX32LIve/xBnZzMAX6yKAWDalMn06xNKcUkJz856RT+H5DdpzO/9ul/XlTdffZkjR4/huHCD8NfffY/WF2ZXfLEqhr/cOJY/jb0Bu91OSWkJc19/i/3fNYwVtQMDWvPSUzOA8zPv1ny1nveWLuOPN0QCjeNnjbu7GyuWLuFP426nsPD8Zbo/HX9DO//V/duycetWnnv8UVr7+3M6PZ3HnnmOvPwCOnW8lj/dMIbnX3kVgBssoxl/y98AeG/px6yOTzBwJJeuurHfdsvNuLq4VJahB5IPMnPufHxbtuDRB+7ngcdm/Oz3SX1T3fh7de/GtR2uhgpIO32GF1+dR1Z2dpXxQ/X/TtQ31Y1/ZVw8Tzz0IAeSkyt/3gEN8vz/HirOREREREREREREqqFLNUVERERERERERKqh4kxERERERERERKQaKs5ERERERERERESqoeJMRERERERERESkGirOREREREREREREqqHiTEREREREREREpBoqzkRERERERERERKqh4kxERERERERERKQaKs5ERERERERERESqoeJMRERERERERESkGirORERERERExFARI4Yz98Xnfvbx7td15ZPFb1/BRCIi5zk5+YdUGB1CRORKWfjyTOLWfkV0bNxFj322ZDGlpaXccudEA5KJiIiIyI+2r43jL7fdQYot7ZKOGzM6nKhICxPvf/AyJRORxkYzzkREgJ7drqe5jw+BAQF0DulodBwRERGRRsNs0q+lIlJ3ORsdQEQar3F/+ys3Rlpo7uNDekYGb7z7Phu/3orJZOLeCXcSOXoU584VsfTTz5g2ZTIDR0didzjw8GjKv+6eyIC+fXBUOFgdn8Ci95fgcDgq/8p44LtkxlojKCgoZNa819i2cxd33zGe7td1pWvnTtw/aSKr4xN4+bWFAESOHsXmrdtwc3UjMnwUyYe+r8zZvl07pk6aSMi112K3l/PJ51/y/sefYDKZGHfzTYy1RtDcx4dTKak8/OTTpGdk8q97/knEiBG4ubqQlp7OjOde5OjxE0Z9qUVEREQM4e/nywOT7qH79V0xmUys+WoDB78/zI2RFr49dIjI8FF8vnI1Kam2yplir78yC4Alb75OBRU8//Icss/m8NQjDxF1y7iffd4f39f9nOu7dGbq5HtoGxzEyZRU5ix4nf3fJQPnZ6r9Y9zf8fH2Jjc3jzcXv0/8V+sJDgzg0Qen0rHDNZSXl7Nrz14ef/YFANq1CebBeycRcu215OTm8tZ777Nu42YA+vftw30T78Lfz4/Cc+dY9tkXLP3vZ5fryywil5GKMxExTKotjbunTiMr+ywjhw7mqUce5i/j/8GQAf3o37c34yZOpri4mOdnPFbluCceepCzObn8ZfwdNHF3Z/az/+FMegZfro4BoGunEGLWJGD58838YYyVRx+cyti/3cobi9+n23VdL7pU083NjRGDB/H4cy/i7ubK9PvvY+4bb1FeXk7TJk2YP/MFlv73U6Y9/iTOzs60b9cWgFv+8ifCRwzjgUdncDIlhQ5Xt6e4pISw3qH0vP56/nr7nRQUFnJV2zbkFxReuS+siIiISB1gMpl4+dn/sGvvXp76v5k47A46h3QkODCQLp07kbBhI5F/+RvOzs6MGja08rh7HniI7WvjGDfxnspLNXt17/arz/tLvJp58vJz/+GVBW+Q8NV6RgwdwsvP/Ye/jP8HpaWlPDD5bu6Y/C9OpqTQskULvJo1A+Cft48nMWk3k6dNx8XZufJ13N3dmPfSC7z1/gdM/ffjXHN1e+a99Dw/HDvB8ZMneezBqTz2zHN8c+Bbmnl6Eti6dW1/eUXkCtGcWBExzFebNpOZlU1FRQVrN2ziVGoqXTp1ZOTQIXzy+QoyMjPJLyjgg2XLK49p4ePDgL59eHXhGxQXl3A2J5dln31B+PD/vdlKO5POipg4HA4Hq9esxc+3JS2aN//ZHMMHDaS0rIzEXUl8vT0RZ2dnBob1BWBgvzCyzmaz9NPPKS0r41xREd8ePARAlNXCm4vf52RKCgBHjh4jLy//fOHWtAnt2rbBycmJ4ydPkZWdfTm+hCIiIiJ1VpeQEHxbtuC1N9+muLiE0rIyvjnwLQCZWVn898to7A4HJaWltfa8P2dAWBinUm3ErV2H3eEgYf0GTpxKYXC/fgA4Kiq4pn073FxdycrO5tiJ81cKlNvLad3KH9+WLau8zqB+YaSdOcPq+ATsDgffH/mB9Zu/ZuTQwZXHtW/XjqZNm5JfUMChI0cuaYwiUndoxpmIGMYaPpJb/vwnAlq3AqBJkyb4eHnj27IlZzIyKvc7k/6/j1u38sfZ2ZlVy5dWbjM5OXEmI7Py8+yzZys/LikpAaBpE3ey/7e5isjRo1i3cTN2hwO7w8H6zVuIHD2KjV9vpZWfL6k/c1Pan3ssae83fPrlSh6aMpnWrfzZsOVr5r35NufOnavBV0VERESkYWjl78vpM+nYHY6LHkv/yfu72nzen+PXsgWnz6RX2Xb6zBn8fFtSXFzC48++wK03/ZlHH5zKvm+/Y94bb3HiVAqvvfUOE++4jXdfm0t+QQFLP/2MVXFraO3vT9dOISR8+Wnl85nNZuLWrgPg308/yx233sKku+7gyNFjLHx7MQeSk3/zmEXEOCrORMQQrf39+ffUfzHl4X+z/7tkHA4HH7yxACcnJ7Kys/H3863ct5W/X+XHZzIyKS0rw/Knv17Sm6UfVVRUXUjYz9eX0B7d6dIphOGDBwLg7uaGq6sr3l5enMnIJHz4sGqf60xGJkGBAdXeu2z5lytY/uUKmvt489wTj/F/f/0Lb733wSXnFREREamvzqRn0srfH7PJdNH7toqfOeb3Pu/PycjKZlgr/yrbWvn7s31nEgA7diWxY1cSbq6uTLxjPP9+4H7unjqN7LNneeGVuQB0v64r82a+wN59+zmTkcmeffu5b/qj1b5e8qHveXjG05jNZm76QxTPPfEoN/593O8YtYgYRZdqiogh3N3dqQDO5uQCMCYinKvbXwXAuo2buPmPf8CvZUs8PTwYd/NNlcdlZWeTmLSb++7+J02bNsXJyYmggAB6dru+Rq+bffYsgQH/u8eENXwkp1JS+evtdzFu4mTGTZzMTbffRXpmJqNHDOPr7Tto2aIFN//pD7i4uNC0SRO6dgoBIDo2jom3j6dNUCAAHdq3x8urGZ1DOtK1Uwhms5mi4mJKSktx/IaST0RERKQ+++7QIbKys5l01z9wd3fD1cWFbl271OjYrOxsAgMCftPzOgGuLi5V/tuamEjb4CBGjxiG2WRi1LAhtG/Xli3bd9DCx4fBA/rh7u5WeWuOH9+7jRgyGD/f83/QzcsvoKKiAkdFBV9v30Gb4CAso0ZiNpsxm810DunIVW3b4OzsTMSI4Xh4NMVut1NYeA5Hhd4LitRXmnEmIoY4fvIkH//3MxbNm0NFhYPYhHXsu3DPiBWrY2kTHMSHi16nsPAcy79cQa/u3Sr/ovj0S7OYfNc/WPbOmzRt2pTUtDSWLPtvjV73k89XMGP6g/xp7Bji1n5F39BefBa9ssrlnQBfrFxNZPgo/vtlNPdN/zdTJ93NneNupaysjGWff8m3Bw/x8aef4+riwtwXn8fb24sTp1KY/uR/8GjalPvvmUhgQGtKS0vZsSuJj5Z/+jOJRERERBomh8PBtCee5IHJ97Bi6RIqKipY89UGDh3+9ft9vf3Bh8x4eBpubq68OGdu5R9bf+l59337HQDdruvKptiVVZ5v4OhIpj3+JFMn3c3D/5pCSqqNaY8/SW5eHi1btOCWP/+JJ6c/REVFBYd/OMrMua8B0CWkI/dPmoinhwfZZ88yZ+Eb2NJOA/Cv6Y/xr3v+yb/unoDJZOLwD0eZ+8ZbAFjCR/LglEmYTSZOpKTy1Asza+VrKiJXnpOTf8jvmSUrInLZ9e/Tm4fvn8Ifbx1vdBQRERERERFpRHSppojUOW6urvTv2wezyYRfy5bcedutbNyy1ehYIiIiIiIi0shoxpmI1Dlubm68/sos2rUJpqSklK07Enll4RtalVJERERERESuKBVnIiIiIiIiIiIi1dClmiIiIiIiIiIiItW4pFU1H5s2lYFhYZzNyeHWCXdXu88Dk++hf98+lJSU8MzMlzl05PyKKZHho7jj1lsAWPzRx8QkrAUg5NoOPPHwg7i5urEtcSevLHgdAK9mnjz7+KMEtGpF2pkzPPbM8+QXFPzmgYqIiIiIiIiIiFyKSyrOVscn8OmXK5kxfVq1j/fv24c2QYHcNP4fdO3ciYf/dS93Trkfr2ae3HnbrdwxaQoVFfDe6/PZvG07+QUFPPyvKbzwyly+TT7InOefoX+f3mzbuYvb/nYzO/fsZcmy5Yz721+57W9/ZcHb7/5ivrjPPiHt9JlLGZKIiIjIRQJat8Ly55uNjiG/Qu/9REREpDb80nu/SyrO9u4/QECrVj/7+JAB/YlJWAfAt8kH8fT0pGWLFvTq3o3EpD3k5Z+fMZaYtId+fXqz+5t9eDRtyrfJBwGISVjHkIED2LZzF4MH9GfSgw+f375mLQtfnvmrxVna6TPcMfm+SxmSiIiIyEUWL5hndASpAb33ExERkdrwS+/9avUeZ36+LUnPyKj8PD0jAz/fltVsz6zcnpGZedH+AC2a+5CVnQ1AVnY2LZr71GZUERERERERERGRX3RJM86MVFFR/eKfN46x8odIKwA+Pt5XMpKIiIiIiIiIiDRgtVqcZWRm4e/nV/m5v58fGZlZZGRm0at7t59s92X3N/vIyMzCz9f3ov0Bss/m0LJFC7Kys2nZogVnc3Krfc0Vq2NZsToW0GUVIiIiIkaqbiGpZx//N22DgwFo5ulJfkEBt909+aJjv/jwfQqLzuGwO7Db7boEU0REROqEWi3ONm/bzk03jiVh/Qa6du5EQWEhWdnZ7Ni1i3v+cTvNPD0BCAvtxevvvEtefgGF587RtXMnvk0+SGT4SJZ/GV35XJGjR7Fk2XIiR49i89ZttRlVRERERGpZdQtJPf7sC5Uf3zdxAgWFhT97/OQHp5Obl3dZM4qIiIhciksqzv7z6CP06t4NH28voj9ewqL3P8TZ2QzAF6ti2LojkQF9+/DpB+9SXFLCs7NeASAvv4B3P1rKuxdmhL3z4UeVCwXMmvcaTzz0IG5urmxL3MW2xJ0AfLDsE557/FGiLBGcTk/nsWeeq7VBi4iIiEjt+7WFpEYOHcK9D02/golEREREfp9LKs5mPP/ir+4ze/6CarevilvDqrg1F20/+P3hyqn8P5WXl8+Uh/99KfFEREREpI7qcf11ZJ89y6lUW7WPV1RUMO+l56moqOCL1TGVt+IQERERMVK9WRxARERExGw2MyCsD6dSUjl+8pTRceQSjB4xjIT1G3728Yn3P0hGVhbNfbyZ99ILnDh5ir37D1y035VaGMrN1ZVB/cP44dhx/b8mIiLSiJmMDiAiIiLya9oEBTLprjtYsXQJs/7zFFFWi9GR5BKYTSaGDRpIwoZNP7tPRtb5BaLO5uSy8eutdOkUUu1+K1bHcsfk+7hj8n3k/MziUbXBbDbz3BOPMXhA/8v2GiIiIlL3acaZiIiI1Elurq4MHzyIqEgLvbp3o9xuZ9uOnUTHxrF1R6LR8eQS9AntyfGTp8jIzKz2cXd3N0xOJs4VFeHu7kbf0F68++FHVzhlVeeKisg+e5bgwABDc4iIiIixVJyJiIhInXLtNVcTZbVgGTWCZp6epNhsLHxnMTFrEsjMyjY6nvyC6haSWhkXT/iwiy/T9G3ZgkcfuJ8HHptBi+bNeempGcD5mV5rvlrP9p1JBoygqhRbGkEBKs5EREQaMxVnIiIiYjgPj6aMHj6MKKuFziEdKSktZcPmLayIiWPPvv1UVFQYHVFq4OcWknpm1ssXbcvMyuaBx86XZba004ybOOmyZvstUm1p9Oh2ndExRERExEAqzkRERMQw3a/rSlSkhZFDBuPu7s6Ro8d4+bWFxK/7irz8AqPjSSOXmpZGxMjhuLi4UFZWZnQcERERMYCKMxEREbmiWvj4YB09iiirhXZtgiksLCR27VdEx8aRfOh7o+OJVEqxpWEymQho1YqTKSlGxxEREREDqDgTERGRy85kMhEW2ouoSAuD+/fD2dmZbw58ywcff8K6TZsoLi4xOqLIRVJtNgCCAgNUnImIiDRSKs5ERETksglo1YoxEeGMtUTQyt+Pszk5fPL5l6yMi+f4yVNGxxP5Ram2NACtrCkiItKIqTgTERGRWuXi4sKQAf2Islro06snADt2JfHq62+wedsOysvLDU4oUjPZOTmcKyrSypoiIiKNmIozERERqRXt27UjyhqBNXwkPt7epJ05wztLPmJV/BrOpGcYHU/kN0m1pWnGmYiISCOm4kxERER+sybu7owcNoQoq4VuXbtQVlbGpq3biY6NY+fuPTgcDqMjivwuqbY02rUNNjqGiIiIGETFmYiIiFyyLiEdiYq0ED58GB5Nm3L8xEnmvfEWsWvXcTYn1+h4IrUmJS2N/mF9cHJyoqKiwug4IiIicoWpOBMREZEa8fJqhmXkCKKsFjpc3Z6iomLWbtxIdEwc+79LNjqeyGWRarPh5uqKb8uWZGRmGh1HRERErjAVZyIiIvKznJycCO3RnSirhWGDBuDq6sq3Bw/xwpy5JKzfyLlz54yOKHJZ/XRlTRVnIiIijY+KMxEREbmIX8uWjIkIZ6w1gqCAAPLy8/lydSzRsXEcOXrM6HgiV0xK2vniLCgwgD379hucRkRERK40FWciIiICgNlsZkBYH260Wujftw9ms5lde/by5uL32bhlKyWlpUZHFLnizpxJp7y8nOAArawpIiLSGKk4ExERaeTaBAUy1hpBZHg4vi1bkJGZxZJly1kZt4bUC7NtRBoru8NB2pl0ggJVnImIiDRGKs5EREQaITdXV4YPHkRUpIVe3btRbrezdUci0TFxbEvcid3hMDqiSJ2RmpZGUGCg0TFERETEACrOREREGpFrr7maKKsFy6gRNPP05FSqjYXvLCZmTQKZWdlGxxOpk1JtNjoPG2Z0DBERETGAijMREZEGzsOjKaOHDyPKaqFzSEdKSkvZsHkLK2Li2LNvPxUVFUZHFKnTUm1peHs1o5mnJ/kFBUbHERERkStIxZmIiEgD1f26rkRFWhg5ZDDu7u4c/uEos+cvYM1X68nL1y//IjWVYvvfypoHvz9scBoRERG5klSciYiINCAtfHywjh5FlNVCuzbBFBYWEpOwjujYOP3CL/Ib/bhIRrCKMxERkUZHxZmIiEg9ZzKZCOsdSpQ1gsH9++Hs7Mw3B77lg48/Yd2mTRQXlxgdURqJx6ZNZWBYGGdzcrh1wt0A3HXb/xEVaSEnJxeA1999j22JOy86tl+fUKZOugeTyUR0bBxLli2/otl/yY/FWVCAVtYUERFpbFSciYiI1FMBrVpxg2U0N0SMppW/H2dzcvjk8y9ZGRfP8ZOnjI4njdDq+AQ+/XIlM6ZPq7J92WdfsPS/n/3scSaTiWlTJnPf9EdJz8hk8YJ5bN66neMnT17uyDVSXFxCZla2VtYUERFphFSciYiI1CMuLi4MGdCPKKuFPr16ArBjVxKvvv4Gm7ftoLy83OCE0pjt3X+AgFatLvm4LiEhpNjSsKWdBiBhw0aGDOxfZ4ozOD/rLDhQM85EREQaGxVnIiIi9UD7du2IskZgDR+Jj7c3aWfO8M6Sj1gVv4Yz6RlGxxP5RTfdGEVk+CiSv/+eeW8sumhlSj/flqT/5P/j9IxMunYKudIxf1GKzUbvHj2MjiEiIiJXmIozERGROqqJuzsjhw0hymqhW9culJWVsWnrdqJj49i5ew8Oh8PoiCK/6vPoVbz74VIqKiqYePtt3Hf3BJ6bPec3P9+NY6z8IdIKgI+Pd23F/FWptjSso0bi6uJCaVnZFXtdERERMZaKMxERkTqma6cQoiItjBo2FI+mTTl+4iTz3niL2LXrOHvhBusi9UV2Tk7lxyti4pj97NMX7ZORmYW/v1/l5/5+vmRkZVX7fCtWx7JidSwAixfMq92wvyDVlobJZCIwoLXuISgiItKIqDgTERGpA7y8mmEZOYKoSAsd2renqKiYtRs3Eh0Tx/7vko2OJ/KbtWzRgqzsbACGDhrA0ePHL9on+dAh2gQFEtC6FRmZWYQPG8qM51+6wkl/WcpPVtZUcSYiItJ4qDgTERExiJOTE6E9uhNltTBs0ABcXV359uAhXpgzl4T1Gzl37pzREUUuyX8efYRe3bvh4+1F9MdLWPT+h/Tq3o1rO1wNFZB2+gwvvnp+lphvyxY8+sD9PPDYDOwOB7PnL2Tui89hMplYFbeGYydOGDyaqlJtF4ozLRAgIiLSqKg4ExERucL8WrZkTEQ4Y60RBAUEkJefz5erY4mOjePI0WNGxxP5zWY8/+JF21bGxVe7b2ZWNg88NqPy822JO9mWuPOyZfu9cnJzKSwsJDgw0OgoIiIicgWpOBMREbkCzGYzA8P6EhVpoX+f3pjNZnbt2cubi99n45atlJSWGh1RRH5FSlqaZpyJiIg0MirORERELqM2QYGMtUYwZnQ4LVu0ICMziyXLlrMybg2pF+6ZJCL1Q6otjauvusroGCIiInIFXVJx1q9PKFMn3YPJZCI6No4ly5ZXeby1vz+PTZtKcx8f8vLzefKFmWRkZgIw+a5/MCCsLwCLP1rK2g2bAAjt0Z37Jk7A2dmZg4cP8/zsOdgdDpp5evLYtKkEBwZSUlrKc7Nf4ejxunWvCxERkeq4uboyfPAgoiIt9OrejXK7na07EomOiWNb4k7sDofREUXkN0i1pTGoXxgmkwmHvo9FREQahRoXZyaTiWlTJnPf9EdJz8hk8YJ5bN66neMnT1buM2XiBGIT1hGTsJbQHt2ZdOcdPP3SLAaE9SXk2g7cNnESLq4uLHx5FlsTd1FUVMSMh6dx70OPcCo1lQnjxxE5OpyVcfGM//vfOPzDUR556hnatQlm2pTJTHn435fliyAiIlIbOna4hrGWCCyjRtDM05NTqTYWvrOYmDUJZGZlGx1PRH6nlLQ0XF1d8fNtyZn0DKPjiIiIyBVgqumOXUJCSLGlYUs7TXl5OQkbNjJkYP8q+7Rv15Zde/cCkLT3G4YM6Fe5fc++A9gdDoqLSzhy9Bj9+4Ti7eVFWXkZp1JTAUhM2s3wwQP/91x7zj/XiVMpBLRuRQsfn985XBERkdrl4dGUP40dw3sL5/PBGwuIirTw9fZEJj34MH+9/U4++PgTlWYiDUTlypoBus+ZiIhIY1HjGWd+vi1J/8lf1tIzMunaKaTKPoePHmXYoIEs/2IFwwYNxMPDAy+vZhz+4Sh3jbuVpZ9+hrubG6E9unH8xAlycnMxm8106ngtB78/zIghg/H39zv/XD8cZdjggXxz4Fu6hHSkdatW+Pn5kp2TU+U1bxxj5Q+RVgB8fLx/69dBRETkknS/ritRkRZGDhmMu7s7h384yuz5C4hft578ggKj44nIZfBjcRYcGMjub/YZnEZERESuhFpdHGD+m4uYdu9kxkSEs3ffAdIzMnDYHSQm7aZLSEcWzX2FnNxcDnyXXHl/lyeefZH775mIi4sLiUm7cdjPb/9g2XIemHQ3H7yxgB+OHef7Iz9Uey+JFatjWbE6FoDFC+bV5nBERESqaOHjg3X0KKKsFtq1CaawsJCYhHVEx8Zx8PvDRscTkcvsTEYGZWVlWllTRESkEalxcZaRmVU5GwzA38+XjKysKvtkZmXzyNPPANDE3Z3hgwdSUFgIwHtLl/He0mUAPP3odE6mnL8880ByMndPnQZA39BetAkOAuDcuXM8O/uVyuf+4sP3SU07fckDFBER+T1MJhNhvUOJskYwuH8/nJ2d2bv/AB98/AnrNm2iuLjE6IgicoU4HA7SzpxRcSYiItKI1Lg4Sz50iDZBgQS0bkVGZhbhw4Yy4/mXquzj7eVFXn4+FRUVjL/lZlbGrQHO/9Lh6elBXl4+Hdq3p0P79iTuSgKguY83Z3NycXFxYdzNN1WWa54eHhSXlFBeXs6NkRb27N/PuXPnamvcIiIivyigVStusIzmhojRtPL3I/tsDss+/5KVsXGcOJVidDwRMUiqLY1g3eNMRESk0ahxcWZ3OJg9fyFzX3wOk8nEqrg1HDtxggnjx3Hw+8Ns3radXt27MenOO6iggr37DjBr/oLzL2I28+ac2QAUnjvHUy/OrLxU89a/3sSgsL44mUx8vnIVSXu/AeCqtm2ZMf1BKirg2PETPPfynNoeu4iISBUuLi4MGdCPKKuFPr16ArBjVxKvvv4Gm7ftoLy83OCEImK0FFsa13XpbHQMERERuUIu6R5n2xJ3si1xZ5Vti95fUvnx+s1bWL95y0XHlZaVccudE6t9ztfeepvX3nr7ou0HkpP56+13XUo8ERGR36R9u3ZEWSOwho/Ex9ubtDNneGfJR6yKX8OZnyyMIyKSmpZGM09PvLyakZeXb3QcERERucxqdXEAERGR+qKJuzsjhw0hymqhW9culJWVsXHrNlbGxrNz955qF6QREalcWTMggO9UnImIiDR4Ks5ERKRR6dophKhIC6OGDcWjaVOOnTjB3NffInbtOnJyc42OJyJ1XMqF4iwoMJDvDn1vcBoRERG53FSciYhIg+fl1QzLyBFERVro0L49RUXFrN24keiYOPZ/l2x0PBGpR2xpF2acaWVNERGRRkHFmYiINEhOTk6E9uhOlNXCsEEDcHV15duDh3hhzlwS1m/USs0i8puUlJaSnplJkFbWFBERaRRUnImISIPi5+vLDRHh3GAZTVBAALl5+Xy5Opbo2DiOHD1mdDwRaQBSbWkEacaZiIhIo6DiTERE6j2z2czAsL5ERVro36c3ZrOZnbv38Ma777Nxy9eUlpUZHVFEGpBUWxphvUONjiEiIiJXgIozERGpt9oEBTLWGsGY0eG0bNGCjMwslixbzsq4NaReuA+RiEhtS01Lw8+3JW5ubpSUlBgdR0RERC4jFWciIlKvuLm5MXzwIKKsEfTq3o1yu52tOxKJjoljW+JO7A6H0RFFpIGrXFkzoDVHj58wOI2IiIhcTirORESkXujY4RqirBYiRg6nmacnp1JtLHxnMTFrEsjMyjY6nogAj02bysCwMM7m5HDrhLsBuPefdzGoXxjl5eWk2Gw8O+sVCgoLLzr2iw/fp7DoHA67A7vdzh2T77vS8Wss1WYDICgwQMWZiIhIA6fiTERE6ixPDw9GjxhGlNVCp47XUlJayvpNW4iOjWPPvv1UVFQYHVFEfmJ1fAKffrmSGdOnVW5LTNrN62+/i93hYPJd/2D8LTez4O13qz1+8oPTyc3Lu1Jxf7OUC5eCBwcEGpxERERELjcVZyIiUud0v64rUZEWRg4ZjLu7O4d/OMrs+QuIX7ee/IICo+OJyM/Yu/8AAa1aVdmWmLS78uMDyQcZMWTQlY5V6/Ly8skvKNDKmiIiIo2AijMREakTWvj4EDl6FGOtFtq1CaawsJCYhHVEx8Zx8PvDRscTkVow1jKatRs2VftYRUUF8156noqKCr5YHcOK1bHV7nfjGCt/iLQC4OPjfdmy/ppUW5qKMxERkUZAxZmIiBjGZDIR1juUG60WBvUPw9nZmb37D/DBx5+wbtMmiou1Wp1IQ3H73/9Gud1O3Lqvqn184v0PkpGVRXMfb+a99AInTp5i7/4DF+23YnVsZam2eMG8y5r5l6SkpRFyzTWGvb6IiIhcGSrORETkigto1YobLKO5IWI0rfz9yD6bw7LPv2RlbBwnTqUYHU9EatmY0eEM7BfGvQ898rP7ZGRlAXA2J5eNX2+lS6eQaouzuiLVlsawgQMwm0xazVdERKQBU3EmIiJXhIuLC0MG9CPKaqFPr54A7NiVxKuvv8HmbTsoLy83OKGIXA79+oTyfzf/hXseeJiSkupnkbq7u2FyMnGuqAh3dzf6hvbi3Q8/usJJL02qLQ1nZ2f8/f1IO33G6DgiIiJymag4ExGRy6p9u3ZERUZgHTUSH29v0s6c4Z0lH7Eqfg1n0jOMjiciteg/jz5Cr+7d8PH2IvrjJSx6/0Nuu+VmXF1cmPfS88D5BQJmzp2Pb8sWPPrA/Tzw2AxaNG/OS0/NAMBsNrPmq/Vs35lk5FB+VYrNBkBwYKCKMxERkQZMxZmIiNS6Ju7ujBo2lKhIC9d36UxZWRkbt24jOiaOXXv24tBlTSIN0oznX7xo28q4+Gr3zczK5oHHzpdltrTTjJs46bJmq22paWkABAUEsJM9BqcRERGRy0XFmYiI1JqunUKIirQwathQPJo25diJE8x9/S1i164jJzfX6HgiIrUmIzOL0tJSgrWypoiISIOm4kxERH4XL69mWEaOICrSQof27SkqKmbtxo1Ex8Sx/7tko+OJiFwWDocD2+kzBKk4ExERadBUnImIyCVzcnKid88eRFkjGDpwAK6urnybfJAXXnmVhA2bOHfunNERRUQuu9S0NIICVJyJiIg0ZCrORESkxvx8fbkhIpyxlggCA1qTm5fPF6tiWBkbz5Fjx4yOJyJyRaXa0uhx/XVGxxAREZHLSMWZiIj8IrPZzMCwvkRFWujfpzdms5mdu/fw+rvvsXHL15SWlRkdUUTEECk2Gx5Nm9Lcx5uzObqPo4iISEOk4kxERKrVJiiQsdYIxowOp2WLFmRkZrFk2XJWxq2pXE1ORKQxS7X9uLJmoIozERGRBkrFmYiIVHJzc2P44EFEWSPo1b0b5XY7X2/fQXRMHNt37sLucBgdUUSkzvjxjwjBgQEcSNZiKCIiIg2RijMREaFjh2uIslqIGDmcZp6enEq1seDtd4lZs5as7Gyj44mI1Em2tNM4HA6trCkiItKAqTgTEWmkPD08GD1iGFFWC506XktJaSnrN20hOjaOPfv2U1FRYXREEZE6rbSsjIzMLBVnIiIiDZiKMxGRRqb7dV2JirQwcshg3N3d+f7ID8yev4D4devJLygwOp6ISL2SmpZGcICKMxERkYZKxZmISCPQwseHyNGjGGu10K5NMIWFhcQkrGVFTByHDh8xOp6ISL2VYktjYFgfo2OIiIjIZaLiTESkgTKZTIT1DuVGq4VB/cNwdnZm7/4DvL90GV9t3kxxcYnREUVE6r1Um42WLVrQxN2douJio+OIiIhILVNxJiLSwAS0bsUNEaO5IWI0rfz9yD6bw7LPv2RlbBwnTqUYHU9EpEH5cWXNoIAAjhw7ZnAaERERqW0qzkREGgAXFxeGDuxPlNVC39BeOBwOduxK4tXX32Dzth2Ul5cbHVFEpEFKsV0ozgJVnImIiDREKs5EROqxq69qx1hrBNZRI/Hx9ibtzBneeu8DVq9J