Repository: inseq-team/inseq (language: python)

Issue:
Attributed behavior for contrastive step functions
## Description

This PR:
- Reorganizes logics related to contrastive step functions in the `contrast_utils` module.
- Add the `is_attributed_fn` bool flag (default: `False`) to the `StepFunctionBaseArgs` class. This flag enables custom behavior during step function execution by checking whether the function is being called as a step function or as an attribution target.
- Fix the problematic behavior of `contrast_prob_diff` as an attributed function (#188) by forcing original inputs at every step when
 performing contrastive feature attribution (matching [Yin and Neubig (2022)](https://aclanthology.org/2022.emnlp-main.14/) implementation). The previous behavior allowing for measuring contrastive probability difference in presence of different input sources is preserved when the function is used as step function.


Discussion / hints:

Fix patch:
diff --git a/docs/source/examples/quickstart.rst b/docs/source/examples/quickstart.rst
index 8a0021a8..434f6164 100644
--- a/docs/source/examples/quickstart.rst
+++ b/docs/source/examples/quickstart.rst
@@ -248,8 +248,6 @@ Inseq allows users to specify custom attribution targets using the ``attributed_
         step_scores=["contrast_prob_diff"]
     )
 
-    # Weight attribution scores by the difference in probabilities
-    out.weight_attributions("contrast_prob_diff")
     out.show()
 
 .. raw:: html
diff --git a/docs/source/html_outputs/contrastive_example.htm b/docs/source/html_outputs/contrastive_example.htm
index d18b3b59..654c26de 100644
--- a/docs/source/html_outputs/contrastive_example.htm
+++ b/docs/source/html_outputs/contrastive_example.htm
@@ -1,20 +1,20 @@
 <br/><b>0th instance:</b><br/>
 <html>
-<div id="psrnzfgvlmlxsjvmyzzi_viz_container">
-    <div id="psrnzfgvlmlxsjvmyzzi_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "psrnzfgvlmlxsjvmyzzi_saliency_plot_container" class="psrnzfgvlmlxsjvmyzzi_viz_container" style="display:block">
-
-<div id="sgnvwnjolvqedrieijng_saliency_plot" class="sgnvwnjolvqedrieijng_viz_content">
+<div id="yekuzbhxjorapnmjcsqc_viz_container">
+    <div id="yekuzbhxjorapnmjcsqc_content" style="padding:15px;border-style:solid;margin:5px;">
+        <div id = "yekuzbhxjorapnmjcsqc_saliency_plot_container" class="yekuzbhxjorapnmjcsqc_viz_container" style="display:block">
+            
+<div id="uhyfyqqgymilhcohviah_saliency_plot" class="uhyfyqqgymilhcohviah_viz_content">
     <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
         <span style="font-size: 20px;">Source Saliency Heatmap</span>
         <br>
         x: Generated tokens, y: Attributed tokens
     </div>
-
+    
 <table border="1" cellpadding="5" cellspacing="5"
     style="overflow-x:scroll;display:block;">
     <tr><th></th>
-<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁I</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.1171321053673995)">0.079</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.001</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.002</th></tr><tr><th>▁said</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.14866310160427795)">0.098</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.002</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.004</th></tr><tr><th>▁hi</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.18019409784115661)">0.117</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.002</th><th style="background:rgba(30.0, 136.0, 229.0, 0.006773618538324436)">-0.006</th></tr><tr><th>▁to</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.055</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.001</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.003</th></tr><tr><th>▁the</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.20384234501881549)">0.136</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.002</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.005</th></tr><tr><th>▁manager</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.647</th><th style="background:rgba(255.0, 13.0, 87.0, 0.014656367597544035)">0.011</th><th style="background:rgba(30.0, 136.0, 229.0, 0.022539116656763607)">-0.019</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.219607843137255)">0.142</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.002</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.005</th></tr><tr style="outline: thin solid"><th><b>example_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>
+<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁I</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)">0.05</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁said</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)">0.063</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁hi</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.17231134878193693)">0.074</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁to</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.034</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁the</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.1959595959595959)">0.086</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁manager</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.429</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.2117250940780353)">0.092</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr style="outline: thin solid"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>
 </div>
 
         </div>
@@ -23,21 +23,21 @@
 </html>
 <br/><b>0th instance:</b><br/>
 <html>
-<div id="uxbgcpboccspiyypbkpg_viz_container">
-    <div id="uxbgcpboccspiyypbkpg_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "uxbgcpboccspiyypbkpg_saliency_plot_container" class="uxbgcpboccspiyypbkpg_viz_container" style="display:block">
-
-<div id="xeromkjseagozludfjmn_saliency_plot" class="xeromkjseagozludfjmn_viz_content">
+<div id="lvxoiwxdxhmktffgwhye_viz_container">
+    <div id="lvxoiwxdxhmktffgwhye_content" style="padding:15px;border-style:solid;margin:5px;">
+        <div id = "lvxoiwxdxhmktffgwhye_saliency_plot_container" class="lvxoiwxdxhmktffgwhye_viz_container" style="display:block">
+            
+<div id="abnukoxnwsuuukoarngc_saliency_plot" class="abnukoxnwsuuukoarngc_viz_content">
     <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
         <span style="font-size: 20px;">Target Saliency Heatmap</span>
         <br>
         x: Generated tokens, y: Attributed tokens
     </div>
-
+    
 <table border="1" cellpadding="5" cellspacing="5"
     style="overflow-x:scroll;display:block;">
     <tr><th></th>
-<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ho</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.030421865715983164)">0.024</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.001</th><th style="background:rgba(30.0, 136.0, 229.0, 0.014656367597544028)">-0.015</th></tr><tr><th>▁saluta</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.13289760348583876)">0.09</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.005</th><th style="background:rgba(30.0, 136.0, 229.0, 0.03042186571598325)">-0.023</th></tr><tr><th>to</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)">0.039</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.001</th><th style="background:rgba(30.0, 136.0, 229.0, 0.006773618538324436)">-0.009</th></tr><tr><th>▁il</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.002</th><th style="background:rgba(30.0, 136.0, 229.0, 0.006773618538324436)">-0.007</th></tr><tr><th>▁manager</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(30.0, 136.0, 229.0, 0.03042186571598325)">-0.022</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th></tr><tr style="outline: thin solid"><th><b>example_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>
+<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ho</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)">0.025</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁saluta</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(255.0, 13.0, 87.0, 0.23537334125569415)">0.103</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>to</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.09348385818974037)">0.043</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁la → ▁il</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>▁manager</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th><th style="background:rgba(0.0, 0.0, 0.0, 0.0)"></th></tr><tr style="outline: thin solid"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>
 </div>
 
         </div>
diff --git a/docs/source/images/cat_example_plot.png b/docs/source/images/cat_example_plot.png
index e7abcba3..2e35dfe4 100644
Binary files a/docs/source/images/cat_example_plot.png and b/docs/source/images/cat_example_plot.png differ
diff --git a/examples/inseq_tutorial.ipynb b/examples/inseq_tutorial.ipynb
index cce02a11..62c86e34 100644
--- a/examples/inseq_tutorial.ipynb
+++ b/examples/inseq_tutorial.ipynb
@@ -1009,16 +1009,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Attributing with saliency...: 100%|██████████| 7/7 [00:00<00:00, 13.78it/s]\n",
-      "/home/gsarti/projects/inseq/inseq/data/attribution.py:304: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)\n",
-      "  Args:\n"
+      "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/models/marian/tokenization_marian.py:197: UserWarning: Recommended: pip install sacremoses.\n",
+      "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
+      "Provided alignments do not cover all 6 tokens from the original sequence.\n",
+      "Filling missing position with right-aligned 1:1 position alignments.\n",
+      "Generated alignments: [(1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6)]\n",
+      "Attributing with saliency...:  14%|█▍        | 1/7 [00:00<?, ?it/s]/home/gsarti/projects/inseq/inseq/utils/contrast_utils.py:146: UserWarning: Contrastive inputs do not match original inputs when using a contrastive attributed function.\n",
+      "By default we force the original inputs to be used (i.e. only the contrastive predicted target is different).\n",
+      "This is a requirement for gradient-based attribution method, as contrastive inputs don't participate in gradient computation.\n",
+      "For attribution methods with less stringent requirements, set --contrast_force_inputs to True to use the contrastive inputs for attribution instead.\n",
+      "  warnings.warn(\n",
+      "Attributing with saliency...: 100%|██████████| 7/7 [00:01<00:00,  5.47it/s]\n"
      ]
     },
     {
@@ -1026,11 +1034,11 @@
       "text/html": [
        "<br/><b>0th instance:</b><br/>\n",
        "<html>\n",
-       "<div id=\"esejzqorvufaigfywirk_viz_container\">\n",
-       "    <div id=\"esejzqorvufaigfywirk_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
-       "        <div id = \"esejzqorvufaigfywirk_saliency_plot_container\" class=\"esejzqorvufaigfywirk_viz_container\" style=\"display:block\">\n",
+       "<div id=\"nbgnihlfpglbahxuceyn_viz_container\">\n",
+       "    <div id=\"nbgnihlfpglbahxuceyn_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
+       "        <div id = \"nbgnihlfpglbahxuceyn_saliency_plot_container\" class=\"nbgnihlfpglbahxuceyn_viz_container\" style=\"display:block\">\n",
        "            \n",
-       "<div id=\"vqwpsbfhcgchqjjfgyqp_saliency_plot\" class=\"vqwpsbfhcgchqjjfgyqp_viz_content\">\n",
+       "<div id=\"gucgkbmmgyntzexkdptp_saliency_plot\" class=\"gucgkbmmgyntzexkdptp_viz_content\">\n",
        "    <div style=\"margin:5px;font-family:sans-serif;font-weight:bold;\">\n",
        "        <span style=\"font-size: 20px;\">Source Saliency Heatmap</span>\n",
        "        <br>\n",
@@ -1040,7 +1048,7 @@