4Ex6htHxREQavNSfFGciIiLS8Kg4ExGpZ5q4uzNq2FCiIi1c36UzZWVlbNy6jeiYOHbt2YvD4TA6oohIo5FfUEBuXj7BKs5EREQapEsqzvr1CWXqpHswmUxEx8axZNnyKo+39vfnsWlTae7jQ15+Pk++MJOMzEwAJt/1DwaE9QVg8UdLWbthEwChPbpz38QJODs7c/DwYZ6fPQe7w4GHR1OefuRhWvn7Yzab+ei/n7I6PqE2xiwiUi917dyJKGsEo4YNxaNpU46dOMHc198idu06cnJzjY4nItJopaalERSg4kxERKQhqnFxZjKZmDZlMvdNf5T0jEwWL5jH5q3bOX7yZOU+UyZOIDZhHTEJawnt0Z1Jd97B0y/NYkBYX0Ku7cBtEyfh4urCwpdnsTVxF0VFRcx4eBr3PvQIp1JTmTB+HJGjw1kZF89fosZy7MRJpj3xFD7e3nyy+G3i163XfXpEpFHx9vLCMmoEUVYL17S/iqKiYtZu3Eh0TBz7v0s2Op6IiHB+Zc3OIR2NjiEiIiKXQY2Lsy4hIaTY0rClnQYgYcNGhgzsX6U4a9+uLXPfeBOApL3fMPPpGZXb9+w7gN3hwF5cwpGjx+jfJ5SkvfsoKy/jVGoqAIlJuxl/y82sjIunAmjatAkATZq4k5efj91ur5VBi4jUZU5OTvTu2YMoawRDBw7A1dWVb5MP8sIrr5KwYRPnzp0zOqKISLUemzaVgWFhnM3J4dYJdwPg1cyTZx9/lIBWrUg7c4bHnnme/IKCi46NDB/FHbfeAsDijz4mJmHtFc3+e6Ta0hg+ZDBms1nvV0VERBoYU0139PNtSfpPbjSdnpGJX8uWVfY5fPQowwYNBGDYoIF4eHjg5dWMwz8cpX+fUNzc3PD28iK0Rzda+fmRk5uL2WymU8drARgxZDD+/n4AfPplNP+vvTuPi6pe/D/+YgYEhEAUUBZNyzJb3HHfRYbBpO79fbvdbrf9drtldetmy23vtli2W2qmqW3m7VZeNwS03JfcU7pomrkAo2wKys7M/P4AUQQTEzjM8H4+Hj6cOeczw/vMYZl5zznz6dihA4v+PYfPp3/A21M+wOl0XvAGi4g0VSHBwdxx8018/cks3ps4gb69ezNvUQJ/vvte7nrgIeYnJKo0E5EmbXHSUh7+59PVlt36xxvZtG07N9x+F5u2befWP/6hxu0CLvLnrltv5q4H/s6d9/+du269mYv8/Rsr9gVLs9nwNJtpFxpqdBQRERGpZ/U6OcB706Yz/v5xjLGMZvuOFDKzsnDYHWzcspUru1zO9Hff4lheHin/S8Ve+eHVz7z0Kg/dew9eXl5s3LIVh71ieb8+vfnp558ZN/5xIsPDmPTaBP68M6XGi8brxli5Ps4KQKtWgfW5OSIiDc5sNjO4fz/GWi0MiOqD2Wxm09ZtTJ05m5Vr1lJaVmZ0RBGROtu+M4Wwtm2rLRsycAD3PfIYAAnJy5jy5kQmz5hZbUy/Pn3YuGUb+ccrjkTbuGUb/aP6sHT5ikbJfaFOn1kz3WYzOI2IiIjUpzoXZ1nZOVVHgwGEhgSTlZNTbUx2Ti5PvPAiUDHr24ghgzhRUADA7DlzmT1nLgAvPPk4B9MqTs9MSU3lbw+PB6Bv7160j4wA4NrYGD754t8AFaeIHj5Mx/aR/G/3T9W+5vzFS5i/eAkAsyZPquvmiIgYqn1EBPFWC3Ex0bRp3Zqs7Bw+nfslCxOT9aJLRNxK66BW5OTmApCTm0vroFY1xoQEtyEz64wzG4Lb1BjXVKVVFmeR4WFs3GJwGBEREalXdS7OUnfvpn1EOGHt2pKVncPo4cN49pXXqo0JDAgg//hxnE5n5WeVJQMVEwv4+/uRn3+czp060blTJzZurnhWEdQqkKPH8vDy8uKWG2+oKteOZGYS1asnP6T8SOtWrejQPpL0ys9XExFxRd7e3owYMph4q4Ve3btRbrezdsP3LEhIZMOmzVVH4oqIuLML/eiNpni2QXZODiWlpZpZU0RExA3VuTizOxy88d4U3n31ZUwmE4sSk/nlwAHuvu0Wdv20h9XrN9Crezfuu+sOnDjZviOF19+bXPFFzGamvf0GAAWFhTz/6sSqF4g3/+EGBvfri4fJxDcLF7Fl+w8AzPxsDs88+gifTZ+KBx5MmT6TvPz8+t5+EZEGd3nnS4m3xmIZNYKL/P05lJ7B5BkzSUheVnUUhoiIu8o9eow2rVuTk5tLm9atOXosr8aYrOwcenXvVnU9NCSYrT/sqPX+muLZBk6nkwybjYhwFWciIiLu5rw+42z9xk2s37ip2rLpH39adXn56jUsX72mxu1Ky8q46a57ar3P9z+cwfsfzqixPDsnl78/8dT5xBMRaTL8/fywjBrB2FgLV1x+GcUlJSxftYaFiUlnfTEoIuKOVq/fQFxMNJ/O/ZK4mGhWr1tfY8z3mzdz7523V00I0K93L6Z+NLPGuKYsPcNGZHi40TFERESkntXr5AAiIs1dj2uuJj4ulpFDh+Dj7c1Pe3/m9UmTSf5uOcdPnDA6nohIg/rXk0/Qq3s3WgUGsOCLT5n+8Wd8MvffvPz0k8THWjicmclTL74MwBWXX8bvrx3DK2+9Q/7xE8z8fA4zK48g++izz6smCnAVaRk2evXobnQMERERqWcqzkRELlDrVq2Ii4lmrDWWi9tHcqKggITkpcxPSGT3nr1GxxMRaTTPvvJqrcsfeOyfNZbt+mkPr7z1TtX1RYnJLKr8fFxXlG6z0dLXl9ZBQeQePWp0HBEREaknKs5ERH4Dk8lEvz69uc4ay+AB/fD09GT7zhQ+njOX71avpri4xOiIIiLSiE6fWVPFmYiIiPtQcSYich7C2rXlWksMY2NjCA0JIffoMeZ+PY+FiUkcOJRmdDwRETFIemVxFhEexo4f/2dwGhEREakvKs5ERM7By8uLYYMGEG+NpW/vXjgcDjZs2sxbkz9gzYbvKS8vNzqiiIgYzHbkCA6Hg4gwzawpIiLiTlSciYicxSUdL2as1YI1ehStAgOxHTnCh7M/YXHyUo5kZhkdT0REmpCysjKOZGVpZk0RERE3o+JMROQ0vj4+RA8fRnxcLNdc2ZWysjJWrlvPgoRENm/bjsPhMDqiiIg0UekZNiLCdcSZiIiIO1FxJiICXNX1CuKtFqKHD8OvZUt+OXCAd6d+yJJl33IsL8/oeCIi4gLSbTaGDBhgdAwRERGpRyrORKTZCgwIIDZ6JPHWWC7t1JGiomKWrljJgoREUlJTjY4nIiIuJi3DRuugVrT09aWwqMjoOCIiIlIPVJyJSLPi4eFBn549iLdaGDZoIC1atODH1F1MeOsdlq5YRWFhodERRUTERZ0+s+aen/cZnEZERETqg4ozEWkWQoKDudYymrGxFsLD2pGXf5x5ixJYuCSJvb/8YnQ8ERFxA2kZGQBEqjgTERFxGyrORMRtmc1mBvfvx1irhQFRfTCbzWzauo2pM2ezcs1aSsvKjI4oIiJuJO3kEWdhmiBARETEXag4ExG30z4ignirhbiYaNq0bk1mdjafzP2ShYlJZNgOGx1PRETcVGFhIUePHSMiPNzoKCIiIlJPVJyJiFvw9vZmxJDBxFst9OrejXK7nbUbvmdBQiIbNm3G7nAYHVFERJqBdJuNyHAdcSYiIuIuVJyJiEvr0rkz8XEWLKNG4u/nx6H0DCbPmElC8jJycnONjiciIs1MeoaNa6680ugYIiIiUk9UnImIy/H388MyagTx1li6XNaZ4pISlq9aw8LEJLb+sMPoeCIi0oylZdiIHj4MT09PysvLjY4jIiIiF0jFmYi4jB7XXE18XCwjhw7Bx9ubn/b+zOuTJpP83XKOnzhhdDwRERHSM2yYzWbC2oZyKD3D6DgiIiJygVSciUiT1jooiLjRo4iPi6VDZCQnCgpISF7K/IREdu/Za3Q8ERGRatJtlTNrhoepOBMREXEDKs5EpMkxm0z0i+pDvNXC4P798PT0ZPvOFGZ/PpfvVq+muLjE6IgiIiK1SsuoLM7CwoEtxoYRERGRC6biTESajLB2bRkba+Fay2hCQ0LIPXqMuV/PY2FiEgcOpRkdT0RE5JxycnMpKirWzJoiIiJuQsWZiBjKy8uLYYMGEG+NpW/vXjgcDjZs2sxbkz9gzYbv9cHKIiJuoENkJC89/c+q6xFh7fjw40/59zf/rVrWq3s3Jv7rOTJshwFYsWYtMz+b09hR60X6YRsRKs5ERETcgoozETHEJR0vZqzVQtzoaAIDArAdOcKHsz9hcfJSjmRmGR1PRETq0cG0NG792zgATCYTC+d+xso162qM274zhfFPP9fY8epdeoaNyPBwo2OIiIhIPVBxJiKNpqWvL9HDhxEfZ+Hqrl0pKytj5br1LEhIZPO27TgcDqMjiohIA+vTswfpGTYOZ2YaHaXBpGfY6Ne7l9ExREREpB6oOBORBndVZHVq1AAALf9JREFU1yu4zhpL9IhhtPT1Zd/+A7wzdRqJy77jWF6e0fFERKQRjR4xjOTlK2pdd82VXfl02hSyc3KYNG0Gvxw40Ljh6km6zYaPjw/BbVqTnZNrdBwRERG5ACrORKRBBAYEEBs9knhrLJd26khRUTFLV6xkQUIiKampRscTEREDeHp6MmRAf6bOmFVj3a49e7n+T7dSVFzMgL5RTHzhWW64/a4a464bY+X6OCsArVoFNnjm3+LUzJphKs5ERERcnIozEak3Hh4e9OnZg3irhWGDBtKiRQt+TN3FhLfeYemKVRQWFhodUUREDDSgbx9279lL7rFjNdad/jdi/cZNeD54P4EBAeTl51cbN3/xEuYvXgLArMmTGjTvb5WekQFAZHg4P6T8aHAaERERuRAqzkTkgoUEB3OtJYaxsTGEh7UjLz+fbxYuZmFiEj//st/oeCIi0kTEjBh+1tM0WwcFkXv0KABXdrkcD5NHjdLMVdiOZFJut2tmTRERETeg4kxEfhOz2czg/v2Ij4ulf5/emM1mNm3dxpSPZrFq7TpKy8qMjigiIk2Ij483fXv34tV3Th0l9rtr4wCYtyiBkUMH8/ux12K32ykpLeGZlyYYFfWC2e12jmRmqjgTERFxAyrOROS8tI+IIN5qYYxlNK2DgsjMzuaTuV+yMDGJDNtho+OJiEgTVVxcguX3f6i2bN6ihKrLX81fyFfzFzZ2rAaTnmEjMkzFmYiIiKtTcSYi5+Tt7c2IIYOJt1ro1b0b5XY7azd8z4KERDZs2ozd4TA6ooiISJOSlmFj5NDBRscQERGRC6TiTETOqkvnzsTHWbCMGom/nx+H0tKZPP0jEpZ+S06uZgkTERE5m3SbjVaBgfj5taSgQJPjiIiIuCoVZyJSjb+fH5ZRI4i3xtLlss4Ul5SwfNUaFixJZNuOnUbHExERcQnpGTYAIsPC2b13r8FpRERE5LdScSYiAPS45mri42IZOXQIPt7e/LT3Z16fNJnk75Zz/MQJo+OJiIi4lLSMDAAiwsNUnImIiLiw8yrO+kf15uH77sVkMrFgSSKfzv2y2vp2oaE8Nf5hglq1Iv/4cZ6bMJGs7GwAxv3lTgb26wvArM/nsGzFKgB69+jOg/fcjaenJ7v27OGVN97G7nBw8x/+D8vIEUDF7H0dO7TH+n83kn9cL+BF6kvroCDiRo8iPi6WDpGRnCgoICF5KfMTEtm9R0/yRUREfquTE+ZoZk0RERHXVufizGQyMf6BcTz4+JNkZmUza/IkVq/bwP6DB6vGPHDP3SxZ+i0JS5fRu0d37rvrDl547XUG9utLl8s6c+s99+HVwospb77Ouo2bKSoq4tnHxnP/o09wKD2du2+7hbiY0SxMTOLzL7/i8y+/AmBw/3788f/9TqWZSD0wm0z0i+pDvNXC4P798PT0ZNuOncz6/Au+W7WGkpISoyOKiIi4vMKiInKPHiVSxZmIiIhLq3NxdmWXLqRl2KrePVu6YiVDBw2oVpx1urgD734wDYAt239g4gvPVi3ftiMFu8OBvbiEvft+YUBUb7Zs30FZeRmH0tMB2LhlK7fddCMLE5Oqfe3RI4ezdPmKC9pQkeYurF1bxsZauNYymtCQEHKPHmPu1/NYmJjEgUNpRscTERFxO2kZNiLCVJyJiIi4MlNdB4YEtyEzM6vqemZWNiFt2lQbs2ffPoYPHgTA8MGD8PPzIyDgIvb8vI8BUb3x9vYmMCCA3j260TYkhGN5eZjNZq64/DIARg4dQmhoSLX79Pb2pn+fPixfveY3b6RIc+Xl5UX08KFMeu0V5n32Mbf/6Y/s3fcLTzz/IvE3/Zn3p3+k0kxERKSBpGfYdKqmiIiIi6vXyQHemzad8fePY4xlNNt3pJCZlYXD7mDjlq1c2eVypr/7Fsfy8kj5Xyp2hwOAZ156lYfuvQcvLy82btmKw+6odp9DBvRj548/nvU0zevGWLk+zgpAq1aB9bk5Ii7rko4XE2+NxTp6FIEBAdgOH+HD2Z+wKCmZzKxso+OJiIg0C+k2G5ZRI/Dy8qKsrMzoOCIiIvIb1Lk4y8rOqXY0WGhIMFk5OdXGZOfk8sQLLwLg6+PDiCGDOFFQAMDsOXOZPWcuAC88+TgH0ypOz0xJTeVvD48HoG/vXrSPjKh2n9HDh5H8K6dpzl+8hPmLlwAwa/Kkum6OiNtp6etL9PBhxMdZuLprV8rKyli5dh0LEhLZtG07TqfT6IgiIiLNSlqGDZPJRFjbthxM0xHeIiIirqjOxVnq7t20jwgnrF1bsrJzGD18GM++8lq1MYEBAeQfP47T6az8rLJkoGJiAX9/P/Lzj9O5Uyc6d+rExs1bAAhqFcjRY3l4eXlxy403VJVrAH5+LenZrRvPvzqxPrZVxC1d1fUKrrPGEj1iGC19fdm3/wDvTJ1G4rLvOJaXZ3Q8ERGRZis9IwOomFlTxZmIiIhrqnNxZnc4eOO9Kbz76suYTCYWJSbzy4ED3H3bLez6aQ+r12+gV/du3HfXHThxsn1HCq+/N7nii5jNTHv7DQAKCgt5/tWJVadq3vyHGxjcry8eJhPfLFzElu0/VH3N4YMGsXHLFoqLNcufyOkCAwKwjh7F2FgLl3bqSFFRMUtXrGRBQiIpqalGxxMREREqPuMM0MyaIiIiLuy8PuNs/cZNrN+4qdqy6R9/WnV5+eo1tX6If2lZGTfddU+t9/n+hzN4/8MZta5bnLyUxclLzyeiiNvy8PAgqmcP4uNiGTpwAC1atODH1F1MeOsdlq5YRWFhodERRURE5DS5x45RWFSkmTVFRERcWL1ODiAi9S8kOJhrLTGMjY0hPKwdefn5fLNwMQsTk/j5l/1GxxMREZFfkZ5h0xFnIiIiLkzFmUgTZDabGdy/H/FxsfTv0xuz2cymrduY8tEsVq1dR6lm5hIREXEJ6Rk2Lu4QaXQMERER+Y1UnIk0IR0iI4m3WoiLiaZ1UBCZ2dl8MvdLFiYmkWE7bHQ8EREROU9pNhsD+kXh4eGhGa5FRERckIozEYN5e3szcuhg4q2x9Ox2DeV2O2s3fM+ChEQ2bNpcNZGGiIiIuJ70jAy8W7QguE0bsrKzjY4jIiIi50nFmYhBunTuTHycBcuokfj7+XEoLZ3J0z8iYem35OTmGh1PRERE6sHpM2uqOBMREXE9Ks5EGpG/nx+WUSOIt8bS5bLOFJeUsHzVGhYsSWTbjp1GxxMREWkw8z77mIKiQhx2B3a7nTvGPVhjzD/G3cuAvlGUlJTw4sQ32b13rwFJ61earaI4iwgP0996ERERF6TiTKQR9Ox2DWOtFkYOHYKPtze79+7l9UmTSf5uOcdPnDA6noiISKMY98jj5OXn17puQN8o2keEc8Ntd3JV1yt47O/3c9cDDzVuwAZw5Egm5eXlRIZpZk0RERFXpOJMpIG0DgpiTEw0Y60WOkRGcqKggMVJySxISHKLd9BFRETq09CBA0hY+i0AP6buwt/fnzatW7v8xxfYHQ5sRzKJCFdxJiIi4opUnInUI7PJRL+oPsRbLQwe0B9Ps5ltO3Yy6/Mv+G7VGkpKSoyOKCIiYgin08mk117B6XQyb3EC8xcvqbY+JLgNmVlZVdczs7IICW7j8sUZQLrNRkR4uNExRERE5DdQcSZSD8LatWVsrIVrLaMJDQkh9+hR5n71DQuWJHEwLc3oeCIiIoa756FHyMrJIahVIJNem8CBg4fYvjPlvO/nujFWro+zAtCqVWB9x2wQ6RkZdB0+3OgYIiIi8huoOBP5jVp4eTF00EDirRb69u6F3W5nw+YtvDX5A9Zs+J7y8nKjI4qIiDQZWTk5ABw9lsfKteu48oou1YqzrOwcQkNCqq6HhoSQlZ1T437mL15SdbTarMmTGjh1/UjPsBEYcBEX+fvrs01FRERcjIozkfN0SceLibfGYh09isCAAGyHjzBt1icsTk4mM0vTzIuIiJzJx8cbk4eJwqIifHy86du7FzM/+7zamNXrN3DDdWNZunwFV3W9ghMFBW5xmiZAWsapmTV3/bTH4DQiIiJyPlScidRBS19foocPIz7OwtVdu1JWVsbKtetYkJDIpm3bcTqdRkcUERFpsloHBfHa888CYDabSf5uORs2beF318YBMG9RAuu+38jAvlF89clMiktKeOn1t4yMXK/SbRXFWaSKMxEREZej4kzkV1zV9Qqus8YSPWIYLX192bf/AO9MncaSpd+Sl59vdDwRERGXkGE7zC333Fdj+bxFCdWuv/He5MaK1KhOFmcRYZpZU0RExNWoOBM5Q2BAANbRoxgba+HSTh0pLCpi2fKVLFiSREpqqtHxRERExMUUF5eQnZOrmTVFRERckIozEcDDw4Oonj2Ij4tl6MABtGjRgpTUVF558x2WrVhJYVGR0RFFRETEhaXbbESG64gzERERV6PiTJq1kOBgrrXEMDY2hvCwduTl5/PNwsUsTEzi51/2Gx1PRERE3ERaRgZ9evQwOoaIiIicJxVn0uyYzWYG9+9HfFws/fv0xmw2s2nrNqZ8NItVa9dRWlZmdEQRERFxM+kZNqzRo2jh5aXnGiIiIi5ExZk0Gx0iI4m3WoiLiaZ1UBCZ2dl8/MW/WZSUTIbtsNHxRERExI2lZ9gwmUyEh7Vj/8FDRscRERGROlJxJm7N29ubkUMHE2+NpWe3ayi321mzfgMLliTx/abN2B0OoyOKiIhIM5B22syaKs5ERERch4ozcUtdOncmPs6CZdRI/P38OJSWzuTpH7E4eRm5R48aHU9ERESamfSMyuJMEwSIiIi4FBVn4jYu8vcnZuQI4uMsdOncmeKSEpavWsOCJYls27HT6HgiIiLSjB3Ly6OgoIDI8HCjo4iIiMh5UHEmLq9nt2uIt8YyYuhgfLy92b13L69Pep+kb5dzoqDA6HgiIiIiQMXpmjriTERExLWoOBOX1DooiDEx0Yy1WugQGcmJggIWJyWzICGJ3Xv3Gh1PREREpIb0DBuXdOxodAwRERE5DyrOxGWYTSb6RfUh3mph8ID+eJrNbNuxk1mff8F3q9ZQUlJidEQRERGRs0rPsDG4fz9MJhMOTVAkIiLiElScSZMXHtaOsbEWxlhGExocTO7Ro8z96hsWLEniYFqa0fFERERE6iTNZqNFixaEBLfhSGaW0XFERESkDlScSZPUwsuLoYMGcl1cLFG9emK329mweQtvvT+VNRu+p7y83OiIIiIiIuelambNsDAVZyIiIi5CxZk0KZd26sjYWAvW0aMIDAjAdvgI02Z9wuLkZDKzso2OJyIiIvKbnSzOIsPD2frDDoPTiIiISF2oOBPDtfT1JXr4MOLjLFzdtStlZWWsXLuOBQmJbNq2HafTaXREERERkQt2JCuLsrIyzawpIiLiQlSciWGu7tqV+LhYoocPpaWvL/v2H+CdqdNYsvRb8vLzjY4nIiIiUq8cDge2I0dUnImIiLgQFWfSqAIDArCOHkW8NZZLOl5MYVERy5avZMGSJFJSU42OJyIiItKg0jNsRIapOBMREXEVKs6kwXl4eBDVswfxcbEMGzQQLy8vUlJTeeXNd1i2YiWFRUVGRxQRERFpFGkZNq6+sqvRMURERKSOVJxJgwkNCWZMTAzxVgth7dqSl5/P1wsWsTAxiZ9/2W90PBEREWkkoSHBPPf4o7QOaoXTCf9dnMCX8+ZXG9Orezcm/us5MmyHAVixZi0zP5tjRNwGlW6zcZG/PwEBF5Gff9zoOCIiInIOKs6kXpnNZoYM6E+81UK/Pr0xm81s3LKVyTM+YtXa9ZSWlRkdUURERBqZ3e5g0gfT2b13Ly19fZk99T02btnG/oMHq43bvjOF8U8/Z1DKxlE1s2ZYGP9TcSYiItLknVdx1j+qNw/fdy8mk4kFSxL5dO6X1da3Cw3lqfEPE9SqFfnHj/PchIlkZWcDMO4vdzKwX18AZn0+h2UrVgHQu0d3Hrznbjw9Pdm1Zw+vvPE2docDqHjn8aF778HT05NjeXnc98hjF7zB0jA6REYSb7UQFxNN66AgMrOz+fiLf7MoKbnqnWMRERFpnnJyc8nJzQWgsKiI/QcPERrcpkZx1hykVRZnEeHh/G/3TwanERERkXOpc3FmMpkY/8A4Hnz8STKzspk1eRKr122o9oTngXvuZsnSb0lYuozePbpz31138MJrrzOwX1+6XNaZW++5D68WXkx583XWbdxMUVERzz42nvsffYJD6encfdstxMWMZmFiEv5+fjz64Dge+ufTHMnMIqhVYIM8APLbeXt7M3LoYOKtsfTsdg3ldjtr1m9gwZIkvt+0uaoAFRERETkprG1bLu98KSm7dtdYd82VXfl02hSyc3KYNG0Gvxw4YEDChpVhqzziLDzc4CQiIiJSF3Uuzq7s0oW0DFvV0UNLV6xk6KAB1YqzThd34N0PpgGwZfsPTHzh2arl23akYHc4sBeXsHffLwyI6s2W7TsoKy/jUHo6ABu3bOW2m25kYWISllEjWLFmHUcyswA4eiyvfrZYLliXzp2Jj7NgGTUSfz8/DqalMXn6RyxOXkbu0aNGxxMREZEmytfHhwnPPc07U6ZRWFhYbd2uPXu5/k+3UlRczIC+UUx84VluuP2uGvdx3Rgr18dZAWjlgm+slpSWkpmdTWS4ZtYUERFxBXUuzkKC25BZWWIBZGZlc9UVXaqN2bNvH8MHD+LLefMZPngQfn5+BARcxJ6f9/GXW25mzldf4+PtTe8e3dh/4ADH8vIwm81ccfll7PppDyOHDiE0NASA9hEReHp6MuXNibT09eXf8/7LkqXf1tNmy/m6yN+fmJEjiI+z0KVzZ4pLSvhu1WoWJCSyfWeK0fFERESkiTObzUx4/hmSvl3OijVra6w/vUhbv3ETng/eT2BAAHn5+dXGzV+8hPmLlwAwa/Kkhg3dQNIzbESoOBMREXEJ9To5wHvTpjP+/nGMsYxm+44UMrOycNgdbNyylSu7XM70d9/iWF4eKf9LrTqN75mXXuWhe+/By8uLjVu24rBXLK8o1Dpz/6NP4N3CmxmT3iblf7uqjk47ydXfdWzqena7hnhrLCOGDsbH25vde/fy+qT3Sfp2OScKCoyOJyIiIi7iqfEPs//AQb74+pta17cOCqo6cv3KLpfjYfKoUZq5i/QMG/369DY6hoiIiNRBnYuzrOycqqPBoGJa8aycnGpjsnNyeeKFF4GKQ/FHDBlUVa7MnjOX2XPmAvDCk49zMK2iAEtJTeVvD48HoG/vXrSPjAAgMzubvPx8iotLKC4uYdvOFC679JIaxZk7vOvY1LQOCmJMTDRjrRY6REZyoqCAxUnJLEhIYvfevUbHExERERfT/eqriBsdzd59v/DJB5MBmDpzNu0qn1vOW5TAyKGD+f3Ya7Hb7ZSUlvDMSxOMjNyg0m02QoLb4O3tTUlJidFxRERE5FfUuThL3b2b9hHhhLVrS1Z2DqOHD+PZV16rNiYwIID848dxOp2Vn1WWDFRMLODv70d+/nE6d+pE506d2Lh5CwBBrQI5eiwPLy8vbrnxhqpybfW69Txy/32YTSY8vby46oouzD3LO5Ry4cwmE/2i+hBvtTB4QH88zWa27djJrM+/4LtVa/SkTkRERH6zH1J+pH907K+O+Wr+Qr6av7CREhmrambNsHbs2+9+EyCIiIi4kzoXZ3aHgzfem8K7r76MyWRiUWIyvxw4wN233cKun/awev0GenXvxn133YETJ9t3pPD6exXvKHqazUx7+w0ACgoLef7ViVWnat78hxsY3K8vHiYT3yxcxJbtPwCw/+AhNmzewmfTp+JwOFmwJFFPLBpAeFg7xsZaGGMZTWhwMLlHj/LFf75mYWIyB9PSjI4nIiIi4nbSMzIAiAgP0/NbERGRJu68PuNs/cZNrN+4qdqy6R9/WnV5+eo1LF+9psbtSsvKuOmue2q9z/c/nMH7H86odd3nX37F519+dT4RpQ5aeHkxdNBArouLJapXT+x2Oxs2b+HN96awZsP32O12oyOKiIiIuK00W8URZ5Fh4QYnERERkXOp18kBpGm7tFNHxsZasI4eRWBAALbDR5g26xMWJyeTmZVtdDwRERGRZiE//zjHT5zQzJoiIiIuQMWZm2vp60v08GHEx1m4umtXSktLWbVuPQsSEtm0bTtOp9PoiCIiIiLNTnqGTcWZiIiIC1Bx5qau7tqV+LhYoocPpaWvL/v2H+DtKR+QuOw7t53aXURERMRVpNlsdLn0UqNjiIiIyDmoOHMjgQEBWEePIt4ayyUdL6awqIhly1cyf0kiP6buMjqeiIiIiFRKz7AxfNBAzCZT1aRZIiIi0vSoOHNxHh4eRPXqSXxcLMMGDsDLy4uU1FReefMdlq1YSWFRkdERRUREROQM6Rk2PD09CQ0NwXb4iNFxRERE5CxUnLmo0JBgrrXEMDbWQli7tuTl5/P1gkUsTEzi51/2Gx1PRERERH5FWkYGAJHh4SrOREREmjAVZy7E09OTwf37EW+10D+qDyaTiY1btjJ5xkesWrue0rIyoyOKiIiISB2k22wARISFsYltBqcRERGRs1Fx5gI6REYSb7UQFxNN66AgMrOzmT1nLouSksmwHTY6noiIiIicp6zsHEpLS4nUzJoiIiJNmoqzJsrb25tRQ4cw1mqhZ7drKLfbWbN+AwuWJPH9ps36EFkRERERF+ZwOMg4fIQIFWciIiJNmoqzJqbLZZ25Li6WmJEj8Pfz42BaGpOnf8Ti5GXkHj1qdDwRERERqSfpNhsRYSrOREREmjIVZ03ARf7+xIwcQXychS6dO1NcUsJ3q1azICGR7TtTjI4nIiIiIg0gPcNGj2uuNjqGiIiI/AoVZwbq2e0a4q2xjBg6GB9vb3bv3cvrk94n6dvlnCgoMDqeiIiIiDSgtIwM/Fq2JKhVIEeP5RkdR0RERGqh4qyRtWndmriYaOJjLbSPjOBEQQGLk5JZkJDE7r17jY4nIiIiIo0kPePkzJrhKs5ERESaKBVnjcBsMtE/qg/xcbEM6t8PT7OZrT/sYObnc/hu1RpKSkqMjigiIiIijSzdVlGcRYaHkZKaanAaERERqY2KswYUHtaOsbEWxlhGExocTO7Ro3zxn69ZmJjMwbQ0o+OJiIiIiIEybIdxOByaWVNERKQJU3FWz1p4eTF00ECui4slqldP7HY7GzZv4c33prBmw/fY7XajI4qIiIhIE1BaVkZWdo6KMxERkSZMxVk9ubRTR+KtscRGjyQwIIAM22GmzfqYRUlLycrONjqeiIiIiDRB6TYbkWEqzkRERJoqFWcXoKWvL6NHDCPeGstVXa+gtLSUlWvXsWBJEpu3bcfpdBodUURERKRJ6B/Vm4fvuxeTycSCJYl8OvfLauu9vLx47vHxdLnsMvLz83n6pQnYjhwxKG3jSUvP4NrYGBK//jdFxcUUFRVTVFR06nJxMUXFRRQWFVFcXFL5fzGFRUWn1p8+/uTl4mKd6SAiIlIPVJz9Bld37Up8XCzRw4fS0teXn3/Zz9tTPiBx2Xfk5ecbHU9ERESkSTGZTIx/YBwPPv4kmVnZzJo8idXrNrD/4MGqMfFWC/nHT3DDbXcSPXwY4+6+k6dfmmBg6sbx+X++4lh+Hr4+vrT09cXX1wdfHx98fX0JDQmuvH5qudlsrvN9l5aWVpVoxUXFlJWX43A4sDscOBx2HHYH5XY7DofjtOUOHHYHdrv91LjKdfbKsXa747Tb2CuvV9yfw+ls9DePPTw8ar0M4FFz8NnH1hhMxWPhcFR/jOz26o9XjfWO6o/zaY9ZtfuqvB+TyYSn2Yz5rP9+Zb3JVG2c2VR9/cnb4QH2cjtl5WWUl9spLy+n3G6nrKwMu73yermdcns5ZWXllNvLq8bZa1lWVl5esa7yNidv73A6qh67at8GZ3xPOHHWuspZ/Uqt4z3wqPWx8PSsud3nelyq1tfyuJaWlVFSUkJxcQklpSf/L6W4uJjikhJKSkopLimu/L+E0tJSHTgh4qZUnNVRwEX+xMWMJt4ayyUdL6awqIhly1cyf0kiP6buMjqeiIiISJN1ZZcupGXYyLAdBmDpipUMHTSgWnE2ZOAAZnz8GQDLV61m/AP3GZK1sR04lMaUGbPqPL6Flxe+vhUlm4+PDy19K0q2irLtVOlWUbZ5V/xfuczTs6IQMJlMmMynLnt6emIymU5bZ6oqD0wm86l1VQVE5biq21QuM5sx1dY+1TOTyYTD4ai2zFmtZDnDmaXN6WNrFDqneACmypKlqSu32yvKzlr/VTxWZk8znmZPvLw88TR74ulpxsvLy+Dk7qWiUKu9bCspKaG4tJSS09aV28uBijIQTitzK/8/+dN0cvmp9ae+5pm3PfX/qTFOZ8X3usPpgKrLTqhc5nSC0+HACTgcDpyVBXjFP3A6HWfcpnKdw4mTU2PPx5nF9W9R9TVP71srr1T/OT+19szb1j6u4vHz8PCozOlx6joe4FH98faoXE/leI+T21d1e6qWnby/WramljzOMzfvtG0+v3GnHqrqX+fMx7Dm+trz1Vx/yum7tuYbFGdu+9nfwDhzbFp6Bus3ba75BRuBirM6ah8RwUP33sPO/6Xy8htv8+3KVRQWFRkdS0RERKTJCwluQ2ZmVtX1zKxsrrqiS/UxbdpwJKtijN3h4ERBAYEBATqa/wylZWWUlpXpcWlkHh4e1QpEk+lU8Wg2mfA4vUg0n1ZCnhxnNlUvJyuPFPPw8Kg6+sxut59WgDlqLcHKHdULMUflkYEXouJorYoiraJQ88Tr5HXPypLttLLt5FgvT6/Ko7xO3eb0x+vU5RoP5qmL1D7u144iBKqOmDvzcSovL686CrLmY1qzUDzbOofTSQsvL3y8vfGu+tcCH29vfHx88G7RAh8fb7xbeOPj41017vTxPmfc5iJ//+rrfbzxNJuryo66Fh38SklytrIDPDCZPKqKm6qyp3KZyaP6OpPJVOMxFzHaspWrVJw1dT/u2s2Nd/yFA4fSjI4iIiIi0mxdN8bK9XFWAFq1CjQ4jTQXTqezqlShzOg09evkdpWUGJ2kaSkvL2/2B0qYTKaKarOyTPMAPEwmPDzA5GGqWF5VuNV+9NGvuZBTW2s7su7UkXo1i9dqxezJ9bUUtKdvh7PyCLuKo+oqrjupXOY8/UiviuucPBKvxvVT93fqPk/PUz1/9TweNcZ5VG+Zz7p9NY5g9Kh9vcepO/7V9Wc+brU9Zied7bRrqH7qdS2rzziC+NTl8rJyjKLi7DyoNBMRERE5f1nZOYSGhlRdDw0JJisnp/qYnBzahoSQlZ2N2WTC38+v1qOq5i9ewvzFSwCYNXlSwwYXEWnGTj8lWpONSHOmYzBFREREpEGl7t5N+4hwwtq1xdPTk9HDh7F63YZqY1av20BcTDQAI4YOYfP2H4yIKiIiIlKNijMRERERaVB2h4M33pvCu6++zNyZH/LtylX8cuAAd992C0MG9Adg4ZJEAgMC+M/HM7np/37PlBkzDU4tIiIiolM1RURERKQRrN+4ifUbN1VbNv3jT6sul5aV8dSLLzd2LBEREZFfpSPOREREREREREREaqHiTEREREREREREpBYqzkRERERERERERGqh4kxERERERERERKQWbjU5QFi7tsyaPKlBv0arVoEcO5bXoF9Dzk6Pv/G0D4ynfWA87QPjNfQ+CGvXtsHuW+qPnvs1rOa87aDt1/Y33+1vztsO2v7muv2/9tzPwyO0i7MRs7i8WZMncce4B42O0Wzp8Tee9oHxtA+Mp31gPO0DaSzN+XutOW87aPu1/c13+5vztoO2v7lvf210qqaIiIiIiIiIiEgtVJyJiIiIiIiIiIjUQsXZefpvwhKjIzRrevyNp31gPO0D42kfGE/7QBpLc/5ea87bDtp+bX/z3f7mvO2g7W/u218bfcaZiIiIiIiIiIhILXTEmYiIiIiIiIiISC08jQ7QFPWP6s3D992LyWRiwZJEPp37ZbX1Xl5ePPf4eLpcdhn5+fk8/dIEbEeOGJTWPZ1rH9z0/35PfJwFu93B0WPHePmNtzmcmWlQWvd0rn1w0oghg5jw3DPcft8D7PppTyOndG912Qejhg3hL7f+GacT9uzbx3OvvGZAUvd1rn3QNjSEZx8bj7+/H2aTmckzZrJ+4yaD0rqfp8Y/zKB+/Th67Bg33/23Wsf8Y9y9DOgbRUlJCS9OfJPde/c2ckpxB835uV9oSDDPPf4orYNa4XTCfxcn8OW8+dXG9OrejYn/eo4M22EAVqxZy8zP5hgRt0HM++xjCooKcdgd2O32WmeTc9ffNR0iI3np6X9WXY8Ia8eHH3/Kv7/5b9Uyd9v/tf1tCbjIn5eefpKwtm2xHTnCUy++wvETJ2rcNm50NHfcfBMAsz7/goSlyxo1+4Wqbdvv/+tfGNy/H+Xl5aRlZPDS629xoqCgxm3r8nPS1NW2/X+59c/Ex8Vy7FgeAFNnzq71uVxdXxs1ZbVt/0tP/5MOkZEAXOTvz/ETJ7j1b+Nq3NYd9v+FUHF2BpPJxPgHxvHg40+SmZXNrMmTWL1uA/sPHqwaE2+1kH/8BDfcdifRw4cx7u47efqlCQamdi912Qe79+7l9vsWU1JSwu/HjuH+v96lfVCP6rIPAFr6+vKH311PSmqqQUndV132QfuIcG696Ub++vdHOH7iBEGtAg1M7H7qsg/uuPkmvl25im8WLqZjhw68/cqL/O7PtxmY2r0sTlrKV/9dyLOPj691/YC+UbSPCOeG2+7kqq5X8Njf7+euBx5q3JDi8pr7cz+73cGkD6aze+9eWvr6Mnvqe2zcsq3G3/ztO1MY//RzBqVseOMeeZy8/Pxa17nz75qDaWlVL5JNJhML537GyjXraoxzp/1f29+WW/94I5u2befTuV9yyx//wK1//AOTZ8ysdruAi/y569abueO+B3A6YfbU91i9fkOtBVtTVdu2b9yylakzZmJ3OBj3lzu57aYba2z7Sb/2c+IKzva8Yu7X85jzn6/Peru6vjZq6mrb/tP/lj14z921lqYnufr+vxA6VfMMV3bpQlqGjQzbYcrLy1m6YiVDBw2oNmbIwAEkJFe8u7B81Wr69OxhQFL3VZd9sPWHHZSUlACQkrqL0OBgI6K6rbrsA4C/3n4rn/77P5SWlhmQ0r3VZR9cF2fl6/mLqp6wHa18p0zqR132gdMJfi1bAuDv50dWTo4RUd3W9p0p5B8/ftb1QwcOIGHptwD8mLoLf39/2rRu3VjxxE009+d+Obm5VUdPFRYVsf/gIUKD2xicqmlpLr9r+vTsQXqGze3P4qjtb8vpP+MJycsYOmhgjdv169OHjVu2kX/8BMdPnGDjlm30j+rTKJnrS23bvnHLVuwOB1D5uirEfV9Xnet5xdnU9bVRU3eu7R81bChLl69ovEAuRMXZGUKC25CZmVV1PTMrm5A21Z88hLRpw5GsijF2h4MTBQUEBgQ0ak53Vpd9cLqxsRbWb9rcGNGajbrsgy6dO9M2NIR1329s7HjNQl32QfvICDpERvDhO28y47236R/Vu7FjurW67IMZn3yGJXokC774lLde+Rdvvj+lsWM2ayHBbcjMOn0fZRGiF/xynvTc75Swtm25vPOlpOzaXWPdNVd25dNpU3j7lRfpdPHFBqRrOE6nk0mvvcLsKe9x3RhrjfXN5XfN6BHDSD7Li2Z33v8ArYNakZObC1SUya2DWtUYU/P7INvtvg/GxsawfmPtr6vO9XPiym64Lp7PPpzKU+Mf5iJ//xrrz/f1qSvqcc3V5B49yqH0jFrXu/P+rwudqikuLXbUSLp2uYx7//GY0VGaFQ8PD/5+7195ceKbRkdp1sxmM5ER4dz7yGOEhgTzwVtvcPPdf/vVQ6ylfsWMGE5C0lLmfPUNV3ftyvNPPMqf/vI3nE5NWC0irsXXx4cJzz3NO1OmUVhYWG3drj17uf5Pt1JUXMyAvlFMfOFZbrj9LoOS1r97HnqErJwcgloFMum1CRw4eIjtO1OMjtWoPD09GTKgP1NnzKqxzt33f22a49/x2//0R8rtdhK//a7W9e76c/LNgkXM/GwOTqeTe26/lQf/djcvv/G20bEaXczI4b96tJm77v+60hFnZ8jKziE0NKTqemhIcI1Tb7JycmgbUjHGbDLh7+fXbM/1bQh12QcAUb16cvuf/sijzzxPWZlOFaxP59oHLVv6cknHi5ny5kTmffYxV3W9gtf/9TxXXH6ZAWndU11+DjKzslm9fgN2ux3b4SMcTEujfWREY0d1W3XZB2OtFpatXAVASmoqLVq0oFWg+x2F0lRlZecQGnL6PgohK1uny8r50XO/ijdiJjz/DEnfLmfFmrU11hcWFlJUXAzA+o2b8PT0dKsj7k7u76PH8li5dh1XXtGl+vpm8LtmQN8+7N6zl9xjx2qsc/f9D5B79FjV6bdtWreu9eMvan4fBLvN98GYmNEM6t+P5yZMPOuYc/2cuKrcY8dwOBw4nU7mJyRyZZea21XX16euymwyMXzwIJauWHXWMe66/+tKxdkZUnfvpn1EOGHt2uLp6cno4cNYvW5DtTGr120gLiYagBFDh7B5+w9GRHVbddkHl3e+lMcfeoBHn31en+vUAM61DwoKCon9fzfyuz/fxu/+fBs/pu7i0Wef16ya9aguPwer1q2jV/duAAQGBNAhMpJ0m82IuG6pLvvgSGYmUT17AtCxQ3taeLXQ76RGtHr9BuJGjwLgqq5XcKKgoOpUG5G60nO/ipnW9h84yBdff1Pr+tZBQVWXr+xyOR4mD7cpDn18vGnp61t1uW/vXuzbv7/amObwuyZmxPCznqbpzvv/pNXrT/2Mx8VEs3rd+hpjvt+8mX69e3GRvz8X+fvTr3cvvt/s+h8X0z+qN3++8f949Jnnqz5D+kx1+TlxVad/XuGwwQNr3a66/J1wZVG9e7L/4CGysrNrXe/O+7+uPDxCuzS/41DPYUDfKB6+7x5MJhOLEpOZPWcud992C7t+2sPq9Rto4eXFc088xuWdLyX/+HGeeXlC1fTMUj/OtQ/emziBSzt1JDun4knLkcwsHn32eWNDu5lz7YPTTXlzIpOmTVdxVs/qsg/+/re/0j+qN3aHg9mfz2XZipUGp3Yv59oHHTt04Ml//B1fX1+cTifvT/+IjVu2Gh3bbfzrySfo1b0brQIDyD16lOkff4anpxmAeYsSABj/wDj6R/WmuKSEl15/S7+H5Ddpzs/9ul99FdPeeZO9+37BUfkB4VNnzqZd5dEV8xYl8H/XjeX3Y6/FbrdTUlrCu1M/ZOf/3GNG7fCwdrz2/LNAxZF3yd8tZ/acufzu2jigefyu8fHxZv6cT/n9LbdTUFBxmu7p2+9u+7+2vy0r163j5aefpF1oKIczM3nqxZfJP36CKy6/jN9fO4ZX3noHgGtjY7jtpj8CMHvOFyxOWmrglpy/2rb91ptupIWXV1UZmpK6i4nvvkdwm9Y8+Y+H+MdTz57158TV1Lb9vbp347LOl4ATbIeP8Oo7k8jJza22/VD73wlXU9v2L0xM4plHHyElNbXq9x3glvv/Qqg4ExERERERERERqYVO1RQREREREREREamFijMREREREREREZFaqDgTERERERERERGphYozERERERERERGRWqg4ExERERERERERqYWKMxERERERERERkVqoOBMREREREREREamFijMREREREREREZFa/H9DcoZvsCVZ9QAAAABJRU5ErkJggg==", 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"text/plain": [ - "