        "<table border=\"1\" cellpadding=\"5\" cellspacing=\"5\"\n",
        "    style=\"overflow-x:scroll;display:block;\">\n",
        "    <tr><th></th>\n",
-       "<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁I</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)\">0.038</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.001</th></tr><tr><th>▁said</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.048</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.001</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.001</th></tr><tr><th>▁hi</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.17231134878193693)\">0.056</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.001</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.002</th></tr><tr><th>▁to</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)\">0.026</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.001</th></tr><tr><th>▁the</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.1959595959595959)\">0.066</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.001</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.002</th></tr><tr><th>▁manager</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 1.0)\">0.325</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.00677361853832443)\">0.005</th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.014656367597544028)\">-0.007</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.2117250940780353)\">0.07</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.001</th><th style=\"background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)\">-0.002</th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>\n",
+       "<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁I</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)\">0.05</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁said</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.063</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁hi</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.17231134878193693)\">0.074</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁to</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)\">0.034</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁the</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.1959595959595959)\">0.086</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁manager</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 1.0)\">0.429</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.2117250940780353)\">0.092</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>\n",
        "</div>\n",
        "\n",
        "        </div>\n",
@@ -1049,11 +1057,11 @@
        "</html>\n",
        "<br/><b>0th instance:</b><br/>\n",
        "<html>\n",
-       "<div id=\"znzwasrngqcllywgnexw_viz_container\">\n",
-       "    <div id=\"znzwasrngqcllywgnexw_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
-       "        <div id = \"znzwasrngqcllywgnexw_saliency_plot_container\" class=\"znzwasrngqcllywgnexw_viz_container\" style=\"display:block\">\n",
+       "<div id=\"zsbzaqiolhfkihkdwrsu_viz_container\">\n",
+       "    <div id=\"zsbzaqiolhfkihkdwrsu_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
+       "        <div id = \"zsbzaqiolhfkihkdwrsu_saliency_plot_container\" class=\"zsbzaqiolhfkihkdwrsu_viz_container\" style=\"display:block\">\n",
        "            \n",
-       "<div id=\"xlggogpxkfzncbcqukbj_saliency_plot\" class=\"xlggogpxkfzncbcqukbj_viz_content\">\n",
+       "<div id=\"bvsrvprksyshxijddyvc_saliency_plot\" class=\"bvsrvprksyshxijddyvc_viz_content\">\n",
        "    <div style=\"margin:5px;font-family:sans-serif;font-weight:bold;\">\n",
        "        <span style=\"font-size: 20px;\">Target Saliency Heatmap</span>\n",
        "        <br>\n",
@@ -1063,7 +1071,7 @@
        "<table border=\"1\" cellpadding=\"5\" cellspacing=\"5\"\n",
        "    style=\"overflow-x:scroll;display:block;\">\n",
        "    <tr><th></th>\n",
-       "<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ho</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.030421865715983164)\">0.012</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.014656367597544028)\">-0.005</th></tr><tr><th>▁saluta</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.047</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.002</th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.022539116656763607)\">-0.008</th></tr><tr><th>to</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)\">0.019</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.006773618538324436)\">-0.003</th></tr><tr><th>▁la → ▁il</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.001</th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.006773618538324436)\">-0.003</th></tr><tr><th>▁manager</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(30.0, 136.0, 229.0, 0.022539116656763607)\">-0.008</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>\n",
+       "<th>▁Ho</th><th>▁saluta</th><th>to</th><th>▁la → ▁il</th><th>▁manager</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ho</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)\">0.025</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁saluta</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.23537334125569415)\">0.103</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>to</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.09348385818974037)\">0.043</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁la → ▁il</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>▁manager</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)\">0.0</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th><th style=\"background:rgba(0.0, 0.0, 0.0, 0.0)\"></th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.0</th><th>0.0</th><th>0.0</th><th><b>0.707</b></th><th>0.013</th><th>-0.043</th></table>\n",
        "</div>\n",
        "\n",
        "        </div>\n",
@@ -1098,8 +1106,6 @@
     "    step_scores=[\"contrast_prob_diff\"],\n",
     ")\n",
     "\n",
-    "# Weight attribution scores by the difference in probabilities\n",
-    "out.weight_attributions(\"contrast_prob_diff\")\n",
     "out.show()"
    ]
   },
@@ -1250,16 +1256,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
+      "/home/gsarti/.cache/pypoetry/virtualenvs/inseq-PzwjmCYf-py3.9/lib/python3.9/site-packages/transformers/models/marian/tokenization_marian.py:197: UserWarning: Recommended: pip install sacremoses.\n",
       "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
-      "Attributing with saliency...: 100%|██████████| 8/8 [00:01<00:00,  5.91it/s]\n"
+      "Provided alignments do not cover all 7 tokens from the original sequence.\n",
+      "Filling missing position with right-aligned 1:1 position alignments.\n",
+      "\n",
+      "Generated alignments: [(1, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 8), (7, 9)]\n",
+      "Attributing with saliency...:  12%|█▎        | 1/8 [00:00<?, ?it/s]/home/gsarti/projects/inseq/inseq/utils/contrast_utils.py:139: UserWarning: Forcing contrastive inputs to be used for attribution. This may result in unexpected behavior for gradient-based attribution methods.\n",
+      "  warnings.warn(\n",
+      "Attributing with saliency...: 100%|██████████| 8/8 [00:01<00:00,  6.72it/s]\n"
      ]
     },
     {
@@ -1267,11 +1279,11 @@
       "text/html": [
        "<br/><b>0th instance:</b><br/>\n",
        "<html>\n",
-       "<div id=\"esejzqorvufaigfywirk_viz_container\">\n",
-       "    <div id=\"esejzqorvufaigfywirk_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
-       "        <div id = \"esejzqorvufaigfywirk_saliency_plot_container\" class=\"esejzqorvufaigfywirk_viz_container\" style=\"display:block\">\n",
+       "<div id=\"prrqyfukqoxylyyivhgz_viz_container\">\n",
+       "    <div id=\"prrqyfukqoxylyyivhgz_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
+       "        <div id = \"prrqyfukqoxylyyivhgz_saliency_plot_container\" class=\"prrqyfukqoxylyyivhgz_viz_container\" style=\"display:block\">\n",
        "            \n",
-       "<div id=\"vqwpsbfhcgchqjjfgyqp_saliency_plot\" class=\"vqwpsbfhcgchqjjfgyqp_viz_content\">\n",
+       "<div id=\"huutevpwmwhhmazvksix_saliency_plot\" class=\"huutevpwmwhhmazvksix_viz_content\">\n",
        "    <div style=\"margin:5px;font-family:sans-serif;font-weight:bold;\">\n",
        "        <span style=\"font-size: 20px;\">Source Saliency Heatmap</span>\n",
        "        <br>\n",
@@ -1281,7 +1293,7 @@
        "<table border=\"1\" cellpadding=\"5\" cellspacing=\"5\"\n",
        "    style=\"overflow-x:scroll;display:block;\">\n",
        "    <tr><th></th>\n",
-       "<th>▁Le → ▁Les</th><th>▁forces → ▁soldats</th><th>▁militaires → ▁de</th><th>▁de → ▁la</th><th>▁paix</th><th>▁des → ▁ONU</th><th>▁Nations → &lt;/s&gt;</th></tr><tr><th>▁UN</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.09</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.08560110913052081)\">0.057</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)\">0.07</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.12501485442661908)\">0.081</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.1171321053673995)\">0.075</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4009110714993067)\">0.253</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.3772628243216479)\">0.241</th></tr><tr><th>▁peacekeepers</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.8975242622301447)\">0.568</th><th style=\"background:rgba(255.0, 13.0, 87.0, 1.0)\">0.632</th><th style=\"background:rgba(255.0, 13.0, 87.0, 1.0)\">0.629</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.976351752822341)\">0.615</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.9842345018815607)\">0.618</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.7635175282234107)\">0.484</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.7477520301049713)\">0.47</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.5428005545652606)\">0.342</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.48762131115072294)\">0.311</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4718558130322837)\">0.301</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)\">0.304</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.48762131115072294)\">0.307</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.41667656961774624)\">0.263</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4560903149138443)\">0.289</th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.031</th><th>0.138</th><th><b>0.886</b></th><th>0.156</th><th><b>0.954</b></th><th>-0.434</th><th>-0.039</th></table>\n",
+       "<th>▁militaires → ▁Les</th><th>▁de → ▁soldats</th><th>▁paix → ▁de</th><th>▁des → ▁la</th><th>▁Nations → ▁paix</th><th>▁Unies → ▁ONU</th><th>&lt;/s&gt;</th></tr><tr><th>▁UN</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.09</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)\">0.07</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)\">0.069</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4087938205585263)\">0.258</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.2826698356110118)\">0.18</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4087938205585263)\">0.258</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.14078035254505847)\">0.092</th></tr><tr><th>▁peacekeepers</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.9290552584670234)\">0.58</th><th style=\"background:rgba(255.0, 13.0, 87.0, 1.0)\">0.628</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.976351752822341)\">0.611</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.7714002772826303)\">0.486</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.8265795206971679)\">0.517</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.7162210338680925)\">0.447</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.9369380075262429)\">0.589</th></tr><tr><th>&lt;/s&gt;</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.5270350564468211)\">0.33</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)\">0.302</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.5112695583283818)\">0.319</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4087938205585263)\">0.256</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)\">0.303</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.4718558130322837)\">0.295</th><th style=\"background:rgba(255.0, 13.0, 87.0, 0.5112695583283818)\">0.319</th></tr><tr style=\"outline: thin solid\"><th><b>contrast_prob_diff</b></th><th>0.042</th><th><b>-0.607</b></th><th><b>0.865</b></th><th>0.467</th><th>0.056</th><th><b>-0.691</b></th><th>0.015</th></table>\n",
        "</div>\n",
        "\n",
        "        </div>\n",
@@ -1312,6 +1324,7 @@
     "    attributed_fn=\"contrast_prob_diff\",\n",
     "    step_scores=[\"contrast_prob_diff\"],\n",
     "    contrast_targets=pe_target,\n",
+    "    contrast_force_inputs=True,\n",
     ")\n",
     "\n",
     "# Unreasonable alignments\n",
@@ -1328,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1355,7 +1368,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Attributing with saliency...: 100%|██████████| 8/8 [00:00<00:00, 14.78it/s]\n"
+      "Attributing with saliency...: 100%|██████████| 8/8 [00:00<00:00, 17.28it/s]\n"
      ]
     },
     {
@@ -1363,11 +1376,11 @@
       "text/html": [
        "<br/><b>0th instance:</b><br/>\n",
        "<html>\n",
-       "<div id=\"esejzqorvufaigfywirk_viz_container\">\n",
-       "    <div id=\"esejzqorvufaigfywirk_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
-       "        <div id = \"esejzqorvufaigfywirk_saliency_plot_container\" class=\"esejzqorvufaigfywirk_viz_container\" style=\"display:block\">\n",
+       "<div id=\"nixcouauibqrnieidqih_viz_container\">\n",
+       "    <div id=\"nixcouauibqrnieidqih_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
+       "        <div id = \"nixcouauibqrnieidqih_saliency_plot_container\" class=\"nixcouauibqrnieidqih_viz_container\" style=\"display:block\">\n",
        "            \n",
-       "<div id=\"vqwpsbfhcgchqjjfgyqp_saliency_plot\" class=\"vqwpsbfhcgchqjjfgyqp_viz_content\">\n",
+       "<div id=\"krvckabfknkurranovia_saliency_plot\" class=\"krvckabfknkurranovia_viz_content\">\n",
        "    <div style=\"margin:5px;font-family:sans-serif;font-weight:bold;\">\n",
        "        <span style=\"font-size: 20px;\">Source Saliency Heatmap</span>\n",
        "        <br>\n",
@@ -1401,6 +1414,7 @@
     "    step_scores=[\"contrast_prob_diff\"],\n",
     "    contrast_targets=pe_target,\n",
     "    contrast_targets_alignments=[(0, 0), (1, 1), (2, 2), (3, 4), (4, 4), (5, 5), (6, 7), (7, 9)],\n",
+    "    contrast_force_inputs=True,\n",
     ")\n",
     "\n",
     "# Reasonable alignments\n",
@@ -1425,11 +1439,12 @@
      "output_type": "stream",
      "text": [
       "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
-      "Provided alignments do not cover all 8 tokens from the original sequence.\n",
-      "Filling missing position with 1:1 position alignments.\n",
       "Using sentence-transformers/LaBSE for automatic alignments. Provide custom alignments for non-linguistic sequences, or for languages not covered by the aligner.\n",
-      "Generated alignments: [(0, 0), (1, 1), (2, 2), (3, 4), (4, 4), (5, 5), (6, 7), (7, 9)]\n",
-      "Attributing with saliency...: 100%|██████████| 8/8 [00:00<00:00, 11.73it/s]\n"
+      "\n",
+      "Generated alignments: [(1, 1), (2, 2), (3, 4), (4, 4), (5, 5), (6, 7), (7, 9)]\n",
+      "Attributing with saliency...:  12%|█▎        | 1/8 [00:00<?, ?it/s]/home/gsarti/projects/inseq/inseq/utils/contrast_utils.py:139: UserWarning: Forcing contrastive inputs to be used for attribution. This may result in unexpected behavior for gradient-based attribution methods.\n",
+      "  warnings.warn(\n",
+      "Attributing with saliency...: 100%|██████████| 8/8 [00:00<00:00, 18.30it/s]\n"
      ]
     },
     {
@@ -1437,11 +1452,11 @@
       "text/html": [
        "<br/><b>0th instance:</b><br/>\n",
        "<html>\n",
-       "<div id=\"esejzqorvufaigfywirk_viz_container\">\n",
-       "    <div id=\"esejzqorvufaigfywirk_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
-       "        <div id = \"esejzqorvufaigfywirk_saliency_plot_container\" class=\"esejzqorvufaigfywirk_viz_container\" style=\"display:block\">\n",
+       "<div id=\"jjfpamiiwviksfeppboj_viz_container\">\n",
+       "    <div id=\"jjfpamiiwviksfeppboj_content\" style=\"padding:15px;border-style:solid;margin:5px;\">\n",
+       "        <div id = \"jjfpamiiwviksfeppboj_saliency_plot_container\" class=\"jjfpamiiwviksfeppboj_viz_container\" style=\"display:block\">\n",
        "            \n",
-       "<div id=\"vqwpsbfhcgchqjjfgyqp_saliency_plot\" class=\"vqwpsbfhcgchqjjfgyqp_viz_content\">\n",
+       "<div id=\"vkzncbkmyeiclzuilwco_saliency_plot\" class=\"vkzncbkmyeiclzuilwco_viz_content\">\n",
        "    <div style=\"margin:5px;font-family:sans-serif;font-weight:bold;\">\n",
        "        <span style=\"font-size: 20px;\">Source Saliency Heatmap</span>\n",
        "        <br>\n",
@@ -1475,6 +1490,7 @@
     "    step_scores=[\"contrast_prob_diff\"],\n",
     "    contrast_targets=pe_target,\n",
     "    contrast_targets_alignments=\"auto\",\n",
+    "    contrast_force_inputs=True,\n",
     ")\n",
     "\n",
     "# Reasonable alignments produced automatically\n",
@@ -1510,7 +1526,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "from transformers import AutoModelForCausalLM\n",
     "\n",
     "import inseq\n",
     "\n",
@@ -1518,7 +1534,6 @@
     "# This will make the model much smaller for inference purposes, but attributions are not guaranteed to match those\n",
     "# of the full-precision model.\n",
     "model = AutoModelForCausalLM.from_pretrained(\"gpt2-xl\", load_in_8bit=True, device_map=\"auto\")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2-xl\")\n",
     "\n",
     "# Important: adding a space after the prompt would cause problems since the final space would be tokenized separately\n",
     "prompt = \"The Eiffel Tower is located in the city of\"\n",
@@ -1558,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1607,267 +1622,267 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>The</th>\n",
-       "      <td>0.114970</td>\n",
-       "      <td>0.112049</td>\n",
-       "      <td>0.097279</td>\n",
-       "      <td>0.098705</td>\n",
-       "      <td>0.103262</td>\n",
-       "      <td>0.081201</td>\n",
-       "      <td>0.064696</td>\n",
-       "      <td>0.039593</td>\n",
-       "      <td>0.023691</td>\n",
-       "      <td>0.014623</td>\n",
+       "      <th>(0, The)</th>\n",
+       "      <td>0.070843</td>\n",
+       "      <td>0.114042</td>\n",
+       "      <td>0.097879</td>\n",
+       "      <td>0.095965</td>\n",
+       "      <td>0.091416</td>\n",
+       "      <td>0.083317</td>\n",
+       "      <td>0.066999</td>\n",
+       "      <td>0.065708</td>\n",
+       "      <td>0.046398</td>\n",
+       "      <td>0.042155</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001963</td>\n",
-       "      <td>0.002564</td>\n",
-       "      <td>0.004040</td>\n",
-       "      <td>0.003287</td>\n",
-       "      <td>0.003187</td>\n",
-       "      <td>0.003994</td>\n",
-       "      <td>0.008052</td>\n",
-       "      <td>0.010903</td>\n",
-       "      <td>0.031432</td>\n",
+       "      <td>0.009971</td>\n",
+       "      <td>0.010152</td>\n",
+       "      <td>0.014151</td>\n",
+       "      <td>0.026439</td>\n",
+       "      <td>0.044600</td>\n",
+       "      <td>0.054960</td>\n",
+       "      <td>0.066599</td>\n",
+       "      <td>0.079636</td>\n",
+       "      <td>0.057506</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ĠE</th>\n",
-       "      <td>0.084764</td>\n",
-       "      <td>0.070963</td>\n",
-       "      <td>0.071029</td>\n",
-       "      <td>0.065764</td>\n",
-       "      <td>0.057698</td>\n",
-       "      <td>0.059411</td>\n",
-       "      <td>0.056259</td>\n",
-       "      <td>0.043724</td>\n",
-       "      <td>0.048695</td>\n",
-       "      <td>0.045607</td>\n",
+       "      <th>(1, ĠE)</th>\n",
+       "      <td>0.082103</td>\n",
+       "      <td>0.070663</td>\n",
+       "      <td>0.057518</td>\n",
+       "      <td>0.061961</td>\n",
+       "      <td>0.050641</td>\n",
+       "      <td>0.048189</td>\n",
+       "      <td>0.044730</td>\n",
+       "      <td>0.036794</td>\n",
+       "      <td>0.039718</td>\n",
+       "      <td>0.035112</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.024910</td>\n",
-       "      <td>0.025512</td>\n",
-       "      <td>0.022277</td>\n",
-       "      <td>0.022929</td>\n",
-       "      <td>0.030234</td>\n",
-       "      <td>0.029892</td>\n",
-       "      <td>0.008052</td>\n",
-       "      <td>0.011526</td>\n",
-       "      <td>0.008484</td>\n",
+       "      <td>0.039271</td>\n",
+       "      <td>0.027281</td>\n",
+       "      <td>0.024855</td>\n",
+       "      <td>0.029273</td>\n",
+       "      <td>0.025610</td>\n",
+       "      <td>0.022501</td>\n",
+       "      <td>0.013762</td>\n",
+       "      <td>0.015216</td>\n",
+       "      <td>0.018794</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>iff</th>\n",
-       "      <td>0.160311</td>\n",
-       "      <td>0.178163</td>\n",
-       "      <td>0.205860</td>\n",
-       "      <td>0.196370</td>\n",
-       "      <td>0.189534</td>\n",
-       "      <td>0.187073</td>\n",
-       "      <td>0.193052</td>\n",
-       "      <td>0.158926</td>\n",
-       "      <td>0.142962</td>\n",
-       "      <td>0.155769</td>\n",
+       "      <th>(2, iff)</th>\n",
+       "      <td>0.140957</td>\n",
+       "      <td>0.157038</td>\n",
+       "      <td>0.182212</td>\n",
+       "      <td>0.169766</td>\n",
+       "      <td>0.180928</td>\n",
+       "      <td>0.168693</td>\n",
+       "      <td>0.185059</td>\n",
+       "      <td>0.140923</td>\n",
+       "      <td>0.134487</td>\n",
+       "      <td>0.144310</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.042697</td>\n",
-       "      <td>0.035851</td>\n",
-       "      <td>0.034897</td>\n",
-       "      <td>0.044016</td>\n",
-       "      <td>0.045351</td>\n",
-       "      <td>0.047375</td>\n",
-       "      <td>0.051627</td>\n",
-       "      <td>0.040190</td>\n",
-       "      <td>0.025707</td>\n",
+       "      <td>0.045401</td>\n",
+       "      <td>0.036838</td>\n",
+       "      <td>0.033408</td>\n",
+       "      <td>0.042922</td>\n",
+       "      <td>0.040024</td>\n",
+       "      <td>0.040202</td>\n",
+       "      <td>0.042203</td>\n",
+       "      <td>0.034024</td>\n",
+       "      <td>0.022652</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>el</th>\n",
-       "      <td>0.146465</td>\n",
-       "      <td>0.133030</td>\n",
-       "      <td>0.134302</td>\n",
-       "      <td>0.136165</td>\n",
-       "      <td>0.153691</td>\n",
-       "      <td>0.150928</td>\n",
-       "      <td>0.186720</td>\n",
-       "      <td>0.178493</td>\n",
-       "      <td>0.228001</td>\n",
-       "      <td>0.243314</td>\n",
+       "      <th>(3, el)</th>\n",
+       "      <td>0.152048</td>\n",
+       "      <td>0.153524</td>\n",
+       "      <td>0.142376</td>\n",
+       "      <td>0.142573</td>\n",
+       "      <td>0.152442</td>\n",
+       "      <td>0.164651</td>\n",
+       "      <td>0.180138</td>\n",
+       "      <td>0.175674</td>\n",
+       "      <td>0.207000</td>\n",