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" ] }, "metadata": {}, @@ -200,12 +207,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb112ad158f248a68f0950ef142f1951", + "model_id": "61b563af512640828f05fb04f9795a5b", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/2048 [00:00=bestReward:\n", + " if np.mean(totalRewards) >= bestReward:\n", " bestReward = np.mean(totalRewards)\n", " gail.saveWeights(np.mean(totalRewards))\n", " agentMem.clearMem()\n" diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/4 b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/4 new file mode 100644 index 0000000..e69de29 diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..193fec5 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.index new file mode 100644 index 0000000..aa94262 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/actor.ckpt.index differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/checkpoint new file mode 100644 index 0000000..59bf098 --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/actor/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "actor.ckpt" +all_model_checkpoint_paths: "actor.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/checkpoint new file mode 100644 index 0000000..c2f0da0 --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "critic.ckpt" +all_model_checkpoint_paths: "critic.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..317f4f6 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.index new file mode 100644 index 0000000..b636e3d Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/040553/critic/critic.ckpt.index differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/checkpoint new file mode 100644 index 0000000..531032c --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "discriminator.ckpt" +all_model_checkpoint_paths: "discriminator.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..ae35d93 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.index new file mode 100644 index 0000000..fcd0c5e Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1015-0405/discriminator/discriminator.ckpt.index differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/5 b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/5 new file mode 100644 index 0000000..e69de29 diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..423b52f Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.index new file mode 100644 index 0000000..9feada3 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/actor.ckpt.index differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/checkpoint new file mode 100644 index 0000000..59bf098 --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/actor/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "actor.ckpt" +all_model_checkpoint_paths: "actor.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/checkpoint new file mode 100644 index 0000000..c2f0da0 --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "critic.ckpt" +all_model_checkpoint_paths: "critic.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/critic.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/critic.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..3244b26 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/critic.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/critic.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1020-0235/023530/critic/critic.ckpt.index new file mode 100644 index 0000000..8038752 Binary files /dev/null and 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diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/checkpoint b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/checkpoint new file mode 100644 index 0000000..c2f0da0 --- /dev/null +++ b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "critic.ckpt" +all_model_checkpoint_paths: "critic.ckpt" diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.data-00000-of-00001 b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.data-00000-of-00001 new file mode 100644 index 0000000..e9ee5a1 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.data-00000-of-00001 differ diff --git a/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.index b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.index new file mode 100644 index 0000000..c219c42 Binary files /dev/null and b/Aimbot-PPO-Python/GAIL-Model/1020-0404/040456/critic/critic.ckpt.index differ diff --git a/Aimbot-PPO-Python/GAIL.py b/Aimbot-PPO-Python/GAIL.py index 514d994..a088ca4 100644 --- a/Aimbot-PPO-Python/GAIL.py +++ b/Aimbot-PPO-Python/GAIL.py @@ -21,9 +21,23 @@ class GAIL(object): conActRange: float, gailConfig: GAILConfig, ): + if disActShape == [0]: + # non dis action output + self.disActSize = 0 + self.disOutputSize = 0 + else: + try: + if np.any(np.array(disActShape) <= 1): + raise ValueError( + "disActShape error,disActShape should greater than 1 but get", disActShape + ) + except ValueError: + raise + self.disActSize = len(disActShape) + self.disOutputSize = sum(disActShape) + self.stateSize = stateSize self.disActShape = disActShape - self.disActSize = len(disActShape) self.conActSize = conActSize self.conActRange = conActRange diff --git a/Aimbot-PPO-Python/GAILHistory.py b/Aimbot-PPO-Python/GAILHistory.py index 6b1cd8a..c55f5be 100644 --- a/Aimbot-PPO-Python/GAILHistory.py +++ b/Aimbot-PPO-Python/GAILHistory.py @@ -12,14 +12,20 @@ class GAILHistory(object): self.criticLosses = [] self.demoAccs = [] self.agentAccs = [] + self.averageEntropys = [] + self.discrimRewards = [] - def saveHis(self, rewards, dLosses, aLosses, cLosses, demoAcc, agentAcc): + def saveHis( + self, rewards, dLosses, aLosses, cLosses, demoAcc, agentAcc, averageEntropy, discrimReward + ): self.meanRewards.extend([rewards]) self.discrimLosses.extend(dLosses) self.actorLosses.extend(aLosses) self.criticLosses.extend(cLosses) self.demoAccs.extend([demoAcc]) self.agentAccs.extend([agentAcc]) + self.averageEntropys.extend([averageEntropy]) + self.discrimRewards.extend(discrimReward) def drawHis(self): def setSubFig(subFig, data, title): @@ -32,8 +38,8 @@ class GAILHistory(object): subFig.plot(range(len(data)), data, color=DarkWhite, label=title) subFig.set_title(title, color=DarkWhite) - fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots( - 3, 2, figsize=(21, 13), facecolor=DarkBlue + fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8)) = plt.subplots( + 4, 2, figsize=(21, 13), facecolor=DarkBlue ) plt.tick_params() setSubFig(ax1, self.meanRewards, "meanRewards") @@ -42,4 +48,6 @@ class GAILHistory(object): setSubFig(ax4, self.actorLosses, "actorLosses") setSubFig(ax5, self.agentAccs, "agentAccs") setSubFig(ax6, self.criticLosses, "criticLosses") + setSubFig(ax7, self.averageEntropys, "averageEntropys") + setSubFig(ax8, self.discrimRewards, "discrimRewards") plt.show() diff --git a/Aimbot-PPO-Python/HumanAction.py b/Aimbot-PPO-Python/HumanAction.py index cfc88ce..90a52bd 100644 --- a/Aimbot-PPO-Python/HumanAction.py +++ b/Aimbot-PPO-Python/HumanAction.py @@ -48,7 +48,7 @@ class HumanActions: if keyboard.is_pressed("0"): click = 1 - actions = [ws, ad, click, [xMovement]] + actions = [ws, ad, click, xMovement] mouse.move(self.screenW / 2, self.screenH / 2) return actions diff --git a/Aimbot-PPO-Python/PPO-mian.ipynb b/Aimbot-PPO-Python/PPO-mian.ipynb index 0b4344a..9371dc1 100644 --- a/Aimbot-PPO-Python/PPO-mian.ipynb +++ b/Aimbot-PPO-Python/PPO-mian.ipynb @@ -17,7 +17,8 @@ "from PPO import PPO\n", "from PPOBuffer import PPOBuffer\n", "from PPOConfig import PPOConfig\n", - "from PPOHistoryRecorder import PPOHistory" + "from PPOHistoryRecorder import PPOHistory\n", + "from IPython.display import clear_output" ] }, { @@ -45,13 +46,7 @@ "\n", "MAX_EP = 1000\n", "EP_LENGTH = 100000\n", - "GAMMA = 0.99 # discount future reward (UP?)\n", - "EPSILON = 0.2 # clip Ratio range[1-EPSILON,1+EPSILON]\n", - "ACTOR_LR = 1e-5 # LR\n", - "CRITIC_LR = 2e-5 # LR\n", "BATCH = 256 # learning step\n", - "EPOCHS = 8\n", - "ENTROPY_WHEIGHT = 0.001 # sigma's entropy in Actor loss\n", "ACTION_INTERVAL = 1 # take action every ACTION_INTERVAL steps\n", "\n", "\n", @@ -61,7 +56,18 @@ "\n", "CTN_ACTION_RANGE = 10\n", "\n", - "ppoConfig = PPOConfig()" + "ppoConfig = PPOConfig(\n", + " NNShape=[512, 512],\n", + " actorLR=2e-3,\n", + " criticLR=2e-3,\n", + " gamma=0.99,\n", + " lmbda=0.95,\n", + " clipRange=0.20,\n", + " entropyWeight=1e-2,\n", + " trainEpochs=5,\n", + " saveDir=\"PPO-Model/\" + datetime.datetime.now().strftime(\"%m%d-%H%M\") + \"/\",\n", + " loadModelDir=None,\n", + ")\n" ] }, { @@ -74,19 +80,88 @@ "output_type": "stream", "text": [ "√√√√√Enviroment Initialized Success√√√√√\n", - "√√√√√Buffer Initialized Success√√√√√\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'numpy' has no attribute 'aa'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_40408/576030716.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mACTSPEC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mACTION_SPEC\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloadDir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetSteps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maa\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m agent = PPO(\n", - "\u001b[1;32mc:\\Users\\UCUNI\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\numpy\\__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(attr)\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTester\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 315\u001b[1;33m raise AttributeError(\"module {!r} has no attribute \"\n\u001b[0m\u001b[0;32m 316\u001b[0m \"{!r}\".format(__name__, attr))\n\u001b[0;32m 317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'aa'" + "√√√√√Buffer Initialized Success√√√√√\n", + "---------thisPPO Params---------\n", + "self.stateSize = 31\n", + "self.disActShape = [3, 3, 2]\n", + "self.disActSize 3\n", + "self.disOutputSize 8\n", + "self.conActSize = 1\n", + "self.conActRange = 10\n", + "self.conOutputSize = 2\n", + "---------thisPPO config---------\n", + "self.NNShape = [512, 512, 256]\n", + "self.criticLR = 0.002\n", + "self.actorLR = 0.002\n", + "self.gamma = 0.99\n", + "self.lmbda = 0.95\n", + "self.clipRange = 0.2\n", + "self.entropyWeight = 0.01\n", + "self.trainEpochs = 5\n", + "self.saveDir = GAIL-Model/1023-2324/\n", + "self.loadModelDir = None\n", + "---------Actor Model Create Success---------\n", + "Model: \"model_1\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " stateInput (InputLayer) [(None, 31)] 0 [] \n", + " \n", + " dense0 (Dense) (None, 512) 16384 ['stateInput[0][0]'] \n", + " \n", + " dense1 (Dense) (None, 512) 262656 ['dense0[0][0]'] \n", + " \n", + " dense2 (Dense) (None, 256) 131328 ['dense1[0][0]'] \n", + " \n", + " muOut (Dense) (None, 1) 257 ['dense2[0][0]'] \n", + " \n", + " sigmaOut (Dense) (None, 1) 257 ['dense2[0][0]'] \n", + " \n", + " disAct0 (Dense) (None, 3) 771 ['dense2[0][0]'] \n", + " \n", + " disAct1 (Dense) (None, 3) 771 ['dense2[0][0]'] \n", + " \n", + " disAct2 (Dense) (None, 2) 514 ['dense2[0][0]'] \n", + " \n", + " tf.math.multiply (TFOpLambda) (None, 1) 0 ['muOut[0][0]'] \n", + " \n", + " tf.math.add (TFOpLambda) (None, 1) 0 ['sigmaOut[0][0]'] \n", + " \n", + " totalOut (Concatenate) (None, 10) 0 ['disAct0[0][0]', \n", + " 'disAct1[0][0]', \n", + " 'disAct2[0][0]', \n", + " 'tf.math.multiply[0][0]', \n", + " 'tf.math.add[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 412,938\n", + "Trainable params: 412,938\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "---------Critic Model Create Success---------\n", + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " stateInput (InputLayer) [(None, 31)] 0 \n", + " \n", + " dense0 (Dense) (None, 512) 16384 \n", + " \n", + " dense1 (Dense) (None, 512) 262656 \n", + " \n", + " dense2 (Dense) (None, 256) 131328 \n", + " \n", + " dense (Dense) (None, 1) 257 \n", + " \n", + "=================================================================\n", + "Total params: 410,625\n", + "Trainable params: 410,625\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "No loadDir specified,Create a New Model\n", + "CONTINUOUS_SIZE 1\n", + "DISCRETE_SIZE 3\n", + "STATE_SIZE 31\n" ] } ], @@ -102,7 +177,6 @@ "CONTINUOUS_SIZE = env.CONTINUOUS_SIZE\n", "ACTSPEC = env.ACTION_SPEC\n", "_, _, _, loadDir, _ = env.getSteps()\n", - "np.aa\n", "\n", "agent = PPO(\n", " stateSize=STATE_SIZE,\n", @@ -142,938 +216,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "EP 0 START\n", - "[0, 1, 0, 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"c:\\Users\\UCUNI\\OneDrive\\Unity\\ML-Agents\\Aimbot-PPO\\Aimbot-PPO-Python\\PPOBuffer.py:27: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return self.standDims(np.asarray(self.actions))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 0, array([10.])]