+       "      <td>0.235107</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.093743</td>\n",
-       "      <td>0.071472</td>\n",
-       "      <td>0.071115</td>\n",
-       "      <td>0.051587</td>\n",
-       "      <td>0.014777</td>\n",
-       "      <td>0.017563</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.078493</td>\n",
+       "      <td>0.062609</td>\n",
+       "      <td>0.064881</td>\n",
+       "      <td>0.049407</td>\n",
+       "      <td>0.029825</td>\n",
+       "      <td>0.028289</td>\n",
+       "      <td>0.019389</td>\n",
+       "      <td>0.018070</td>\n",
+       "      <td>0.012851</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ĠTower</th>\n",
-       "      <td>0.140587</td>\n",
-       "      <td>0.137475</td>\n",
-       "      <td>0.147058</td>\n",
-       "      <td>0.141195</td>\n",
-       "      <td>0.149450</td>\n",
-       "      <td>0.162261</td>\n",
-       "      <td>0.129486</td>\n",
-       "      <td>0.137138</td>\n",
-       "      <td>0.119140</td>\n",
-       "      <td>0.121860</td>\n",
+       "      <th>(4, ĠTower)</th>\n",
+       "      <td>0.163404</td>\n",
+       "      <td>0.153515</td>\n",
+       "      <td>0.166156</td>\n",
+       "      <td>0.160433</td>\n",
+       "      <td>0.165161</td>\n",
+       "      <td>0.171105</td>\n",
+       "      <td>0.141366</td>\n",
+       "      <td>0.142759</td>\n",
+       "      <td>0.129513</td>\n",
+       "      <td>0.123079</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.034061</td>\n",
-       "      <td>0.033870</td>\n",
-       "      <td>0.029359</td>\n",
-       "      <td>0.035042</td>\n",
-       "      <td>0.022648</td>\n",
-       "      <td>0.005157</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.032498</td>\n",
+       "      <td>0.032768</td>\n",
+       "      <td>0.028870</td>\n",
+       "      <td>0.029831</td>\n",
+       "      <td>0.021681</td>\n",
+       "      <td>0.020188</td>\n",
+       "      <td>0.019192</td>\n",
+       "      <td>0.016651</td>\n",
+       "      <td>0.013072</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġis</th>\n",
-       "      <td>0.034933</td>\n",
-       "      <td>0.036481</td>\n",
-       "      <td>0.031905</td>\n",
-       "      <td>0.028272</td>\n",
-       "      <td>0.032980</td>\n",
-       "      <td>0.034967</td>\n",
-       "      <td>0.032864</td>\n",
-       "      <td>0.038585</td>\n",
-       "      <td>0.031686</td>\n",
-       "      <td>0.028574</td>\n",
+       "      <th>(5, Ġis)</th>\n",
+       "      <td>0.029420</td>\n",
+       "      <td>0.026479</td>\n",
+       "      <td>0.025317</td>\n",
+       "      <td>0.022390</td>\n",
+       "      <td>0.026839</td>\n",
+       "      <td>0.027053</td>\n",
+       "      <td>0.025887</td>\n",
+       "      <td>0.030753</td>\n",
+       "      <td>0.025297</td>\n",
+       "      <td>0.023454</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.004390</td>\n",
-       "      <td>0.006013</td>\n",
-       "      <td>0.005111</td>\n",
-       "      <td>0.003795</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.018330</td>\n",
+       "      <td>0.015755</td>\n",
+       "      <td>0.017205</td>\n",
+       "      <td>0.016394</td>\n",
+       "      <td>0.015204</td>\n",
+       "      <td>0.013308</td>\n",
+       "      <td>0.012836</td>\n",
+       "      <td>0.012273</td>\n",
+       "      <td>0.012773</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġlocated</th>\n",
-       "      <td>0.097311</td>\n",
-       "      <td>0.099146</td>\n",
-       "      <td>0.101657</td>\n",
-       "      <td>0.102971</td>\n",
-       "      <td>0.097399</td>\n",
-       "      <td>0.080722</td>\n",
-       "      <td>0.090208</td>\n",
-       "      <td>0.093288</td>\n",
-       "      <td>0.089623</td>\n",
-       "      <td>0.081699</td>\n",
+       "      <th>(6, Ġlocated)</th>\n",
+       "      <td>0.095542</td>\n",
+       "      <td>0.083770</td>\n",
+       "      <td>0.089892</td>\n",
+       "      <td>0.095261</td>\n",
+       "      <td>0.087787</td>\n",
+       "      <td>0.075722</td>\n",
+       "      <td>0.087531</td>\n",
+       "      <td>0.080919</td>\n",
+       "      <td>0.087277</td>\n",
+       "      <td>0.075910</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.004809</td>\n",
-       "      <td>0.005128</td>\n",
-       "      <td>0.005993</td>\n",
-       "      <td>0.008487</td>\n",
-       "      <td>0.002254</td>\n",
-       "      <td>0.002306</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.020240</td>\n",
+       "      <td>0.018310</td>\n",
+       "      <td>0.019372</td>\n",
+       "      <td>0.021043</td>\n",
+       "      <td>0.016309</td>\n",
+       "      <td>0.017173</td>\n",
+       "      <td>0.015097</td>\n",
+       "      <td>0.011951</td>\n",
+       "      <td>0.009478</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġin</th>\n",
-       "      <td>0.056276</td>\n",
-       "      <td>0.067980</td>\n",
-       "      <td>0.055451</td>\n",
-       "      <td>0.053152</td>\n",
-       "      <td>0.056496</td>\n",
-       "      <td>0.063859</td>\n",
-       "      <td>0.067301</td>\n",
-       "      <td>0.090764</td>\n",
-       "      <td>0.099042</td>\n",
-       "      <td>0.105749</td>\n",
+       "      <th>(7, Ġin)</th>\n",
+       "      <td>0.066196</td>\n",
+       "      <td>0.051534</td>\n",
+       "      <td>0.039917</td>\n",
+       "      <td>0.042768</td>\n",
+       "      <td>0.042848</td>\n",
+       "      <td>0.049049</td>\n",
+       "      <td>0.051056</td>\n",
+       "      <td>0.068182</td>\n",
+       "      <td>0.077470</td>\n",
+       "      <td>0.081131</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.166978</td>\n",
-       "      <td>0.179903</td>\n",
-       "      <td>0.187151</td>\n",
-       "      <td>0.174460</td>\n",
-       "      <td>0.142561</td>\n",
-       "      <td>0.138237</td>\n",
-       "      <td>0.049052</td>\n",
-       "      <td>0.027608</td>\n",
-       "      <td>0.010232</td>\n",
+       "      <td>0.109565</td>\n",
+       "      <td>0.105077</td>\n",
+       "      <td>0.111922</td>\n",
+       "      <td>0.109415</td>\n",
+       "      <td>0.107961</td>\n",
+       "      <td>0.093928</td>\n",
+       "      <td>0.047363</td>\n",
+       "      <td>0.038378</td>\n",
+       "      <td>0.024075</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġthe</th>\n",
-       "      <td>0.032196</td>\n",
-       "      <td>0.025242</td>\n",
-       "      <td>0.030561</td>\n",
-       "      <td>0.036763</td>\n",
-       "      <td>0.028915</td>\n",
-       "      <td>0.027731</td>\n",
-       "      <td>0.029469</td>\n",
-       "      <td>0.035213</td>\n",
-       "      <td>0.033358</td>\n",
-       "      <td>0.026387</td>\n",
+       "      <th>(8, Ġthe)</th>\n",
+       "      <td>0.041162</td>\n",
+       "      <td>0.028192</td>\n",
+       "      <td>0.031331</td>\n",
+       "      <td>0.032367</td>\n",
+       "      <td>0.023815</td>\n",
+       "      <td>0.021774</td>\n",
+       "      <td>0.023192</td>\n",
+       "      <td>0.026005</td>\n",
+       "      <td>0.026046</td>\n",
+       "      <td>0.021466</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.031717</td>\n",
-       "      <td>0.032127</td>\n",
-       "      <td>0.032323</td>\n",
-       "      <td>0.033680</td>\n",
-       "      <td>0.035631</td>\n",
-       "      <td>0.049355</td>\n",
-       "      <td>0.051697</td>\n",
-       "      <td>0.038773</td>\n",
-       "      <td>0.042955</td>\n",
+       "      <td>0.036160</td>\n",
+       "      <td>0.039207</td>\n",
+       "      <td>0.040695</td>\n",
+       "      <td>0.035847</td>\n",
+       "      <td>0.037754</td>\n",
+       "      <td>0.041692</td>\n",
+       "      <td>0.036638</td>\n",
+       "      <td>0.032836</td>\n",
+       "      <td>0.031847</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġcity</th>\n",
-       "      <td>0.076785</td>\n",
-       "      <td>0.083072</td>\n",
-       "      <td>0.084211</td>\n",
-       "      <td>0.089355</td>\n",
-       "      <td>0.079066</td>\n",
-       "      <td>0.083121</td>\n",
-       "      <td>0.080690</td>\n",
-       "      <td>0.096155</td>\n",
-       "      <td>0.082809</td>\n",
-       "      <td>0.066747</td>\n",
+       "      <th>(9, Ġcity)</th>\n",
+       "      <td>0.107210</td>\n",
+       "      <td>0.108706</td>\n",
+       "      <td>0.118203</td>\n",
+       "      <td>0.117220</td>\n",
+       "      <td>0.114313</td>\n",
+       "      <td>0.109384</td>\n",
+       "      <td>0.108015</td>\n",
+       "      <td>0.124153</td>\n",
+       "      <td>0.099473</td>\n",
+       "      <td>0.084395</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001963</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.001898</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.012754</td>\n",
+       "      <td>0.012063</td>\n",
+       "      <td>0.013644</td>\n",
+       "      <td>0.012627</td>\n",
+       "      <td>0.011632</td>\n",
+       "      <td>0.013889</td>\n",
+       "      <td>0.010530</td>\n",
+       "      <td>0.012236</td>\n",
+       "      <td>0.011505</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Ġof</th>\n",
-       "      <td>0.055402</td>\n",
-       "      <td>0.056398</td>\n",
-       "      <td>0.040687</td>\n",
-       "      <td>0.051288</td>\n",
-       "      <td>0.051508</td>\n",
-       "      <td>0.068726</td>\n",
-       "      <td>0.069254</td>\n",
-       "      <td>0.088121</td>\n",
-       "      <td>0.100993</td>\n",
-       "      <td>0.109671</td>\n",
+       "      <th>(10, Ġof)</th>\n",
+       "      <td>0.051114</td>\n",
+       "      <td>0.052537</td>\n",
+       "      <td>0.049198</td>\n",
+       "      <td>0.059297</td>\n",
+       "      <td>0.063810</td>\n",
+       "      <td>0.081063</td>\n",
+       "      <td>0.086025</td>\n",
+       "      <td>0.108131</td>\n",
+       "      <td>0.127321</td>\n",
+       "      <td>0.133881</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.592769</td>\n",
-       "      <td>0.607561</td>\n",
-       "      <td>0.607735</td>\n",
-       "      <td>0.620819</td>\n",
-       "      <td>0.703357</td>\n",
-       "      <td>0.706121</td>\n",
-       "      <td>0.831520</td>\n",
-       "      <td>0.871000</td>\n",
-       "      <td>0.881191</td>\n",
+       "      <td>0.597318</td>\n",
+       "      <td>0.639940</td>\n",
+       "      <td>0.630997</td>\n",
+       "      <td>0.626800</td>\n",
+       "      <td>0.649399</td>\n",
+       "      <td>0.653869</td>\n",
+       "      <td>0.716391</td>\n",
+       "      <td>0.728729</td>\n",
+       "      <td>0.785446</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1876,62 +1891,62 @@
        "</div>"
       ],
       "text/plain": [
-       "                0         1         2         3         4         5    \n",
-       "The       0.114970  0.112049  0.097279  0.098705  0.103262  0.081201  \\\n",
-       "ĠE        0.084764  0.070963  0.071029  0.065764  0.057698  0.059411   \n",
-       "iff       0.160311  0.178163  0.205860  0.196370  0.189534  0.187073   \n",
-       "el        0.146465  0.133030  0.134302  0.136165  0.153691  0.150928   \n",
-       "ĠTower    0.140587  0.137475  0.147058  0.141195  0.149450  0.162261   \n",
-       "Ġis       0.034933  0.036481  0.031905  0.028272  0.032980  0.034967   \n",
-       "Ġlocated  0.097311  0.099146  0.101657  0.102971  0.097399  0.080722   \n",
-       "Ġin       0.056276  0.067980  0.055451  0.053152  0.056496  0.063859   \n",
-       "Ġthe      0.032196  0.025242  0.030561  0.036763  0.028915  0.027731   \n",
-       "Ġcity     0.076785  0.083072  0.084211  0.089355  0.079066  0.083121   \n",