\n", - "[0, 1, 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1100,7 +254,6 @@ " step += 1\n", "\n", " actions, predictResult = agent.chooseAction(s)\n", - " print(actions)\n", " avrEntropy, _, _ = agent.getAverageEntropy(predictResult)\n", " nextState, thisReward, done, _, saveNow = env.step(actions=actions)\n", "\n", @@ -1129,15 +282,16 @@ " rewards=ppoBuffer.getRewards(),\n", " dones=ppoBuffer.getDones(),\n", " nextState=nextState,\n", - " epochs=EPOCHS,\n", " )\n", + " clear_output()\n", " ppoBuffer.clearBuffer()\n", " ppoHistory.saveHis(epTotalReward, np.mean(entropys), actorLosses, criticLosses)\n", - "\n", - " if epTotalReward > maxTotalReward and epTotalReward != 0:\n", - " maxTotalReward = epTotalReward\n", - " agent.saveWeights(epTotalReward)\n", - " print(\"New Record! Save NN\", epTotalReward)\n" + " ppoHistory.drawHis()\n", + " if epTotalReward > maxTotalReward and epTotalReward != 0:\n", + " maxTotalReward = epTotalReward\n", + " agent.saveWeights(epTotalReward)\n", + " print(\"New Record! Save NN\", epTotalReward)\n", + " epTotalReward = 0\n" ] } ], diff --git a/Aimbot-PPO-Python/PPO.py b/Aimbot-PPO-Python/PPO.py index 0360beb..32d48a6 100644 --- a/Aimbot-PPO-Python/PPO.py +++ b/Aimbot-PPO-Python/PPO.py @@ -1,3 +1,4 @@ +from os import mkdir import tensorflow as tf from tensorflow.python.ops.numpy_ops import ndarray import tensorflow_probability as tfp @@ -295,7 +296,7 @@ class PPO(object): # y_pred: [[disActProb..., mu, sigma...]] totalALoss = 0 totalActionNum = 0 - advantage = tf.expand_dims(y_true[:, -1], axis=1) + advantage = y_true[:, -1] if self.disActSize != 0: # while NN have discrete action output. @@ -327,15 +328,17 @@ class PPO(object): conActions = y_true[ :, self.disOutputSize - + self.conActSize : self.disOutputSize + self.conActSize + + self.disOutputSize : self.disOutputSize + + self.conActSize + + self.disOutputSize + self.conActSize, ] nowConMusigs = y_pred[:, self.disOutputSize :] # [musig1,musig2] lastConAct = 0 for conAct in range(self.conActSize): thisNowConMusig = nowConMusigs[:, lastConAct : lastConAct + self.muSigSize] - thisOldConProb = oldConProbs[:, conAct : conAct + 1] + thisOldConProb = tf.squeeze(oldConProbs[:, conAct : conAct + 1]) thisConAction = conActions[:, conAct] continuousAloss = getContinuousALoss( thisNowConMusig, thisConAction, thisOldConProb, advantage @@ -343,8 +346,8 @@ class PPO(object): totalALoss += continuousAloss totalActionNum += 1.0 lastConAct += self.muSigSize - # loss = tf.divide(totalALoss, totalActionNum) - return totalALoss + loss = tf.divide(totalALoss, totalActionNum) + return loss return loss @@ -432,7 +435,7 @@ class PPO(object): criticLoss = self.trainCritic(states, discountedR, epochs) actorLoss = self.trainActor(states, oldActorResult, actions, advantage, epochs) # print("A_Loss:", actorLoss, "C_Loss:", criticLoss) - return criticLoss, actorLoss + return actorLoss, criticLoss def trainCritic(self, states: ndarray, discountedR: ndarray, epochs: int = None): """critic NN trainning function @@ -533,7 +536,11 @@ class PPO(object): score_dir = ( self.saveDir + datetime.datetime.now().strftime("%H%M%S") + "/" + str(round(score)) ) - scorefile = open(score_dir, "w") + try: + scorefile = open(score_dir, "w") + except FileNotFoundError: + mkdir(self.saveDir + datetime.datetime.now().strftime("%H%M%S") + "/") + scorefile = open(score_dir, "w") scorefile.close() print("PPO Model's Weights Saved") diff --git a/Aimbot-PPO-Python/PPOHistoryRecorder.py b/Aimbot-PPO-Python/PPOHistoryRecorder.py index f75c446..a561a97 100644 --- a/Aimbot-PPO-Python/PPOHistoryRecorder.py +++ b/Aimbot-PPO-Python/PPOHistoryRecorder.py @@ -1,7 +1,10 @@ -from turtle import color import matplotlib.pyplot as plt +darkBlue = "#011627" +darkWhite = "#c9d2df" + + class PPOHistory(object): def __init__(self): self.meanRewards = [] @@ -16,43 +19,19 @@ class PPOHistory(object): self.criticLosses.extend(cLosses) def drawHis(self): - plt.figure(figsize=(21, 13), facecolor="#011627") - ax = plt.subplot(2, 2, 1) - ax.set_facecolor("#011627") - ax.spines["top"].set_color("#c9d2df") - ax.spines["bottom"].set_color("#c9d2df") - ax.spines["left"].set_color("#c9d2df") - ax.spines["right"].set_color("#c9d2df") - ax.plot( - range(len(self.meanRewards)), self.meanRewards, color="#c9d2df", label="AverageRewards" - ) - ax.set_title("meanRewards", color="#c9d2df") - ax = plt.subplot(2, 2, 2) - ax.set_facecolor("#011627") - ax.spines["top"].set_color("#c9d2df") - ax.spines["bottom"].set_color("#c9d2df") - ax.spines["left"].set_color("#c9d2df") - ax.spines["right"].set_color("#c9d2df") - ax.plot(range(len(self.entropys)), self.entropys, color="#c9d2df", label="AverageEntropys") - ax.set_title("entropys", color="#c9d2df") - ax = plt.subplot(2, 2, 3) - ax.set_facecolor("#011627") - ax.spines["top"].set_color("#c9d2df") - ax.spines["bottom"].set_color("#c9d2df") - ax.spines["left"].set_color("#c9d2df") - ax.spines["right"].set_color("#c9d2df") - ax.plot( - range(len(self.actorLosses)), self.actorLosses, color="#c9d2df", label="actorLosses" - ) - ax.set_title("actorLosses", color="#c9d2df") - ax = plt.subplot(2, 2, 4) - ax.set_facecolor("#011627") - ax.spines["top"].set_color("#c9d2df") - ax.spines["bottom"].set_color("#c9d2df") - ax.spines["left"].set_color("#c9d2df") - ax.spines["right"].set_color("#c9d2df") - ax.plot( - range(len(self.criticLosses)), self.criticLosses, color="#c9d2df", label="criticLosses" - ) - ax.set_title("criticLosses", color="#c9d2df") + def setSubFig(subFig, data, title): + subFig.set_facecolor(darkBlue) + subFig.tick_params(colors=darkWhite) + subFig.spines["top"].set_color(darkWhite) + subFig.spines["bottom"].set_color(darkWhite) + subFig.spines["left"].set_color(darkWhite) + subFig.spines["right"].set_color(darkWhite) + subFig.plot(range(len(data)), data, color=darkWhite, label=title) + subFig.set_title(title, color=darkWhite) + + fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(21, 13), facecolor=darkBlue) + setSubFig(ax1, self.meanRewards, "meanRewards") + setSubFig(ax2, self.entropys, "entropys") + setSubFig(ax3, self.actorLosses, "actorLosses") + setSubFig(ax4, self.criticLosses, "criticLosses") plt.show() diff --git a/Aimbot-PPO-Python/testarea.ipynb b/Aimbot-PPO-Python/testarea.ipynb index aa4369e..55a8f7c 100644 --- a/Aimbot-PPO-Python/testarea.ipynb +++ b/Aimbot-PPO-Python/testarea.ipynb @@ -579,30 +579,18 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 2. 3. 1. 2. 1.]\n", - " [2. 2. 3. 2. 2. 1.]]\n", - "tf.Tensor(\n", - "[[1 2 3 1 2 1]\n", - " [2 2 3 2 2 1]], shape=(2, 6), dtype=int32)\n" - ] - } - ], + "outputs": [], "source": [ - "from matplotlib.pyplot import axis\n", - "import tensorflow as tf\n", - "import numpy as np\n", + "from os import mkdir\n", "\n", - "aa = np.array([[1,2,3],[2,2,3]])\n", - "bb = np.array([[1,2,1.],[2,2,1.]])\n", - "print(np.append(aa,bb,axis=1))\n", - "print(tf.concat([aa,bb],axis=1))" + "\n", + "try:\n", + " open(\"thisdir/51\",\"w\")\n", + "except FileNotFoundError:\n", + " mkdir(\"thisdir/\")\n", + " open(\"thisdir/51\",\"w\")" ] } ],