-       "Ġof       0.055402  0.056398  0.040687  0.051288  0.051508  0.068726   \n",
+       "                     0         1         2         3         4         5   \\\n",
+       "(0, The)       0.070843  0.114042  0.097879  0.095965  0.091416  0.083317   \n",
+       "(1, ĠE)        0.082103  0.070663  0.057518  0.061961  0.050641  0.048189   \n",
+       "(2, iff)       0.140957  0.157038  0.182212  0.169766  0.180928  0.168693   \n",
+       "(3, el)        0.152048  0.153524  0.142376  0.142573  0.152442  0.164651   \n",
+       "(4, ĠTower)    0.163404  0.153515  0.166156  0.160433  0.165161  0.171105   \n",
+       "(5, Ġis)       0.029420  0.026479  0.025317  0.022390  0.026839  0.027053   \n",
+       "(6, Ġlocated)  0.095542  0.083770  0.089892  0.095261  0.087787  0.075722   \n",
+       "(7, Ġin)       0.066196  0.051534  0.039917  0.042768  0.042848  0.049049   \n",
+       "(8, Ġthe)      0.041162  0.028192  0.031331  0.032367  0.023815  0.021774   \n",
+       "(9, Ġcity)     0.107210  0.108706  0.118203  0.117220  0.114313  0.109384   \n",
+       "(10, Ġof)      0.051114  0.052537  0.049198  0.059297  0.063810  0.081063   \n",
        "\n",
-       "                6         7         8         9   ...        38        39   \n",
-       "The       0.064696  0.039593  0.023691  0.014623  ...  0.001963  0.002564  \\\n",
-       "ĠE        0.056259  0.043724  0.048695  0.045607  ...  0.024910  0.025512   \n",
-       "iff       0.193052  0.158926  0.142962  0.155769  ...  0.042697  0.035851   \n",
-       "el        0.186720  0.178493  0.228001  0.243314  ...  0.093743  0.071472   \n",
-       "ĠTower    0.129486  0.137138  0.119140  0.121860  ...  0.034061  0.033870   \n",
-       "Ġis       0.032864  0.038585  0.031686  0.028574  ...  0.004390  0.006013   \n",
-       "Ġlocated  0.090208  0.093288  0.089623  0.081699  ...  0.004809  0.005128   \n",
-       "Ġin       0.067301  0.090764  0.099042  0.105749  ...  0.166978  0.179903   \n",
-       "Ġthe      0.029469  0.035213  0.033358  0.026387  ...  0.031717  0.032127   \n",
-       "Ġcity     0.080690  0.096155  0.082809  0.066747  ...  0.001963  0.000000   \n",
-       "Ġof       0.069254  0.088121  0.100993  0.109671  ...  0.592769  0.607561   \n",
+       "                     6         7         8         9   ...        38  \\\n",
+       "(0, The)       0.066999  0.065708  0.046398  0.042155  ...  0.009971   \n",
+       "(1, ĠE)        0.044730  0.036794  0.039718  0.035112  ...  0.039271   \n",
+       "(2, iff)       0.185059  0.140923  0.134487  0.144310  ...  0.045401   \n",
+       "(3, el)        0.180138  0.175674  0.207000  0.235107  ...  0.078493   \n",
+       "(4, ĠTower)    0.141366  0.142759  0.129513  0.123079  ...  0.032498   \n",
+       "(5, Ġis)       0.025887  0.030753  0.025297  0.023454  ...  0.018330   \n",
+       "(6, Ġlocated)  0.087531  0.080919  0.087277  0.075910  ...  0.020240   \n",
+       "(7, Ġin)       0.051056  0.068182  0.077470  0.081131  ...  0.109565   \n",
+       "(8, Ġthe)      0.023192  0.026005  0.026046  0.021466  ...  0.036160   \n",
+       "(9, Ġcity)     0.108015  0.124153  0.099473  0.084395  ...  0.012754   \n",
+       "(10, Ġof)      0.086025  0.108131  0.127321  0.133881  ...  0.597318   \n",
        "\n",
-       "                40        41        42        43        44        45   \n",
-       "The       0.004040  0.003287  0.003187  0.003994  0.008052  0.010903  \\\n",
-       "ĠE        0.022277  0.022929  0.030234  0.029892  0.008052  0.011526   \n",
-       "iff       0.034897  0.044016  0.045351  0.047375  0.051627  0.040190   \n",
-       "el        0.071115  0.051587  0.014777  0.017563  0.000000  0.000000   \n",
-       "ĠTower    0.029359  0.035042  0.022648  0.005157  0.000000  0.000000   \n",
-       "Ġis       0.005111  0.003795  0.000000  0.000000  0.000000  0.000000   \n",
-       "Ġlocated  0.005993  0.008487  0.002254  0.002306  0.000000  0.000000   \n",
-       "Ġin       0.187151  0.174460  0.142561  0.138237  0.049052  0.027608   \n",
-       "Ġthe      0.032323  0.033680  0.035631  0.049355  0.051697  0.038773   \n",
-       "Ġcity     0.000000  0.001898  0.000000  0.000000  0.000000  0.000000   \n",
-       "Ġof       0.607735  0.620819  0.703357  0.706121  0.831520  0.871000   \n",
+       "                     39        40        41        42        43        44  \\\n",
+       "(0, The)       0.010152  0.014151  0.026439  0.044600  0.054960  0.066599   \n",
+       "(1, ĠE)        0.027281  0.024855  0.029273  0.025610  0.022501  0.013762   \n",
+       "(2, iff)       0.036838  0.033408  0.042922  0.040024  0.040202  0.042203   \n",
+       "(3, el)        0.062609  0.064881  0.049407  0.029825  0.028289  0.019389   \n",
+       "(4, ĠTower)    0.032768  0.028870  0.029831  0.021681  0.020188  0.019192   \n",
+       "(5, Ġis)       0.015755  0.017205  0.016394  0.015204  0.013308  0.012836   \n",
+       "(6, Ġlocated)  0.018310  0.019372  0.021043  0.016309  0.017173  0.015097   \n",
+       "(7, Ġin)       0.105077  0.111922  0.109415  0.107961  0.093928  0.047363   \n",
+       "(8, Ġthe)      0.039207  0.040695  0.035847  0.037754  0.041692  0.036638   \n",
+       "(9, Ġcity)     0.012063  0.013644  0.012627  0.011632  0.013889  0.010530   \n",
+       "(10, Ġof)      0.639940  0.630997  0.626800  0.649399  0.653869  0.716391   \n",
        "\n",
-       "                46   47  \n",
-       "The       0.031432  0.0  \n",
-       "ĠE        0.008484  0.0  \n",
-       "iff       0.025707  0.0  \n",
-       "el        0.000000  0.0  \n",
-       "ĠTower    0.000000  0.0  \n",
-       "Ġis       0.000000  0.0  \n",
-       "Ġlocated  0.000000  0.0  \n",
-       "Ġin       0.010232  0.0  \n",
-       "Ġthe      0.042955  0.0  \n",
-       "Ġcity     0.000000  0.0  \n",
-       "Ġof       0.881191  1.0  \n",
+       "                     45        46   47  \n",
+       "(0, The)       0.079636  0.057506  0.0  \n",
+       "(1, ĠE)        0.015216  0.018794  0.0  \n",
+       "(2, iff)       0.034024  0.022652  0.0  \n",
+       "(3, el)        0.018070  0.012851  0.0  \n",
+       "(4, ĠTower)    0.016651  0.013072  0.0  \n",
+       "(5, Ġis)       0.012273  0.012773  0.0  \n",
+       "(6, Ġlocated)  0.011951  0.009478  0.0  \n",
+       "(7, Ġin)       0.038378  0.024075  0.0  \n",
+       "(8, Ġthe)      0.032836  0.031847  0.0  \n",
+       "(9, Ġcity)     0.012236  0.011505  0.0  \n",
+       "(10, Ġof)      0.728729  0.785446  1.0  \n",
        "\n",
        "[11 rows x 48 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1946,9 +1961,9 @@
     "for layer_idx in range(48):\n",
     "    curr_out = FeatureAttributionOutput.load(f\"../data/cat_outputs/layer_{layer_idx}.json\")\n",
     "    out_dict = curr_out.get_scores_dicts(do_aggregation=False)[0]\n",
-    "    scores[layer_idx] = list(out_dict[\"target_attributions\"][\"ĠParis\"].values())[:-1]\n",
+    "    scores[layer_idx] = list(out_dict[\"target_attributions\"][(11, \"ĠRome → ĠParis\")].values())[:-1]\n",
     "\n",
-    "prefix_tokens = list(out_dict[\"target_attributions\"][\"ĠParis\"].keys())\n",
+    "prefix_tokens = list(out_dict[\"target_attributions\"][(11, \"ĠRome → ĠParis\")].keys())\n",
     "attributions_df = pd.DataFrame(scores, index=prefix_tokens[:-1])\n",
     "\n",
     "# Attributed prefix tokens x layers\n",
@@ -1965,12 +1980,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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KSsJff/0ljijfsWNH1K1bt0Baf39/vbdb2c6dOzF06FDxMzc3N0dgYCB8fHyQlpaG48eP49GjRwDynpzcu3cPf//9N2xsbHTmbYxz6Ny5c+jfv7/4xNbW1hYtW7aEl5cXrK2tkZKSgjt37uDKlSt49eqVQfsgP1OcAwpJSUno1q0bHjx4ABsbGwQGBsLT0xNpaWk4ffq0Strr16+jY8eO4nkjk8nQtGlT1K9fH7a2toiPj8exY8eQmpqKR48eoXPnzjh06JBKy5fC7IMOHTrg5s2b4t9cXFzQpk0bVKlSBTk5OXjw4AHOnz+PlJQUja07X716hQ4dOiAqKkr8W9WqVdG2bVs4ODggJiYGJ06cgFwuR0JCAkJCQrB9+3b0799fZx3T0tLQrVs3XL16FXZ2dmjbti1q1KiB1NRUhIeH4+nTpwCAzZs3o06dOirN/YG8c2bChAmIj48XWyVUrVoVffr0KVCWtu+MrKwshISE4Pz587CwsEDr1q3h7e2NrKwsXLhwQed2aJOTk4M+ffrgzJkzMDc3R9u2beHt7Y3U1FRERETgyZMnAICDBw+iV69e+PPPP2FhYbKvagB511jF9ffHH38Ux9IYMWKE2plDq1WrVqjyrl69ijlz5iAtLQ0eHh5o27Yt3NzcEB8fj7///hsZGRmQy+UYP348/Pz8VMb6yG/GjBn46quvxH87ODigffv2qFy5Mp48eYLw8HDEx8ejR48emDp1aqHqrVDY6/jEiRNVvvuU6/z48WOEh4cjLS0NmZmZ+Pjjj/H48WOVbTSG2NhYTJ8+HcnJyXBwcECHDh1QqVIlPHr0COHh4Xj16hXkcjk+++wz5Obm4j//+Y/OPKWcN48fP0aHDh1w/fp18W8NGjRAo0aN4ODggKdPn+L48eNISkrCixcvMHDgQPz0008YOnSo2rJzcnLQo0cPlXvDypUro127dnB0dBSvSydOnECfPn2MOntLYa6tPPdKjvwtCZTvX0rKPYQucrkcPXr0wPnz58W/NWzYEA0bNoSLiwsyMzPx+PFjXLp0SeX+vShcv34dQUFBePbsmfg3Pz8/NG7cGDKZDNHR0eJvxvPnz6N169Y4duwYfH19VfJRzM5069YtJCcn43//+5/a+3Zlz549w+HDhwHk3Xdpuo5MnToVa9asEf9dsWJFtGrVCpUrV0ZmZiaio6Nx9epVCIKATZs24dGjRwgLC9M6UzZg3M/Y1BS/D4C83w+a7pWePHmCNm3a4M6dO+LfvL29ERAQAGtra1y7dk3skXD79m20b98ev//+O9q0aaOzDnFxcZg1axaePXuGihUrIigoCK6uroiNjUV4eDhycnKQnJyMrl274vbt27h48SLefvtt5OTkoHr16mjTpg2cnJxw69YtHD9+HLm5uYiPj0ffvn0RHR2t9Z7KVMeAocLCwlR+T86YMcMk5QDQI3ReCMqRrY4dO6pNM2TIEAGAYGZmJrx8+VIQBEHIyMgQrKysBCCvJYM6nTp1EvP+8MMPNdYBSlE9a2trwd3dXTh06FCBdBEREYKTk5OY9rPPPtOY58aNG4X//ve/QlxcnMY0ly5dEpo3by7mt2jRIo1pjf0ExdT1ffnypVC7dm0xbcWKFYVt27YJubm5BdKmpaUJW7duFUaPHq02L+XPR1+61hk9erT4/owZM3TmN336dJ37/+OPPxZ27dolHqP55ebmCgcOHBDc3d3FvI4fP66xTCmtfqSsExMTIzg4OIjp/P39hdu3b6ukkcvlwsqVK1We7k2aNEljucY+h95++20xTb9+/YTnz5+rTZeRkSGEhYUJ77//vsa66cvY54DyEyALCwsBgNC/f3/h6dOnKunkcrmQnZ0tCELeuVCvXj1xvbfeekuIiYkpkPfLly+FDz74QExXpUoV4cWLFwZueZ6cnByhTZs2Yp62trbC2rVrxbopy8rKEg4cOCC8/fbbavNSrpu5ubmwevXqAq12bt26JTRr1kxM5+TkpPEptvK+VLRAGzlypJCUlKSSLj09XRg8eLCY1sHBQWNLGkNa0ShfhxWfaVBQkNp6Z2Zmqq2/Pi2FFN9tTZs2LfDU6vXr18KiRYtUzjlNTzSN2VJImT4tDwxZR7m1grW1tWBubi6sXLlSyMnJUUn34MEDoWHDhmLa9u3bayz3yJEjKts0dOjQAtfo1NRUYdSoUSrHl7G+aw25jiu3ZgUgjBo1qkCdX758KQwbNkwl3S+//GLU+iqOQ3X77Pnz50Lfvn3FtGZmZsLJkyfV5mnIeSOXy4X27durfE8pP/FVyMjIEBYuXCi2vrG3txfu3r2rth7/+c9/xPxkMpnwxRdfFGixe/PmTaFRo0Yq26/ts9Pn3DbmtZXnnnFJbSm0d+9elfRhYWHieyXhHkIQdF9z9u/fr3LvcPr0aY11uHr1qjB79mzhzJkzGtMYS1ZWlnjuARA8PDzUtlD6448/hIoVK4rpmjZtqvZc+uyzz1TuI3X5+uuvdX5fbty4UeWeZf369WrL/vvvv1Va2i1dulRtfoZ+xsYmtaWQciuz5s2ba0yn3PLK3t5e2L59e4E0Z8+eFWrVqiWmq1GjhpCcnKyznorrxfz58wu0xLly5YpKT4iZM2cKNWrUECwtLYV169YVuB+NiIgQ7O3txfRbtmzRuE3GPgYK68iRIyotijV9dxiLSYNCu3btUjlg1O3YGjVqCACEJk2aqPxd0bS+evXqBdbJyclR+YB37typsQ7KF3hra2vh0qVLGtOuXbtWTFu3bl0JW6reixcvxAO3SpUqGrsUmToopC996zt37lyxvs7OzsKNGzcMLlPKF7a+6/z999/i+1WrVlXbzURBLperNJ8/cuSI5G1Qdvr0aTGvgQMHakxnqqDQiBEjxDQ+Pj5agwmrVq1SuenXdLNt7HPIzc1NzCs1NVXzBhcDfc+B/N2UunTpovU4EwTVHy19+vTRmV758y5sF5L169eLeVlaWhrcLS0mJkYlmLh27VqNaZ8/f67S9FdTYDj/vhw8eLDGPDMyMsTvDADCjh071KYrbFAIyGui++rVK53rSQ0KAXnNmLV1L5g3b57Kd6e6YHRpDgoBEL7//nuN+V25ckUMBMhkMuHRo0dq0wUEBIj5vfXWWxrPqdzcXKF3794q5RdHUEgulwtvvPGGuM6AAQPUPkxRV2dvb2+d1wwp9QUgdO/eXWOeOTk5Kt1c27ZtqzadIefNjz/+KKZv2bKlzvTK59j48eMLvP/ixQvBzs5OTLNw4UKNeT19+lSoUqWKSp0LExQy1rVVEHjuGZuUoNDr16+Fli1bimmtrKyElJQUyWWa8h5CEHRfc2bMmCG+v379esn1N5VNmzapnCfqgsAKUVFRYhAFUP8j/s6dOyr3pboenCkfrxs2bCjwfkpKitj1yMrKSmswTRDyhj5R/Fh3c3NTOwyDoZ+xsUkJCv30008qddbUjVT5dxYA4bffftOY571791S6Yml6YJz/+2nevHka8/z5559V0ur6Dv78889VrlfqmOIYKIyjR4+qfK8FBgbqdU9aGCYNCj1+/FjlA4uMjFR5/+7du+J7kydPVnlv9uzZ4nv5+5Uq9ycFICQkJGisg3I6ba0hBCHvgFBciGQymcZWIVIoP1W/fPmy2jQlJSgkCLrrm5mZqdJnc926dYUqT98vbCnr5ObmCtWrVxfTaAv0HD58WExXrVo1o1ywFa1B3NzcNKYxRVAoOTlZ5Wnc3r17teYnl8tV+jd//PHHatMZ+xyytLQUgLz+wCWRPuds/i97TWOfKWRnZwseHh7iDcyTJ0901iM+Pl68OS9sH+K6deuKdZ09e7bB+Shflxs3bqzxB62CcqsITTduyvvSyspK6/VcEARh1qxZYvrp06erTWOMoJCusZDU1V/foNDGjRu15pmZmanyw/W7774rkKY0B4X0OZ79/f3F9AcOHCjw/tWrV1W25+bNm1rzu3fvnkpAsziCQocOHZJ0rMfFxYnXSwDC77//brT6ymQytS0VlV2+fFllH6t7AGTIedO4cWMx/cWLF3Wmz8jIEO87nJ2dC3xPf/vtt2J+1atX1znGSP4xMwoTFDLWtVUQeO4Zm75BoWfPnhUY2+qDDz4wuFxT3EMo6LrmjBs3Tnx///79Bm+DsSkHZfL/3lNHeR+2bNlSbRrlcfnUBXoUbt++LaazsbFRex+yevVqMc3UqVP12qb3339fXEddS05DP2Nj0xUUSktLE86ePStMmDBB5Tzt37+/xqCm8piSISEhOuuwdOlSMX2VKlXU3jsq19PDw0OlRXZ+r169Umnt2bhxY63lKwcRNf02M8UxYKhz586p9Ppo1aqVQUFqqUw6UEGlSpVQp04dsZ/18ePHVfonK88uphhPSCEwMBBLly4FkDd+kHL/b+U+476+vqhcubJe9RkwYIDW9x0dHeHt7Y2bN29CEATExsbCz89P6zpPnz7F6dOncf36dSQnJyM9PV1l1Hzl0ckvXryoMz9TK2x9T58+jRcvXgDI218jR44sknpLIZPJMGTIECxbtgwAsHXrVrXjNCneUxg8eLBefUJv3bqFc+fO4c6dO3j58iWysrJU9qFiZpGkpCQ8fPgQNWrUKMzm6O3UqVPIysoCkNcHtlevXlrTm5mZYcyYMWL/1PDwcJ1lGOMcqlGjBu7evYvk5GTs3LkTgwYN0lmuMRn7nH3zzTfVjnum7Ny5c+J4OB07doSHh4fOelatWhV169bF9evXcfXqVbx8+VLjOGfaxMbGiuOeAHnjmRjq77//FpcVM0Vo06dPH7i6uuL58+fIyspCZGQkunXrpjF9YGCgzut5kyZNxGVNs+0UVoUKFdClSxeT5G1tba0y5aumNO+88444Xkd4eLikWVZKOl3XESDvc1aMW6Xuc1aehUkxxoQ2Xl5eaNOmTbFOM618/nTv3l3nsV6tWjV069YNBw8eBJB3HHTt2tUodVGM96ONn58fmjRpIs5CFh4ejjp16mhMr895k5CQIM60U79+fTRq1EhnXW1sbNCqVSscOnQIL1++xNWrV/Hmm2+K7yt/dw0aNAhWVlZa83vnnXcwceJEZGdn6yxbG2NeW4tKeT33gIKfT3Z2NmJjY3H8+HGV2T7r1KmDzz//XGM+xXEPoS/l+83169ejZ8+eMDc3N0rehkpNTVXZJ2PGjNG5zrvvvot169YByJudMD09Hfb29ipphg0bhlOnTgHIu5cfO3as2ryU7/N79uyp9j7qf//7n7g8ZMgQnfUDgA4dOuD7778HkDcLc9++fTWmNeZnXBhbtmxRmTVTHXt7e3zwwQdYsmSJxmNH+Zqrz+c5evRozJkzB7m5uUhISMDNmzfVjsGn0KtXL63jcdna2sLHx0ccD1bXmJW1atWCnZ0dXr16haSkJKSmphYYs83Ux4C+0tPT0a9fP6SlpQHIm5n9jz/+UDvGnLGZdvRK5E0VrwgKHTt2DDNnzhTfUzfItEKbNm0gk8kgCAKOHTumMoWl8npSpqLXJyCjPKBWSkqKxnTXrl3D7NmzcejQIXHwOV30narbFIxVX+UB0Vq2bAlbW1uj1dGYhg0bJgaFfvnlF3z77bcFBlLOyMjA3r17xX8PHz5ca55hYWH49NNP9ZqmVyExMbHIgkLK9fL399drcFrlAd+io6MhCILWH/rGOIcGDhyIL7/8EkBeIE4RGGrfvr1ewRJDmeqcbdasmc40kZGR4nJcXJzePx4UAVhBEBAXF2dQUEj5nK1duzaqV68uOQ9FHZSnTc0/fa86lpaW8Pf3x++//w4AuHDhgtagkDGv0YXRuHFjk91I+/n5wcHBQWe6Vq1aiUEhKdec0sAYn7PysRgQEKBXuQEBAcX6w1T5c9Tn/AHyrtGKoFBhBzlX1qpVK73TKeqt6zjU57xRvhZmZGTofS1UHsz04cOHKkEh5Xrps12Ojo5o2LBhofensa6tRam8nnuA6sQmmnTu3BmhoaFwdXUt8F5x3kPoq3///li4cCFyc3MRFhaGhg0bYsyYMXjrrbfQoEEDnQ9yTOHy5cvi/nJwcFA5dzVp3Lgx7O3tkZ6eDrlcjkuXLhW4Zg4cOBBTpkxBTk4OIiIiEBcXp/YcVA4KabrPV74u/fDDDzoDJ0DevZyCuunRlRnzMza12bNnY+7cuRofksfHx4sPOQH9vsvc3d3h6+srBtEvXLigNSjUsGFDnXlWqFBBXG7QoIFe6RWT16SkpBQIspj6GNDXzz//jNjYWAB5k3YdPHiwSAJCQBEEhYKCgrB+/XoAwMmTJ5GbmyseaIrgTu3atVGpUiWV9SpUqIAGDRrg6tWrKkEgQRBw8uRJlfz1pc8PKktLS3E5JydHbZo//vgDvXv3Fltl6Esxq0RRM2Z9FTPjADDq7B3G5ufnhzfffBOXL19GSkoKDh48WOAJ2cGDB8UbHkV6TRYuXIjPPvtMcj2K8jNXntHB09NTr3W8vLzE5ezsbKSmpmqd7c0Y59C8efNw9OhRnD59GoIgYN++fdi3bx+AvGtB27Zt0bFjR/Tq1ctoF0JTnrPu7u460yjP5nD58mVcvnxZUj2AgjOj6MtY5+zLly9VPk9DjjFdN8jGukYXlj6fqaFq1qwpOZ3yuV0WGONzVt4n+gbei/tHe2Gv0cZ8sGSK41DqtfDevXt6/VDPL/+1ULleUrarsEGh0nI/pKy8nnvqWFhYwNnZGV5eXggICMCQIUM0zoxU3PcQ+qpXrx6WLVuGmTNnQhAE3LhxA7NmzcKsWbNQoUIFtG7dGkFBQejdu7fOFl7Gkv940ScwZWZmhho1aohBBHXXPjc3N7z11ls4cOAAcnNzsX37dpWGBwAQFRWF27dvq6TPLy0tTeVz2rBhg34bpkTX/Zkp7ymkyD/DdWZmJh4+fIjIyEhxH8yfPx+3bt3Cli1b1AaGlD9PW1tbvbfNy8tL6+epTJ/rlPKDb6np81/XiuIY0JdyS8zRo0cXiI+YkmnmT1Oi3JInOTlZnG7w8ePH4omqqbWPYmr6O3fuiDcSV69exfPnz9Xmr4sxIuTPnj3DoEGDxC8GT09PLFmyBCdOnMCjR4/w6tUr5ObmQsgbrwkLFiwQ183NzS10+cVdX+WTRp+n3cVJ+YnAzz//XOB95b9payV0+PBhlYBQq1at8MMPPyA6OhqJiYnIzMwU958gCCqByqL8zBVNDQEUaGarSf50um5gjHEO2dvbIyIiAsuXL1f5wQPkTVu5adMmDB06FJUrV8asWbNUmnUbwtTnrD6t5RRdCgvj9evXBq1nrHNW+fgCDDvGiuL4MgZTtoC0s7PTK52U/VbaGONzVj4e9d2nxf2dVdhrtDGPA1Mch8V1LTTkWNB3/2tTmu6HFMrruQdA5T5NEATk5OQgMTER586dwzfffKMxIFQS7iGkmDFjBsLDw9GxY0eVzzs5ORlhYWGYNWsW6tSpg06dOom/yUzJkOte/rSarj1S7vMHDRqkEvBUKIr7s5LSqyIgIABr164VXxs2bMAff/yBhw8fYtq0aWK6n3/+GcuXL1ebhyk/TwWp16nCXteK8x49v8ePH4vLtWvXNkqe+jJ5S6EaNWrAy8tL7Jd87NgxNGrUSGvXMeW/f/fdd+J677zzjsp6Xl5eej8VMpb169eLB49iO7S1rCjum3lj11e55Ub+H4klzZAhQzB79mzk5ubi0KFDeP78udgkOCkpSezSYmZmprX/qPKFccyYMdiwYYPWC1BxfebKN13p6el6rZM/XVE1UbSyssJHH32EGTNm4PLlyzh27BhOnTqF48ePIz4+HgDw6tUrLF++HMeOHUN4eLjBX6ol4ZxV/jKcPHky1qxZY/QyNDHWOZv/pl5dH391lI+xojq+SjJF82VdjL3fiuOhhCkpH4+G7NPiUNhrtDHPn+I6DpWvGSEhIfj1118LnaeDg4N4jS/KY6E03Q8ZU2k89wqjJNxDSBUUFISgoCA8efIEEREROHnyJE6cOIGLFy+K3wV//fUXAgICcPjwYY0BMWMw5LqXP62ma0+vXr3g7OyMly9f4vLly7h69arY9Ugul2Pnzp1i2mHDhqnNI/99zPPnz1W6JpUHzs7OWLVqFVJTU8VWMvPnz0efPn0KtCgz5edZXErSMaD8IFzbuEqmYPKWQoBqFy9FUEefoJCipZCm9aR0HTOWv/76S1yeN2+e1i8GAGK/wOJi7PoqN2O7d+9e4SpnYlWrVkX79u0B5DUV3LVrl/jerl27xOaD7du3R7Vq1dTmIZfLERERASAveLRkyRKdEekHDx4Yo/qSKTfh1LcOyoNIWllZFfmFWiaToVGjRpg0aRK2b9+OuLg4XLhwAaNHjxbTnDlzxqAuBgol4ZxVPm+UnwIUBWOds87OzipP2Qw5xipWrGhw+WWFvvtNuX+6uv2m/Fno84TKGE/CShLlfaLcr18bfdOZSmGv0cY8f4x1HEplimuhIfvVGOM/lKb7IWMqjedeYZSEewhDVapUCQMHDsSaNWtw/vx5PH78GKtXrxbHjcrIyDD5JAbK52dcXJzKoNya5Obm6nXtsba2VhlkWLll0J9//imOfePj46NxvDEXFxeVH99FfY9Wknz11Vfi76Hs7GzMnTu3QBrlzzMjI0Pvbs0l+V6Qx0CeIgkKKXfxUgw0pwjuVK1aVWNf7Jo1a4otgRTplQeqk9J1zFiU+8PrGrBPLperjH+kiSm7TBi7vsqzx0VGRha6a4+pKT8ZUB5sTnlZ09MDIK/fq2KGEg8PD50DIV+7dk2vC6QpPnPlWZmioqL0GghRMXODYv2S0H2nSZMm2LRpE959913xbwcOHDA4P1Ocs1IpD8Z56tQpvW6KjEX5nL1165bBN+cymQyNGzcW/6187Gjy+vVrnD17Vvx306ZNDSpbqpJwHGty9epVvZ6uKQ96qG6/Kf8wSUpK0pmfvt0ESvK+U6Z8LJ45c0avdRQzKhmL1H2lfI3W5/zJn86Y54/yIMna6DoOpVK+Fl68eNEoLUiU96s+25WWloarV68WulxjXVsVeO6VTCXhHsJY3N3dMWXKFJUWev/88w/u3r1rsjLffPNNcQD61NRUvb6LLl26JF4bzM3Ntc5SqHwPv337dvH+Svk+f+jQoVrL8/f3F5dL8udnag4ODli4cKH47z179hSYYKBatWoqv4X0+S5LTEzErVu3xH8X1b2gFCXlGBg6dChmzJiBGTNm6DXgtjEVeUuhJ0+e4PTp0+IXsqZWQgqK1kLXrl3D6dOnkZCQoDbfoqI86JauZrP79+/XK9qoPCuWsQdONXZ9W7ZsKTapS01NxY8//lio+ply2wGgX79+YrejkydP4v79+7h37554EbO1tUW/fv00rq+8//QJgCmm0NTFFNvdunVrMdL97NkzhIWFaU2fm5uLzZs3i//u0KGDUephLCEhIeKy8oCeUpninJWqTZs2cHFxAZD3pEwxm1BR8PT0VJkKtTCtrpSPkS1btugMbu3fv18MWCimlS4Kpr6uFEZmZiZ2796tNU12drZKs3dFi0dlnp6e4o/ImJgYnd1XlFtKalOS952y4OBgcTkqKgoxMTFa0z948MDosx9J3VfK58///vc/lRlc1Hn06BEOHTqkdv3COnnypM7WLf/884/KYMzK+9xQtWrVEq9H2dnZ2LhxY6HzVD4/du7cqfOz2Llzp+QBg9Ux5rUV4LlXUpWEewhja9OmjcoMa4W5x9LF0dERzZs3F/8dGhqqcx3l64K/v7/WrupBQUHigOcPHjzAsWPHkJ6ejv3794tptD38BfKmqldYt25dkT64K2lGjRqlMt7nokWLCqRRvubq83mGhoaK3RarVq2KOnXqFLqexlZSjoFJkyZhxYoVWLFihcp5UxSKJCjk7e2t0j3niy++EHe2rtY+iqCRIAj44osvxL9Xq1YN3t7eJqitdsqtmrS1Xnj27JnKoF3aKE//qRhPxViMXV9ra2t8+OGH4r9nz56NmzdvGlw/U247kPdl1Lt3bwB5x9C2bduwbds28fjr3bu31i5Tbm5u4qj2L1++FLuSqXPy5Em9g0Km2G4XFxcMGjRI/PfMmTO19m1fu3at+MTGzMwM7733nlHqoU1WVpbeYy8oNx0uzFT1pjhnpbK2tsbUqVPFf3/44YeSPvfC3rBNnz5dXF65cqXBN+jjxo0Tb5AvXLiAH374QWPaFy9eYNasWeK/Bw8erNcMEcZg6utKYc2fP1/rTBWLFy8W621vb4/BgwcXSOPk5CRO6fr69WuVp6L5RUdHi7OA6lLS951Cw4YN0aJFCwB51/apU6dqvYmbNm2a0cdVkrqvunTpgjfeeANA3rVQ+ZqQnyAImDRpkhgc8Pb2RqdOnQpX4Xz5T5kyReM+k8vlmDx5svjvwMBArVMISzF79mxxed68eZIGu1X3g3vIkCHigMcPHz7E0qVLNa6flJSE+fPnS6itdsa6tgI890qqknAPoS99u/K8ePFC5V6sMPdY+lDuovbNN99onYH1/Pnz+P7778V/jx8/XmveMplMpSXQ1q1bsX//frGlUcuWLeHj46OzfooHdxcuXJA023BiYqJeLfNLCwsLC3zyySfiv/fv31+gZaXy57lv3z788ccfGvOLjY1V+f3+/vvvl8hWkTwGiigoBKi2CPrtt9/U/l0d5XGFlNcrjq5jQN6gZgpLlixRO9r9hQsXEBQUhIcPH+o1EKty87AzZ84YdUwaU9R31qxZYkDu5cuXCAwMxI4dO9TeFLx69Qrbt2/HmDFj1OalvO26np4bSvkJwc8//6x31zEgL1jSvXt38d+jRo1S2wx6165d6N69O+RyueTP/NdffxW7qBXW/PnzxUHgbt26ha5duxZoFpybm4s1a9ao3MxOmDChwExgppCQkIAaNWrgo48+wrlz5zSmO3z4sMoMHuqmEdWXKc4BQ8yYMQMNGjQAkHfD37x5c+zevVvjzXJiYiJ++OEHNG3aVOMsEPoaNWoUWrduDSDvCXS3bt3w7bffqn0anZ2djYMHD6JPnz4F3vP29la5GZg4cSK++eabAtsQExODLl26iC0RnJycjPpDTJc33nhD/JEYGxur0oWtuFlZWeHhw4fo0qUL7ty5o/KeXC7HkiVL8J///Ef825w5czSOYaE8QP7HH3+MEydOFEhz6NAhdOnSRe+bsKK4JhuL8o1mWFgYRo4ciZSUFJU0aWlpePfdd7F3716jD9oo9TpuZmaGL7/8Uvz39u3bMW7cuAKB8tTUVIwePRp79+4V/7Zs2TK10wMbysrKCgcPHsSoUaMKPDxITk7G4MGD8ffffwPI+9G1ZMkSo5U9bNgwsdVTamoqAgMD8f3332vcfykpKdi6dSuCg4MxadKkAu87OzurBKDnz5+PpUuXFrhJv337Njp37oxHjx7BysrKKNtirGsrwHOvpCop9xD6GDhwIHr27Ik9e/ZobNUUHx+PIUOGiOebr6+vyR+yDx06VOwClp2dja5duyI8PLxAuiNHjuCtt94Sx8lr2rSp2oci+Snfy+/ZswebNm1S+54mzs7O+Oqrr8R/f/bZZxg5cqTG32OCIODkyZP48MMPUbNmzRI/lIZUo0aNEodvEQQBn3/+ucr77du3V7kv79+/v9pr1vnz59GpUye8ePECQN7kU8oPG0qSknIMeHl5QSaTQSaT6dUKy5hMPvuYQlBQEHbs2KHytwoVKujsL9egQQO4urqqTEMPFF9QaOTIkVi5ciVu3bqFrKwsDB8+HIsXL0ajRo1gY2ODq1evij92GzVqhK5du2LZsmVa86xcuTJat26NU6dOITMzE40aNUK3bt1QpUoV8SbQ29sbH3zwQYmor5OTE/bu3YvOnTvj6dOnSExMxODBgzF16lS0bt0a7u7uyMzMxJ07d3DhwgVkZGRo7A/cr18/McI8e/ZsHDp0CA0aNFC5gZg7d26hRoHv2rUr3N3d8ezZM1y/fl38u7u7O7p27apz/Xnz5mH//v3IyMjA/fv30bJlS7Rq1Qq+vr7Izs5GZGSk+ON33LhxuHXrltYWRUBekMPW1hYZGRm4ePEi6tWrh+DgYLi4uIg/3rp06YIuXbpI2lZvb29s2LABQ4cOhVwuR2RkJOrUqYO2bdvC29sbaWlpKjN8AXlPUXR95sb04sULrFy5EitXroSrqyuaNGmCatWqwcbGBk+fPsXly5dVAlm+vr6YMmWKweWZ4hwwhIODAw4cOIBOnTrh3r17ePz4MQYOHIiKFSuiZcuWqFy5MgRBwPPnz3Ht2jXcvn1bDLYUttuIhYUFdu7ciQ4dOuD27dt49eoVJkyYgLlz56JNmzaoUqUKXr9+jdjYWJw/fx4pKSkaW/WsWLEC586dw9mzZ/H69WtMnDgRX375JQIDA+Hg4IA7d+7g2LFj4g8yCwsLbNy4sUiCjgrm5uZ4++23sW3bNgB53R26deuGmjVrimMbuLq6qjwNKyr9+/dHTEwMoqKiULduXfHcTE1NxbFjx1S6SLdr106lVUV+kyZNwrp16/Do0SO8ePEC7dq1Q5s2bVC3bl1kZmbi3LlzuHHjBoC85tujRo3SWb9+/fqJT2m//fZbnD9/Hk2bNlWZevqDDz4olpa6+XXu3BmTJ0/G119/DQD46aefsH//frRv3x6VKlXC06dPER4ejpSUFLi6umLq1KlicNIYARZDruMDBw7EsWPHxK5GGzZswM6dO1Xq/Ndff6kEiqZOnYq+ffsWur7K5syZgzVr1uDHH3/Evn370KFDB3h4eODx48f4+++/Vcb6mTNnjsoDusIyNzfHrl270LlzZ0RHRyMlJQXjx4/HrFmz0KpVK1SrVg3m5uZITk7GzZs3cf36dfFHoqbu3nPmzMHhw4dx8uRJCIKAjz/+GGvWrEFQUBAcHBwQExOD48ePQy6XIyAgAN7e3uL1oTCMeW3luVcylZR7CH3k5uYiLCwMYWFhsLKyQoMGDeDr6wtnZ2ekpqbiwYMHiIyMFO8tzM3Ni2Q2VCsrK2zfvh1BQUF49uwZHj9+jA4dOqBRo0biGFUXL17EpUuXxHU8PDywfft2tdPI59egQQM0btwYFy9eRHJyshjQtrS0VGlBr82oUaNw9+5dsbvUjz/+iK1bt6Jx48aoW7cuHBwckJaWhri4OFy8eLHMTd6gzNLSEnPmzBF/d+7evRsLFy5UaS26efNmtGnTBnfu3EFaWhoGDhyI2rVrIyAgAFZWVrh27RrOnDkjNhiwt7fH9u3bxdY4JVG5PwaEIvLPP/8IAFRePXv21Gvdnj17Flj32rVreq2rvI4+goKCxPTh4eFq09y8eVOoVatWgTopv9q0aSPExcUJCxYsEP+2YMECjeWePXtWcHR01JhfUFCQXvUvqvoKgiDcv39faNeundZ8lfNXJzs7W2ce9+7dU1lH6mcqCIIwceLEAvlOmjRJ7/X3798v2NnZaa3ne++9J2RmZup1DAmCIKxbt06QyWQa88u//0eOHCm+t3nzZq31PXjwoFCpUiWdn8vgwYOF9PR0rXkZ8xyKi4sTrK2t9TpmAAjBwcFCQkKCXuVqY+xzQMp5kl9SUpIwYMAArZ+98svFxUUIDQ0t9D5QlN2nTx+9yq1WrZrGfFJTU4WBAwfqzKNKlSrC//73P611krovw8PD9bou3r9/X6hcubLGunl6eqqk37x5s/jeyJEjddZDSv3z5x0fHy+0bNlS677r0aOHkJqaqrP88+fPCxUrVtSYj5WVlfDNN98IgqD/uTx48GCtdct/Xnt6emq8Xivoe11U0Pe4yM3NFaZNm6b1fKpataoQGRkp/PDDD+LfpkyZorMO+pB6HVdYtGiRzmuhjY2NsHjxYqPUUxAKfoecPHlSqFKlisbyzc3Nhblz52rN09DzRhAE4dWrV8L48eMFCwsLva5Jtra2WvfHy5cvhW7dumnNo3Xr1sKjR4/0+j6Vcm0y1rWV557xKH9X6Lrm6VJS7iF0Hbfqfi9penl4eAj79+83bIcY6ObNm0KTJk101q1p06ZCTEyMpLxXrFhRIJ9evXpJruPOnTuFqlWr6r0f/f39hczMzAL5FOY+0ZiUjxkp1+isrCyhevXq4rrDhg0rkObx48dChw4ddO4jHx8fISoqSu966vqNIwjSr2v6XCsVjHUMGEK5nvrsB2MqsnB9/fr1VaaxA3R3HdOUzt3dXWVwv6Lm6+uL6OhoLF68GM2bN4ejoyOsra3h6emJnj17Ytu2bYiIiNA4zbk6zZs3x+XLlzF9+nQ0adIEzs7O4hPtklhfIG+QxYiICBw5cgTvv/8+6tevjwoVKsDc3BxOTk7w8/PDqFGjsHv3bjFqn5+lpSWOHDmCdevWoVOnTqhcubLRmnUrGz58eIG/6dOkVKF37964evUqJk6cCF9fX9jY2MDBwQG+vr4YPXo0IiIi8P3330tqIj1+/HgcP34cw4cPh6+vL+zt7Y3Wz7Znz56IiYnBmjVr0LlzZ1StWhVWVlZwdnZGvXr18OGHH+L06dPYtm2bylNIU6tWrRqSkpJw4MABzJw5Ex06dEDNmjVha2sLc3NzuLi4oFGjRnj33Xdx+PBhhIeHo3LlyoUu11TngCFcXV2xa9cuXL58GZ988glat26NKlWqwMrKCjY2NqhcuTICAwMxefJkHDhwAAkJCRg5cqTRyt67dy+ioqIwdepUNG7cGBUrVoS5uTkcHBxQt25dvPPOOwgNDdU6VpiDgwN27tyJU6dO4YMPPkC9evXg7OwMKysrVK1aFV26dMHXX3+N27dvF6rrX2F4enri0qVL+PTTTxEQEIAKFSrAwqLIGsdqVbVqVURERGDjxo3o0KGDeH5WqVIFISEh2LdvH3777TexK6g2TZs2xY0bN/DJJ5/Az88PDg4OsLOzg6+vLyZMmIDo6GiVceD0sXXrVmzduhU9e/ZE9erVVQbALWlkMhlWrVqFyMhIcYBMa2truLm5oXnz5liyZAkuX76Mli1bqrQ4NtbTSkOv4/PmzcPNmzcxb948tGjRAhUrVoSFhQUqVqwIf39/fPrpp7h58ybmzJljlHqq07p1a1y6dAmff/45mjVrBjc3N1hbW+ONN97A2LFjERUVVaDbgDHZ2tpi3bp1uH37NhYtWoT27duLrUatrKzg7u6OgIAAvPfee9i5cyceP36sdX84OTnh0KFD+OWXX9CrVy/xfqJq1aro2LEjNm3ahKNHj6JKlSpG3xZjXVt57pVMJekeQpsDBw7g/PnzWLZsGfr164f69evDyckJ5ubmsLOzg6enJ3r16iWed4oxN4uKr68vzp07h507d2LgwIHw8vKCnZ0d7Ozs4OXlhUGDBmH37t04d+6c5BZxQ4YMKfC7Scp9vsLAgQNx9+5dhIaGYvDgwfDx8RF/kzk5OaFevXro27cvvvrqK9y8eRNnzpwp1d0jNbGyssLHH38s/nv79u0FBpWvVKkS/vrrLxw6dAijRo2Cj48PHBwcYG1tjRo1aiAkJASbNm3CtWvXxHHISoPyegzIBKEcD7FORERE5cLQoUPFLkM7duzQu1tBWTFq1Chs2bIFQF7Tf326ExIZQ3k/94iISrrS1bGXiIiISKK0tDSEhYWJ/y5NTy2JSjOee0REJR+DQkRERFSmffLJJ+KgkAEBASrTTBOR6fDcIyIq+RgUIiIiolJp7dq1WLRoEeLi4tS+//TpU7z33nv473//K/5N24xuRKQfnntERGVHyRh1k4iIiEiixMREfPbZZ1iwYAHq16+PBg0aoEKFCsjMzERMTAzOnj2L7OxsMf3IkSPRp0+fYqwxUdnAc4+IqOxgUIiIiIhKNUEQ8M8//+Cff/5R+76FhQWmTJmCZcuWFXHNiMo2nntERKUfg0JERERUKs2cORP169fHkSNHcPnyZTx9+hSJiYnIzMyEq6sratWqheDgYIwZMwY+Pj7FXV2iMoPnHhFR2cEp6YmIiIiIiIiIyiEONE1EREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKEREREREREREVA4xKERERERERERURGJjYzFjxgzUrVsX9vb2cHV1RYsWLbB8+XK8evXK4Hzv378PmUwm6eXl5aU2r+DgYL3zoNLNorgrQERERERERFQeHDx4EMOGDUNKSor4t1evXuHcuXM4d+4cNmzYgLCwMPj4+BRJferUqVMk5VDJxaAQERERERERkYlFR0dj0KBByMjIgIODA+bMmYP27dsjIyMDO3bswPr163Hr1i306NED586dg6Ojo6T8q1WrhitXruhMt2TJEmzbtg0AMHLkSK1pmzdvjs2bN0uqB5UuDAoRERERERERmdiUKVOQkZEBCwsL/Pnnn2jVqpX4XocOHVC7dm3MmjULt27dwsqVK7Fw4UJJ+VtaWqJhw4Za08jlchw9ehQA4OjoiD59+mhNb29vrzNPKt04phARERERERGRCUVFReH48eMAgLFjx6oEhBRmzJiBevXqAQDWrFmDnJwco9fjyJEjePToEQCgf//+sLW1NXoZVLowKERERERERERkQvv37xeXR48erTaNmZkZRowYAQB48eIFwsPDjV6PH3/8UVzW1XWMygcGhYiIiIiIiIhM6MSJEwDyumM1a9ZMY7qgoCBx+eTJk0atQ2pqqhic8vLyQrt27YyaP5VODAoRERERERERmdD169cBAD4+PrCw0Dy0b926dQusYyx79uwRp7wfPny4XtPJ37hxAwEBAXBxcYGNjQ2qV6+O3r1748cffzRJ9zYqehxomojKhMzMTHG2BXd3d61ftkRERESk2+vXr/Hs2TMAgJ+fH2xsbIq5Rnl1evz4scnyT0hI0Otesnr16nrnmZmZicTERL3Wq1ChAuzt7ZGeno6HDx/qXYY+lLuOKbqp6fLkyRM8efJE/Hd8fDzi4+Nx4MABLF26FHv27BHHQaLSib+aiKhMuHLlCvz9/Yu7GkRERERlUlRUFFq0aFHc1cDjx49Ro0aN4q4GBEHQO21qaqq47ODgoDO9IiiUlpZmUN3UefDgASIiIgAArVu3ho+Pj9b0ZmZm6NixI7p3745GjRrBzc0NqampuHDhAr7//ntcv34d165dQ/v27REVFYWaNWsara5UtBgUIiIiIiIiIjKRzMxMcdnKykpnemtrawBARkaG0erw888/i4EsfVoJ7d27Fy4uLgX+3rZtW3z44YcYN24ctmzZgidPnmDq1KnYu3ev0epKRYtBISIqE9zd3cXlM7/tRpVKHvqt+OqltIIsraWlBwA9+msXKj0AmJlLS29u+su/zKII9hWKYF9JeBL4/2UUwZB9ko+rohhG0IB9JfUzNOAjN+icIiL1DLkmSi7DgHXkryWmlz4WipArl7ZCrsQ6AYBcYhkWugMM+ckcXPROm/D4MfzbdQSgeq9VUvSDHewM+mIo6BUE/IK88XaioqJQpUoVo+QLQKXbXXZ2ts70WVlZAGDU6eJ/+uknAHkBp0GDBulMry4gpGBpaYkNGzbg9OnTuHnzJvbt24f4+HhUq1bNWNWlIsSgEBGVCcr9vqtU8kD1qpX1WzFNYt94K0MCHRJ/jBdFUMjCUnoZEhVJUMiQQAeDQiZUFEEhA84PBoWIjKdIgkIGlCE1KPRa9w/z/IokKPRa4joGPKySObpKXgdAiRyv0Q4yOBht7qRccalKlSqSxgzSxdHRUVzWp0tYeno6AP26mukjKioKN27cAACEhIRoDfjoy8LCAmPHjsWsWbMAABERERgyZEih86WiV/LObCIiIiIiIiIdzCCDmZEC/2aC6R4g2NjYwM3NDUlJSYiLi9OaNjk5WQwKGWvsJEMGmNZH/fr1xeX4+Hij5UtFi1PSExEREREREZmQIoASExOD11pahCla9AAwyqxeOTk52LFjBwDAw8MD3bp1K3SeCvpMaU8lH4NCREREREREVOqYGfllSoGBgQDyuoadP39eYzrFDGEA0KZNm0KXGxYWhqSkJADAkCFDjNoN8Nq1a+Jy1apVjZYvFS0GhYiIiIiIiKjUkckAMyO9TN3o5e233xaXN2/erDZNbm6u2NXLxcUF7du3L3S5yl3HRo4cWej8FF6/fo1NmzaJ/27Xrp3R8qaixaAQERERERERkQn5+/ujbdu2AICNGzciMjKyQJqVK1fi+vXrAIApU6bA0lJ1YpCjR49CJpNBJpNh1KhROst8/vw5wsLCAAB+fn5o3LixXnUNDw/HixcvNL6fk5ODd999V6xrr169jDb+ERU9DjRNREREREREpY4xu30VRWuJNWvWoE2bNsjIyECXLl3wySefoH379sjIyMCOHTvwww8/AAB8fX0xY8aMQpe3Y8cOZGfnzbQnpZXQli1bEBISgpCQEAQHB6NOnTpwcnJCWloazp8/jx9++EHsOubh4YE1a9YUuq5UfBgUolIjOzsbDRo0QExMDHbv3o3+/fsXd5WKlZeXF2JjYzFy5EiEhoYWd3XUEgQBb775Jq5evYpNmzZh9OjRxV0lIiIiIqJi0aRJE+zcuRPDhg1DSkoKPvnkkwJpfH19ERYWpjKNvaEUXcfMzc0xdOhQSeumpaVh27Zt2LZtm8Y0fn5+2LFjB954441C1ZOKF7uPUamxZs0axMTEoGHDhujXr5/WtKdOncKwYcPg6ekJGxsbVK5cGV27dsX27duLpK73798Xm3bev38fABAaGir+zdBXcHBwkdRfCnXbqiCTyTB37lwAwNy5c8XpNYmIiIiICstMJjPqqyj06tULly9fxrRp0+Dr6ws7Ozu4uLigefPmWLp0KaKjo+Hj41Pocm7fvo0zZ84AADp37ozKlSvrve7s2bPx1VdfYeDAgWjYsCEqVaoES0tLODg4wNvbG4MGDcLu3bsRHR2tMi09lU5sKUSlQmpqKpYuXQoAmDdvntbpDxcuXIhFixYhNzdX/NuTJ0/w559/4s8//8TWrVuxZ88e2NjYmLzeBAwcOBALFy7EzZs38c0332DWrFnFXSUiIiIiomLj6emJVatWYdWqVZLWCw4OhiAIeqWtXbu23mnzq1evHurVq4epU6catD6VLgwKUamwbt06JCUloWbNmhgwYIDGdN9//z0+++wzAIC3tzc++eQT+Pn54dGjR1izZg3Cw8MRFhaGMWPGaG0KaQpvv/02mjdvrva9s2fPYsyYMQCADz74AB9++KHadPb29iarn6mYmZlh2rRpGD9+PFasWIHJkyczIEdEREREhVbaxhQiKokYFKISTy6XY+3atQCAwYMHw8xM/SX7+fPnmD17NgCgZs2aOH36NCpWrCi+37NnT/Tp0wcHDx7E9u3b8d577xVpdywXFxe4uLiofS8xMVFc9vDwQMOGDYuoVkVjwIABmDRpEp49e4YdO3boNVsCEREREZE2iinpjZKXcbIhKnUYEKUS7/Dhw3j48CEAaB0gbcOGDXj58iUAYOnSpSoBISBvgLVvv/0W5ubmAIDly5ebqMaUn6urK7p16wYgbwpOIiIiIiIiKn4MClGJt2vXLgB5/WL9/Pw0ptu/fz8AwMnJCX379lWbpnr16ujUqRMA4K+//kJqaqpxK1uMbt68iXHjxsHLywvW1taoVKkS+vTpg9OnT+u1fkxMDKZNmwY/Pz84OzvD1tYWtWrVwqhRo3Du3LlC108xOPjJkyfFIB8RERERkaHMjPwiKo947FOJFx4eDgBo2bKlxjTZ2dmIiooCALRq1QpWVlYa0wYFBQEAsrKyjBLsKAn27duHpk2bYsOGDYiNjUV2djaePn2K/fv3IzAwEDt37tS6/ooVK1C/fn2sXr0aV69eRUpKCjIzM3Hv3j1s2bIF/v7+mD9/fqHqqPj8BEHA77//Xqi8iIiIiIiIqPAYFKISLS4uTpzmvEWLFhrT3bp1C3K5HABQt25drXkqv3/9+vXCV7KYXblyBUOGDEGlSpWwdu1anD59GpGRkVi4cCFsbGwgl8vx3nvv4dmzZ2rXX758OWbOnImcnBy8+eabWLduHY4cOYJz585h69ataNWqFQRBwKJFi/D1118bXE9fX19xTKWIiAjJ68fFxWl9JSQkGFw3IiIiIip9ZDKZUV9E5REHmqYS7dSpU+JykyZNNKaLi4sTl6tXr641zxo1aojLpurG5OXlZfAUkFJduHABzZo1w99//w0nJyfx7y1btoSPjw+GDRuGlJQU/Pzzz5g2bZrKuteuXcPcuXMBAAsWLMCCBQtUvhCbNWuGd955ByNHjsTPP/+MuXPnYvjw4ahQoYKYRt9tlclkaNKkCcLDw3H+/HnJ26n8uRERERERcfYxosLjsU8lmnKwx8PDQ2M65bGBHBwctOapPK17WlpaIWpXcmzatEklIKQwZMgQVK1aFQBw/PjxAu+vXLkSOTk5aN68eYGAkIKZmRn++9//wtraGmlpadizZ4/B9VR8hvfu3SuyoBkRERERERGpx6AQlWjKXZ6UW6fkl5mZKS5rG08IAKytrcXljIyMQtSuZPDz88Obb76p9j1F6xwAuHv3boH3Dx48CCBvEGhtTWZdXFzEQb4jIyMNrqurqyuAvPGcXrx4IWndhw8fan0pxpQiIiIiovLBTGbcF1F5xO5jVKI9f/5cXNYWFLKxsRGXs7OzteaZlZUlLtva2haidiWDrjGUFIGY/DOtxcbGikG3OXPmYM6cOXqV9/jxYwNqmUf5M0xPT9f6meanq1sgERERERERScOWQlSiKQd7tLXqcXR0FJd1dQlLT08Xl3V1NSsN7OzstL5vZpZ3misG4lZ4+vSpQeW9evXKoPUA1c/Q0tLS4HyIiIiIiGQw3nT0bChE5RVbClGJ5u7uLi4/f/5cJfijTLkVifI4ROooDy5dngcvVg4SzZ8/HwMGDNBrPeUxmaRSbvnl7OxscD5ERERERERUeAwKUYmmHBRKTk6Gp6en2nS+vr4wNzeHXC7HjRs3tOap/H69evWMU9FSyM3NTVy2tLREw4YNTV5mcnIygLwBp5VbgRERERERSWUmk8HMSFPJm7GtEJVT7D5GJZpicGMAuHXrlsZ0VlZW8Pf3B5A3ELK2cYUiIiIA5A043bx5cyPVtPSpVauW2Frn5MmTRVKm4jNs0KBBkZRHRERERGWXsbqOGXNqe6LShsc+lWjNmzcXW5ScPXtWa9q3334bAJCSkoK9e/eqTRMXF4cjR44AADp27KixO1p5YG5uju7duwMA/vzzT1y/ft2k5aWkpODmzZsAgICAAJOWRURERERERLoxKEQlmpWVlRhA0DXl+Lvvviu2fPn444+RlJSk8r5cLseHH34ojqUzc+ZMtfkcPXoUMpkMMpkMo0aNKuQWlGxz5syBubk5cnNz0b9/f63jMcnlcmzdulXnmE2anDt3DoIgAAC6dOliUB5ERERERAqckp6o8BgUohKvd+/eAPKCQvmnVVfm6uqKpUuXAsibbj0gIACbN2/GuXPncODAAXTu3BkHDx4EAAwePBjBwcEmr3tJ5+fnhxUrVgAArl27hoYNG2LWrFn4/fffER0djcjISGzfvh2TJ09GjRo1MGzYMLx48cKgsv766y8AQMWKFREYGGisTSAiIiIiIiIDcaBpKvFGjBiBOXPmIDMzE/v27cOIESM0pn3//ffx6NEjLFq0CHfu3MGYMWMKpOnevTs2bdqkMQ/ladOVB2Muq6ZOnQp7e3tMnToVL1++xPLly7F8+XK1aa2srAweIHr79u0AgEGDBnE6eiIiIiIqNGOOBcTWElRe8dinEs/NzQ19+/YFAGzbtk1n+s8++wwnTpzAkCFDUKNGDVhZWcHDwwOdO3fGtm3bEBYWpjWwERkZCQCwsLDApEmTjLMRJdy4ceNw9+5dfPbZZ2jTpg0qVqwICwsL2Nvbw9fXF/369cN3332H+Ph4+Pj4SM4/MjIS9+7dAwB88MEHxq4+EREREZVDMshgZqSXjLOPUTklExSDfBCVYGfOnEHLli1hbm6OO3fuaJya3hiCg4MRERGB0aNHa21RRPp79913sXHjRnTt2hW///67ScqIi4tDjRo1AAAPzkagetXK+q2Y9kJaQVbW0tIDgExi/N2QqVXNzKWltzB9ay2ZhSH7SuK2S923gPR9ZcjXpFkRPHMpin0lmSG3FFK3w4Dzw0jTFRMRDLsmFkUZ8tfS0r/WPFOtJkKuXNoKuRLrBACvJa5jKf27VuboqnfauPh41PDNmzX24cOHqF69uuTyjE35nu8Taxe4GOn77YWQi8VZLwCUnG0lKgpsKUSlQkBAAPr27Qu5XI4lS5aYrJysrCycOXMG5ubmmDt3rsnKKU8ePHiAH3/8EUBeKy4iIiIiImPgQNNEhcegEJUaixcvhoWFBTZv3mzwDFi6REVFITMzE0OGDIG3t7dJyihvlixZgpycHAwYMIBT0RMREREREZUgHGiaSo06depg06ZNuHPnDh48eGCSJp1t27YFe1QajyAI8PT0xIIFC9QO+k1EREREZCgONE1UeAwKUakyfPjw4q4CSSCTyfDxxx8XdzWIiIiIqAwyg/G6fZnxuTCVUwyIEhERERERERGVQ2wpRERERERERKWODICZkaaS5zjTVF6xpRARERERERERUTnElkJEVPYIciBXrl9aBxdpeZtbSq4OhFyJZRhyaZb2fEsmM+B5mJm5tPSGDNpuJvVZhQHbIXUVQ/YVJG67QeMYSFwpV+JxCEjfV4Zsh9Tzoyie5RrymUs+3g3ZWUXxgRRBGUUxoUORfB5SiyiCMgy6tkv8ztH3+1WZ1O+1omiyYWkleRWZ1IpJ/d4EIP37o3wPhGPMqeQ5JT2VV2wpRERERERERERUDrGlEBEREREREZU6nJKeqPAYFCIiIiIiIqJSh93HiAqPAVEiIiIiIiIionKIQSGiUi4+Ph4+Pj7w8fHB7Nmzi7s6RERERERFwgwyo76IyiN2HyMq5XJycnDnzh0AwJMnT4q5NkRERERERFRaMChEREREREREpY7MiGMKydhQiMopBoWISjkvLy8IglDc1SAiIiIiKlKyf1/GyouoPOKYQkRERERERERE5RCDQlRmZWdno3bt2pDJZNizZ09xV6dcWr58OWQyGYKDg4u7KkRERERUxiimpDfWi6g8YlCIyqw1a9YgJiYGDRs2RL9+/dSmiYmJwfbt2zFt2jS0adMGdnZ2kMlkkMlkCA0NLbK6Hj16VCxXHwkJCVi9ejV69uwJb29vODk5wcrKCu7u7mjatCnGjh2LXbt24dWrV3qVqe9r6tSpBfIJDg6GTCbDwoULC7z3wQcfwM3NDREREdi7d6++u4OIiIiIiIiKAMcUojIpNTUVS5cuBQDMmzdPbbAlIiKi1LVgycrKwrx58/DNN98gIyOjwPuJiYlITExEdHQ0Nm3aBEdHR0ydOhUff/wx7Ozsiry+Dg4OmD59OubOnYv58+fj7bffhpkZY9FEREREVHjGnEqeU9JTecWgEJVJ69atQ1JSEmrWrIkBAwaoTaM8OLOZmRnq1asHe3t7REVFFVU1JUlMTERISAgiIyMBAPb29hg4cCA6dOiAmjVrwtHREUlJSYiJicHhw4dx6NAhpKamYtGiRejevTtatmypMe8PPvgAH374oc46VKxYUXK9J0yYgEWLFuGff/7B/v370bdvX8l5EBERERERkfExKERljlwux9q1awEAgwcP1tgypVq1ali+fDlatGiBZs2awcHBAaGhoSUyKCSXyzFgwAAxIBQSEoL169fDw8OjQNpOnTph/PjxePLkCZYvX47Vq1frzN/DwwMNGzY0drUBAM7OzujevTv27t2Lr7/+mkEhIiIiIjIKY44FxDGFqLxiPw4qcw4fPoyHDx8CAIYOHaoxXe3atfHRRx8hKCgIDg4ORVU9g3z11Vc4evQoAKBnz57Yt2+f2oCQskqVKmHFihU4fvw43N3di6CWmik+h4iICNy5c6dY60JEREREZYMMeT9ojfFiTIjKKwaFqMzZtWsXgLygj5+fXzHXpvCysrKwcuVKAICdnR02btwoaVyeVq1awdvb21TV00uPHj1gY2MDANi+fXux1oWIiIiIiIjyMChEZU54eDgAaB1DpzT5448/8PjxYwDAO++8o7OFUElkbW2Nxo0bAwAOHTpUvJUhIiIiojJBZuQXUXnEMYWoTImLi8P9+/cBAC1atCjeyhhJRESEuNytW7dirEnh+Pv74/Tp04iKikJmZqbYckhfcXFxWt9PSEgoTPWIiIiIiIjKHQaFqEw5deqUuNykSZNirIk0wcHBKrOhKbt8+bK43LRpU5OU//TpU1y9elVnujp16sDS0lLlb4qxjnRp1qwZAOD169e4dOkSAgICJNWxRo0aktITERERUdlmJpPBTGakKemNlA9RacOgEJUpyq1JSmM3K3USExPFZW0DRr98+VIcYDs/e3t7vPHGGxrXXbduHdatW6ezLvfu3YOXl5fOdOoofx53796VHBQiIiIiIlJmzG5fDAlRecWgEJUpz549E5crVKhQjDUxntTUVHHZ3t5eY7qDBw9i+PDhat8LCgrSu0WPqbi6uorLijGSpNAU8FJISEiAv7+/5HyJiIiIiIjKKwaFqEx5/vy5uFxWgkKOjo7icnp6OpycnIxexoIFC7Bw4UKj56tM+fNIT0+XvH716tWNWR0iIiIiKuXYUoio8Dj7GJUpyoMXZ2RkFGNNjMfNzU1cVm4Jld+wYcMgCILKqyRR/jzyj0tERERERERERY9BISpTlMfcUW41VJo1atRIXI6Oji7GmhSO8ufh4uJSfBUhIiIiojKBU9ITFR6DQlSmKAeFkpOTi7EmxhMUFCQu//7778VYk8JR/jxq1qxZjDUhIiIiIiIigEEhKmP8/PzE5Vu3bhVjTYyna9euqFSpEgBgx44dWruQlWTKn0eDBg2KsSZEREREVBbIZDKjvojKIwaFqExp3ry5OK7Q2bNni7k2xmFtbY3p06cDyBugedy4ccjNzS3mWkmn+DyqVKnClkJEREREVGjsPkZUeAwKUZliZWWFgIAAAEBUVFSRlBkaGio+XTDVDF7Tp09Hu3btAAC//vor+vfvj8TERK3rlLTuc4rPo3PnzsVcEyIiIiIiIgI4JT2VQb1790ZERASioqKQmpqqMqV7fnv27EFaWpr47xMnTqhdBoDKlSujW7duxq+wHiwsLLB792706tULUVFR2LdvHw4fPox33nkH7du3R82aNeHo6Ij09HTcvXsXx44dw86dO8X17ezstOb/9OlTXL16VWc9bG1t4e3tLbn+t2/fxsOHDwEAffr0kbw+EREREVF+MhivlQNbClF5xaAQlTkjRozAnDlzkJmZiX379mHEiBEa03700UeIjY1V+97GjRuxceNG8d9BQUFqg0LKU60rTx9vbB4eHoiIiMAnn3yCdevWIS0tDRs2bMCGDRs0ruPk5ISJEyfik08+0Zr3unXrsG7dOp11aNSoES5evCi16ti2bRsAwNXVFd27d5e8PhERERERERkfu49RmePm5oa+ffsC+P9ghClFRkYCACpUqIBRo0aZtCwbGxusWrUKd+/excqVK9G9e3d4eXnBwcEBlpaWcHd3R6NGjTBmzBj8/PPPSEhIwBdffAF7e3uT1ksXxecwduxYWFlZFWtdiIiIiKhskMmM+yIqj9hSiMqkKVOmYPv27Thy5AhiY2Ph6empNt39+/cLXdaxY8cAAFOnTtXaVc2YqlSpgunTp4sDUBsiODgYgiAYsVbqnThxArdu3YKVlRUmTpxo8vKIiIiIqHyQ/fufsfIiKo/YUojKpICAAPTt2xdyuRxLliwxWTmxsbGIjY2Fs7MzpkyZYrJySrNFixYBAMaMGcNZx4iIiIiIiEoQBoWozFq8eDEsLCywefNmxMXFmaQMRSuhyZMnw9nZ2SRllGZnzpzBn3/+CUdHRyxYsKC4q0NEREREZQinpCcqPHYfozKrTp062LRpE+7cuYMHDx6gevXqRi9j+PDhGD58uNHzLSuSkpKwYMECNG3aFJUrVy7u6hAREREREZESBoWoTGPApnh1796ds40RERERkUkYs4UPWwpRecXuY0RERERERERE5RBbChEREREREVGpYwbAzEhNfNhagsorHvtERERERERU6siM/F9RiY2NxYwZM1C3bl3Y29vD1dUVLVq0wPLly/Hq1SujlnXkyBGMGjUKPj4+sLe3h7OzM3x9fdG/f3+sW7cOaWlpWtd/9eoVli1bhhYtWsDV1RX29vaoW7cuZsyYgdjYWKPWlYqHTBAEobgrQURUWHFxcahRowYAIPZ/P6N6JXf9VszOklSO8CpFatUAK2tp6XOypZdhaSUtfVaG5CJkFSpJW8HCUnIZUrdd5iqxTgBgZm7a9IDkz0NmZSe9DFkJHP3AkFsKIVfiCgZst8QyDLs1krqO6T8/WREcI4Lkz6+ISP0MDdkOqdeGXEPKkPb8VmbI9UoqQ84PqfUy5NjNlUtcoSiuoUWwrwz6jtL/viQuPh41fBsAAB4+fGiSiVukUr7n+9HFAxXNjXPcJ8rlGPHiKQDTbuvBgwcxbNgwpKSov6f09fVFWFgYfHx8ClVOcnIyRo8ejV9//VVruujoaDRu3FjtezExMejevTtu376t9n0nJyds3boVPXv2LFRdqXixpRARERERERGVSqVpOvro6GgMGjQIKSkpcHBwwBdffIFTp07hr7/+wrhx4wAAt27dQo8ePZCammpwOS9fvkTnzp3FgFCfPn2wdetWnD59GmfPnsXevXsxZcoUrYGv1NRU9OjRQwwIjRs3Dn/99RdOnTqFL774Ag4ODkhJScGgQYNw8eJFg+tKxY9jChERERERERGZ2JQpU5CRkQELCwv8+eefaNWqlfhehw4dULt2bcyaNQu3bt3CypUrsXDhQoPKmTRpEs6fPw9ra2vs2rULISEhKu83b94cffr0wVdffQW5XH0ru+XLl+PWrVsAgGXLlmHmzJnie61atUJwcDCCgoLw6tUrTJ06FUePHjWorlT82FKIiIiIiIiISh2ZzLgvU4qKisLx48cBAGPHjlUJCCnMmDED9erVAwCsWbMGOTk5kss5ceIEfvrpJwDA559/XiAgpEwmk8HComA7kZycHHz99dcAgHr16mHGjBkF0rRu3Rpjx44FAERERODs2bOS60olA4NCREREREREVOoYq+tYUXQh279/v7g8evRotWnMzMwwYsQIAMCLFy8QHh4uuZy1a9cCAJydnTFx4kTpFQUQHh6Oly9fAgBGjhwJMw3jm40aNUpc3rdvn0FlUfFjUIiIiIiIiIjIhE6cOAEAsLe3R7NmzTSmCwoKEpdPnjwpqYzs7GxxHKHOnTvDxsYGACCXy/Hw4UPcv38fmZmZetc1f33ya968Oezs7AyqK5UcHFOIiIiIiIiISh0zyGBmpDY+yvkkJCToTC91drLr168DAHx8fNR22VKoW7dugXX0denSJTHo4+fnh5SUFMyfPx9btmzBixcvAABWVlZo164d5s6di+DgYLX5XLt2TW198rOwsICPjw8uX74sua5UcjAoRERERERERPQvf39/nWkEQdA7v8zMTCQmJgLQHUyqUKEC7O3tkZ6ejocPH+pdBqAazMnNzUXz5s0LTCefnZ2NI0eO4K+//sKSJUswe/bsAvnExcUByGvV5OLiorXMGjVq4PLly3j27BmysrJgbW0tqc5U/Nh9jIiIiIiIiEqd0jKmkPL08g4ODjrT29vbAwDS0tIklfP8+XNxeenSpbh9+za6deuGqKgoZGZm4unTp1i3bh2cnZ0hCAI+/vhjsbuZuvpKqash9aWSgS2FiIiIiIiIiP4VFRWFKlWqGC0/5XF8rKysdKZXtLbJyMiQVE56erpKmZ07d8Zvv/0Gc3NzAIC7uzvGjx+Phg0bIigoCLm5uZgzZw5CQkIgU5p+TVFfKXU1pL5UMrClENG/srOzUbt2bchkMuzZs6e4q1MowcHBkMlkGvsJA3kDzq1Zswb+/v5wcnKCTCaDTCbD22+/rZIuKSkJH330EerVqwdbW1sx3erVq3XW4+zZs5DJZHB1dVV5ckFEREREVFimmpK+SpUqqF69utaXFIoBn4G83xy6ZGVlAQBsbW0NLgfIay2kCAgpCwwMRN++fQHkjVt05coVtflIqash9aWSgUEhon+tWbMGMTExaNiwIfr161fg/ZycHPz++++YNm0aWrdujYoVK8LS0hIuLi5o2rQpZs6cibt37xZJXY8ePSoGZww1ePBgTJ06FWfPnlVp0qrs5cuXaNWqFVauXIkbN26ona1g4cKFGgNQLVq0QNeuXZGcnIyFCxcaXFciIiIiovxKS/cxR0dHcVmfLlaKFj/6dN/SVI67uzuaNGmiMW3Xrl3F5bNnz6rNR0pdAen1pZKB3ceIkNdvdunSpQCAefPmFQi2PHv2DPXq1UNSUlKBdV++fIno6GhER0fj66+/xrJlyzBlypQiqbehTp06hd27dwMAevTogalTp6JSpUqQyWRwcnIS033zzTfi4HSzZs1Cr169xMHm9G1SO3/+fPzxxx/4/vvvMXPmTNSoUcO4G0NEREREVILZ2NjAzc0NSUlJ4iDOmiQnJ4uBFqn3zcrpdbVmUk777NkzlfeqV6+OM2fOID09HS9evNA62LRiMGx3d3cOMl1KMShEBGDdunVISkpCzZo1MWDAgALvZ2VliQGhxo0bo3fv3ggICEClSpXw8uVLHDp0CP/973+RmZmJqVOnwtbWFu+9915Rb4bo6NGjWt8/cuQIAMDc3Bzbtm1TCQSpS9e8eXMxaCZV69at0bJlS5w+fRrLly/H119/bVA+RERERETKZP/+Z6y8TKl+/fo4fvw4YmJi8Pr1a43T0t+4cUNcrlevnqQyGjRoIC7L5XKtaZXfz1+X+vXr45dffhHr07JlS7V5vH79Gnfu3DGorlRysPsYlXtyuRxr164FkNelysys4Gkhk8nQuXNnREZGIjo6GgsXLsRbb72Fpk2bon379li2bBmOHj0q9qOdNWuWxi5ZJUF8fDwAoFKlShoDQsrpfH19C1XekCFDAAChoaFISUkpVF5ERERERKVNYGAggLzuVufPn9eYLiIiQlxu06aNpDI8PT1Rs2ZNAMD9+/chCILGtIpgDgBUq1ZNbV3z1ye/c+fOia2apNaVSg4GhajcO3z4sNjscejQoWrTVKtWDX/++afGKDkABAQE4MMPPwSQ16Xs8OHDxq+skSgGhLO0tDRKOl0GDRoEc3NzpKamit3WiIiIiIgKQyYDzIz0KsRQnXpRnsxl8+bNatPk5ubixx9/BAC4uLigffv2kstRjI2akpKCv/76S2O6vXv3isvKQSAgb9IaZ2dnAMCWLVs0BpdCQ0PF5T59+kiuK5UMDApRubdr1y4AQO3ateHn51eovJQv3MrR96KmafYxxeDUW7ZsAQDExsaKf1O8lAexjo2NBZD3ZaCcRtusZup4eHigbdu2AIDt27cXevuIiIiIiErLQNMA4O/vL94Pb9y4EZGRkQXSrFy5EtevXwcATJkypcCDWeX79FGjRqktZ+rUqeLsYdOnT1fbSv/nn38Wh5vo0aNHgbGLrKysMHnyZAB5s5OtWLGiQB6RkZHYuHEjACAoKAgtWrTQtOlUwjEoROVeeHg4AGhtBaQv5SkZ1U3/WJ4p9u/x48dVZikgIiIiIioP1qxZA1tbW7x+/RpdunTBkiVLcPr0aYSHh+P999/HrFmzAOQN3TBjxgyDyqhZsyb+85//AACuXLkCf39/bN68GefPn0d4eDgmTZokBpScnJzw1Vdfqc1n5syZ4hASs2bNwvvvv4/w8HCcPn0aS5YsQZcuXfD69WvY2tpi9erVBtWVSgYONE3lWlxcHO7fvw8ARoluK/e5LYmDrV25cgVA3gxrv/76K6pWrYo//vhDJc0bb7whpuvatSsePXqE3r174/PPPxfT2NvbSy7b398fAJCdnY3IyEh06tRJ0vq6ZmpISEiQXCciIiIiKr2M2cLH1C2FAKBJkybYuXMnhg0bhpSUFHzyyScF0vj6+iIsLExlenmpZs6ciefPn2Pp0qW4efMmxowZUyCNh4cH9u/fj9q1a6vNw9HREWFhYejevTtu376NH374AT/88INKGicnJ2zduhWNGzc2uK5U/BgUonLt1KlT4nKTJk0KlVdCQoLYP9jd3d2gPsD6Cg4O1jpwnCYNGzYEAHFaSUtLS/Fv6tIpmqy6uLioTQcACxcuxMKFC3WW3axZM3H5/PnzkoNCnMqeiIiIiEq7Xr164fLly1izZg3CwsIQFxcHKysr+Pj4YMCAAZg4cSLs7OwKXc6SJUsQEhKCdevW4fjx40hISICNjQ18fX0REhKCSZMmieMGaeLj44Po6Gh888032L17N2JiYpCdnY0aNWqge/fumDJlCjw9PQtdVypeDApRuabc+sTDw8PgfARBwPvvvy/OOPbpp5+KfXkpj/L+vXv3bjHWhIiIiIjKgryWQsaakr7oeHp6YtWqVVi1apWk9aQ+GG7VqhVatWoltXoq7O3tMWvWLLFrG5U9DApRufbs2TNxuUKFCgbns3jxYhw8eBBA3mDTEyZMKHTdyhobGxvY2toiIyMDjx8/lry+YoY4TRISEsQuakRERERERKQbg0JUrj1//lxcNjQotHXrVnz66acA8sbj2bZtG8zMOIa7OhUqVEBGRoZBA01Xr17dBDUiIiIiotJKZsSp5E09JT1RScVfrlSuKXfxysjIkLx+WFgYRo8eDUEQULlyZRw+fBiVK1c2ZhXLFMU+zj+9JhERERGRVGZGfhGVRzz2qVxzd3cXl5VbDenj6NGj6N+/P3JyclChQgX88ccf8Pb2NnYVy4zc3Fy8fPkSwP8PdE1ERERERETFh0EhKteUg0LJycl6rxcVFYVevXohMzMTDg4OOHToEN58801TVLHMePnyJXJzcwEANWvWLObaEBEREVFpJzPyi6g8YlCIyjU/Pz9x+datW3qtc/nyZXTr1g1paWmwsbHBwYMHERAQYKoqlhnK+7dBgwbFWBMiIiIiIiICGBSicq558+biuEJnz57Vmf7WrVvo0qULkpOTYWlpiV9++QXBwcEmrmXZoLx/GUQjIiIiokKTySAz0osjTVN5xaAQlWtWVlZigCIqKkpr2gcPHqBTp0548uQJzM3NsW3bNnTv3l1ymaGhoeKXz8KFCw2pdqmk2L81a9ZEnTp1irk2RERERFTasfsYUeFxSnoq93r37o2IiAhERUUhNTUVjo6OBdIkJSWhU6dOePjwIQBgxowZqFu3Lq5evaox3woVKqBatWomq3dpIggCwsPDAQB9+vQp5toQERERERERwJZCRBgxYgSsra2RmZmJffv2qU1z5coV3L59W/z3smXL4Ofnp/U1d+5ctXkppmUHADc3N+NuTAl17NgxxMXFAQCGDRtWzLUhIiIiorKALYWICo9BISr33Nzc0LdvXwDAtm3bTF5eZGQkgLyWRKNGjTJ5eSWBYr+2aNECzZs3L+baEBEREREREcDuY0QAgClTpmD79u04cuQIYmNj4enpqfJ+cHAwBEEwSlnHjh0DAEydOlVtVzVjOHr0qNb3Q0NDERoaqjOf+/fvF7ouqamp2LVrFwBg+vTphc6PiIiIiAjA/w8SbaS8iMojthQiQt5sWH379oVcLseSJUtMVk5sbCxiY2Ph7OyMKVOmmKyckmTt2rV48eIF6tevj4EDBxZ3dYiIiIiojDCTGfdFVB4xKET0r8WLF8PCwgKbN28Wx78xNkUrocmTJ8PZ2dkkZZQk6enpWLVqFQBg+fLlMDPjJYeIiIiIiKikYPcxon/VqVMHmzZtwp07d/DgwQNUr17d6GUMHz4cw4cPN3q+JVVsbCwmTJgAV1dXdO/evbirQ0RERERliEwmg8xITXxkuWwqROUTg0JESspTwKYo1K9fHwsXLizuahAREREREZEaDAoRERERERFRqSOT5b2MlRdRecQBPoiIiIiIiIiIyiG2FCIiIiIiIqJShy2FiAqPQSEiKnvkckD+Wr+0EgcnlLm4G1AhiV5nS15FyJG4jpm59DLSkqWtkPJCchlIfSkpufA4XnoZr15JS29vL72MbImfh1wuuQghLc3kZchcXaWt4FZRchmwsZWW3txSehlCrrT0hhy7r9KlpX+t5zVKmZWVtPRS9y0AWEi8NTSkDGuJ6zg4Si/DRuJ5K3W7AcgcK0hcQXoDfZmLh6T0giGzfOZKvDYYcg6aS92/ggFlSDs/ZBYSzydAetSgKGZdNeC4KlNkMsgYFSIqlHJ+FSEiIiIiIiIiKp/YUoiIiIiIiIhKHXYfIyo8thQiIiIiIiIiIiqH2FKIiIiIiIiISh2ZEccUMtrYRESlDINCREREREREVOqw+xhR4bH7GBERERERERFROcSWQkRERERERFTqmMlkMDNSEx9j5UNU2rClEBERERERERFROcSWQkRERERERFTqcEwhosJjSyEiNbKzs1G7dm3IZDLs2bOnuKtT7EaNGgWZTAYvLy/J606YMAEymQwjR440fsWIiIiIiIjIYAwKEamxZs0axMTEoGHDhujXr1+B92NjY7F+/XqMGzcO/v7+8PT0hJ2dHWxtbVGjRg2EhITgp59+Qk5Ojsnrev/+fXE6zvv375u8PHW8vLwgk8kQGhpa4L3Zs2fDysoKP/30E86fP1/0lSMiIiKiMkkGmXgfXOgX2FSIyicGhYjySU1NxdKlSwEA8+bNg0xNW9L169fjvffew4YNG3D27Fk8ePAAGRkZyMzMRFxcHA4ePIgRI0agWbNmuHfvXlFvQolSs2ZNjBw5EoIg4NNPPy3u6hARERFRGSEzM+6LqDzimEJE+axbtw5JSUmoWbMmBgwYoDaNmZkZGjVqhMDAQDRu3BhVqlRBpUqVkJqaijt37mDz5s04deoUrly5gk6dOuHy5cuwt7cv4i0pOWbMmIH169fj0KFDOH/+PJo1a1bcVSIiIiIiIir3GA8lUiKXy7F27VoAwODBg2Fmpv4UmT9/Pi5evIi1a9fi3XffRY8ePdC8eXO0b98e7777Lk6ePIkpU6YAAO7evYuNGzcW2TaURHXq1EHTpk0BAP/973+LuTZEREREVCYYq+uYMUesJiplGBQiUnL48GE8fPgQADB06FCN6SwsdDey+/jjj8Xl48ePF75ypZxif+7evRupqanFXBsiIiIiIiJiUIhIya5duwAAtWvXhp+fX6HycnR0FJczMzMLlZcxyOVybNmyBT179kTVqlVhbW0NNzc3BAYGYtWqVcjIyDBp+YoBu1+9eoVff/3VpGURERERUdmnaOBjrBdRecSgEJGS8PBwAEDLli0LndeOHTvE5bp16xY6v8J48OABmjVrhlGjRiEsLAwJCQnIzs7G8+fPcfLkScyYMQNvvvkmbt26ZbI6eHp6onLlygCAQ4cOmawcIiIiIiof8oI5xupCVtxbQ1Q8ONA00b/i4uLEKd1btGhhUB7Jycm4f/8+fvrpJ3FsIisrK3zwwQfGqqZkSUlJCAwMxMOHD2FtbY1x48YhKCgIXl5eSEtLw59//ok1a9YgJiYGb731Fi5cuABnZ2eT1MXf3x8HDhxARESE5HXj4uK0vp+QkGBotYiIiIiIiMolBoWI/nXq1ClxuUmTJnqvN2rUKGzZskXte3Z2dvjpp59Qq1atQtdPEy8vLwiCoPH9yZMn4+HDh/D09ER4eDjeeOMNlfeDg4MxYMAAtG3bFnfv3sWyZcvwxRdfSKqDIpimS7NmzXDgwAHEx8fjyZMnqFSpkt5l1KhRQ1KdiIiIiKhsM2a3L7YUovKK3ceI/qXcEsXDw6PQ+b3zzju4ceMG+vbtW+i8DHX//n3s3LkTALB27doCASGFJk2aYMKECQCA0NBQk9VHeb/evXvXZOUQERERERGRbmwpRPSvZ8+eicsVKlTQe70vvvgCH330EQAgJSUFly9fxvr167Fjxw7ExcVh06ZNqF27ttHrq4+wsDDI5XLY2dnhrbfe0pq2Xbt2WLZsGR49eoQHDx6gZs2aRq+Pq6uruPz48WNJ6ypmhdMkISEB/v7+BtWLiIiIiEofM5kMZkZq4mOsfIhKGwaFiP71/PlzcVlKUKhatWqoVq2a+O/WrVtj3LhxmDBhAr7//nsEBAQgPDwcjRo1Mmp99XHu3DkAeTN+WVjof7o/fvzYJEEh5f2anp4uad3q1asbuzpERERERETlGruPEf3LxsZGXC7s9Ozm5ub4+uuvUaNGDSQnJxfbQNNPnz41aL1Xr14ZuSZ5lPerpaWlScogIiIiovKBU9ITFR5bChH9y93dXVx+/vw5HB0dC5WflZUVunXrhvXr1yMyMhLx8fEqLYqKglwuBwBUrFgR4eHheq+naeyhwlJujeXi4mKSMoiIiIiofJAhbzp5Y+VFVB4xKET0L+WgUHJyMjw9PY2a54MHD4o8KOTm5gYASE1NRb169WBubl6k5eeXnJwsLpuiexoRERERERHpj93HiP7l5+cnLt+6dcsoecbHx4vLDg4ORslTiiZNmgAAsrKyxPGFipNiv1pbW8PHx6eYa0NEREREpRm7jxEVHoNCRP9q3ry5OK7Q2bNnC51feno6Dh06BACwtbUtliBIr169xCa1q1evLvLy81Ps1yZNmnBMISIiIiIiomLGoBDRv6ysrBAQEAAAiIqK0pguMTERv/zyi9a8MjMzMWbMGHGg5/79+8PW1rZAuqNHj0Imy+sLPWrUKMMrr0GdOnUwYMAAAMCOHTuwatUqrenv3buH7du3G70eQF5rpcuXLwMAunTpYpIyiIiIiKgcMWYrIbYUonKKYwoRKenduzciIiIQFRWF1NRUtYNNp6WloX///vDx8UG/fv3g7++PatWqwdraGomJiYiKisLGjRtx9+5dAHlT1i9durSoN0W0bt06nDt3Dnfv3sWMGTPw66+/YsSIEWjQoAGsra2RlJSES5cu4ffff8fff/+NPn36YPDgwUavx7Fjx5CTkwMA6NOnj9HzJyIiIqLyRWYmg8zMSANNGykfotKGQSEiJSNGjMCcOXOQmZmJffv2YcSIERrTxsTE6Az2tGrVCj///DOqVKmi9n3lKdoVg0Ibm6urK06ePImBAwfi+PHjOHbsGI4dO6YxvZOTk0nqsW3bNgBAgwYN0LhxY5OUQURERERERPpjUIhIiZubG/r27Yvt27dj27ZtaoNCNWvWxPHjx/Hnn38iKioKsbGxePLkCVJTU+Hg4ICaNWuiefPmGDBgALp27ap1mszIyEgAgIWFBSZNmmSy7apcuTKOHTuGsLAwbN++HZGRkXj8+DFycnLg4uKC2rVro1WrVggJCUG7du2MXn5mZib27t0LAPjwww+Nnj8RERERlT/GHCCaA01TecWgEFE+U6ZMwfbt23HkyBHExsYWmJrezMwMgYGBCAwMLHRZihY7w4cPh5eXV6Hz06VHjx7o0aOH5PVCQ0MRGhpqcLl79uxBSkoK3NzctLa+IiIiIiIioqLDgaaJ8gkICEDfvn0hl8uxZMkSk5WTlZWFM2fOwNzcHHPnzjVZOcUtNzcXixcvBgDMnDkTDg4OxVwjIiIiIioLzGQyo76IyiMGhYjUWLx4MSwsLLB582bExcWZpIyoqChkZmZiyJAh8Pb2NkkZJcHu3btx/fp11KxZE5MnTy7u6hAREREREdG/2H2MSI06depg06ZNuHPnDh48eIDq1asbvYy2bdtCEASj51vSyOVyLFiwAB06dICtrW1xV4eIiIiIygiOKURUeAwKEWkwfPjw4q5CmTBkyJDirgIRERERlUEymUzrpC5S8yIqj9h9jIiIiIiIiIioHGJLISIiIiIiIip1ZDBi9zHjZENU6jAoRERlz+ssICdLv7QWlpKyFjLSJFdHZmYurYxcueQyIHEdmXNF6WVIVaGS9HUk7itZU2mfHwAIWRnSyrCWPhaWIJf4Gb7OllyG5GbuMtM3DpY5OEtfSeJnbtB2SC3D3IDbo6LYDkgchy43V3oRUo8rQ8bGM5O27VKvoYYx4Oeg5HOwKH5yFkUZRTAeoiHnh9RjUeJxCKBoPkN2YSKiIsagEBEREREREZU6HFOIqPAYFCIiIiIiIqLSx4izj7H/GJVXHGiaiIiIiIiIiKgcYkshIiIiIiIiKnXYfYyo8NhSiIiIiIiIiIioHGJQiMqM+Ph4+Pj4wMfHB7Nnzy7u6hARERERkQnJzIz7IiqPeOhTmZGTk4M7d+7gzp07ePLkSXFXh4iIiIiIqIDY2FjMmDEDdevWhb29PVxdXdGiRQssX74cr169MkmZr169Qq1atcQud15eXlrTBwcHi2l1vah045hCREREREREVOqUxjGFDh48iGHDhiElJUX826tXr3Du3DmcO3cOGzZsQFhYGHx8fIxa7vz583Hv3j2j5kllA4NCVGZ4eXlBEITirgYRERERERUFMwBmRgrmFEEfmujoaAwaNAgZGRlwcHDAnDlz0L59e2RkZGDHjh1Yv349bt26hR49euDcuXNwdHQ0WrmrV6+GjY0NLC0tkZqaqve6zZs3x+bNm41SDyqZGBQiIiIiIiIiMrEpU6YgIyMDFhYW+PPPP9GqVSvxvQ4dOqB27dqYNWsWbt26hZUrV2LhwoWFLlMul2PcuHGQy+VYsGABNm7cKCkoZG9vj4YNGxa6HlRycUyhciA7Oxu1a9eGTCbDnj17irs6VI4IggA/Pz/IZDI+YSAiIiIiI5MBMiO9YNruY1FRUTh+/DgAYOzYsSoBIYUZM2agXr16AIA1a9YgJyen0OWuWbMG58+fR506dTgZD6nFoFA5sGbNGsTExKBhw4bo16+f3uvl5uaiVatWRTqI2NGjRyWVlZCQgNWrV6Nnz57w9vaGk5MTrKys4O7ujqZNm2Ls2LHYtWtXgQHb7t+/r/fAaRxUTTvlfXn//n2V92QyGebOnQsAmDt3LtLT04uhhkRERERExWv//v3i8ujRo9WmMTMzw4gRIwAAL168QHh4eKHKjI2Nxfz58wEA3333HaysrAqVH5VNDAqVcampqVi6dCkAYN68eZICGd9++y1Onz5tqqoVSlZWFmbOnAlvb29MmzYNYWFhuHv3LlJTU5GTk4PExERER0dj06ZNGDRoECpXroz58+ebbDR/0mzgwIGoU6cOEhIS8M033xR3dYiIiIiojDDGQ96ieuB74sQJAHndsZo1a6YxXVBQkLh88uTJQpX54YcfIj09HcOHD0dwcHCh8qKyi2MKlXHr1q1DUlISatasiQEDBui9Xnx8PD755BPIZDK4ubkhMTHRhLWUJjExESEhIYiMjASQd2EdOHAgOnTogJo1a8LR0RFJSUmIiYnB4cOHcejQIaSmpmLRokXo3r07WrZsiWrVquHKlSsay/Dz8wPAgdWMwczMDNOmTcP48eOxYsUKTJ48GTY2NsVdLSIiIiIq7cxkRhxo2rRBoevXrwMAfHx8YGGh+Wd43bp1C6xjiB07duB///sfKlSogJUrVxqcz40bNxAQEICbN28iMzMTFStWRLNmzdCvXz8MHjwYlpaWBudNJQODQmWYXC7H2rVrAQCDBw+GmZn+DcMmTpyI1NRUjBkzBnfu3EFERISpqimJXC7HgAEDxIBQSEgI1q9fDw8PjwJpO3XqhPHjx+PJkydYvnw5Vq9eLb5naWmp14BpHFjNOAYMGIBJkybh2bNn2LFjB0aNGlXcVSIiIiIiUishIUFnmurVq+udX2ZmpviQXdd6FSpUgL29PdLT0/Hw4UO9y1CWnJyMqVOnAgC+/PJLuLu7G5QPADx58gRPnjwR/x0fH4/4+HgcOHAAS5cuxZ49e8RxkKh0YlCoDDt8+LB4IRk6dKje6+3duxf79+9HxYoVsWzZMknjEJnaV199haNHjwIAevbsiX379ukMdlWqVAkrVqxAv379CnVBJMO5urqiW7duOHjwIDZu3MigEBEREREVnjhItJHy+pe/v7/O5IIg6J218mxfDg4OOtMrgkJpaWl6l6Fs5syZePLkCVq1aoVx48YZlIeZmRk6duyI7t27o1GjRnBzc0NqaiouXLiA77//HtevX8e1a9fQvn17REVFoWbNmgaVQ8WPYwqVYbt27QIA1K5dW+wOpUtKSgomTZoEAFi+fDnc3NxMVj+psrKyxKaPdnZ22Lhxo6TWT61atYK3t7fR6pObm4uff/4Z3bt3R+XKlcUBrtu3b49vv/0W2dnZatfr2bMnZDIZWrZsqfZ95cG2XV1dkZubWyDN48ePxTTfffed2nzkcjm2bNmCnj17omrVqrC2toabmxsCAwOxatUqZGRkaNy24OBgyGQyse/x7du3MXHiRNSuXRt2dnZqB5XWRRFcPHnypMFPPYiIiIiISpvMzExxWZ/Bnq2trQFA6/26JseOHcOmTZtgYWGB7777zuCxkvbu3YsjR45g+vTp6NixIxo3boy2bdtiypQpuHTpEkaOHAkgryWRolUSlU5sKVSGKUar1xR8UOfjjz/Go0eP0K5duxLXmuOPP/7A48ePAQDvvPOO2i5jReX58+cICQkpMPhbYmIijh49iqNHj2Lt2rU4dOgQPD09VdIEBQUhLCwM58+fR1paWoGnBcpd9ZKTk3H58mU0btxYYxp1g8Y9ePAAISEhuHTpUoF6nzx5EidPnsS6desQFhYGX19frdv666+/YujQoYWeOUxxHAqCgN9//93gpxZERERERAAgM5NBZqSxgJTziYqKQpUqVYySLwCV8TQ1PThWlpWVBQCwtbWVVE5WVhbee+89CIKAKVOm4M0335RWUSUuLi4a37O0tMSGDRtw+vRp3Lx5E/v27UN8fDyqVatmcHlUfNhSqIyKi4sTW3K0aNFCr3UiIyPx3XffwdLSEuvWrTNh7QyjHAjp1q1bsdVDLpejZ8+eYkAoKCgIu3fvxrlz53DgwAG8/fbbAPIGhuvYsWOBZp+KIM7r16/FWQiUKbrHafq38t8qVaqkMhgdACQlJSEwMBCXLl2CtbU1Jk6ciN27d+Ps2bMIDw/HnDlzYGdnh5iYGLz11lt4+fKlxm198OABhg0bBjs7O3z55Zc4efIkTp8+jf/+9796NX1V5uvrK365GDJGVVxcnNaXPn2/iYiIiKgMUXQfM9brX1WqVEH16tW1vqRwdHQUl/XpEqZ4GCv1fvuLL77AzZs3UaNGDXz22WeS1pXKwsICY8eOFf9dUsagJenYUqiMOnXqlLjcpEkTnelzcnIwbtw4CIKAjz76CPXr1zdl9TQKDg7W2D/38uXL4nLTpk2LqkoFfPfdd+JA1yNGjEBoaKjYLLNZs2bo1asX5s6di8WLF+POnTtYtGgRli5dKq7ftGlTODo6IjU1FUePHlUJcGVlZeH06dMAgF69euHgwYM4evRogSaZiouu8pSVCpMnT8bDhw/h6emJ8PBwvPHGGyrvBwcHY8CAAWjbti3u3r2LZcuW4YsvvlC7rffu3UPVqlURGRmp0k84ICBAXPby8tKrT7VMJkOTJk0QHh6O8+fP60yfX40aNSSvQ0RERERU3GxsbODm5oakpCTExcVpTZucnCwGhaTe/yp+c3Tq1AkHDx5Um0aRd3p6Onbs2AEA8PDwQIcOHSSVBUDlN2N8fLzk9alkYFCojFK+2OjTzWrp0qX4559/8MYbb+DTTz81ZdUMphixH4DWAaNfvnypccwae3v7AkESqb755huxDmvXrlXbT/ezzz7D3r17cePGDaxfvx7/+c9/xL7B5ubmCAwMxKFDhwq0Ajpz5gwyMzPh7OyMadOm4eDBgzh27Bhyc3PF8ZOePn0qTk+ZPyh0//597Ny5EwCwdu1ajdvapEkTTJgwAcuWLUNoaKjGoBCQN2OBsQaOUxyL9+7dgyAIBvdxJiIiIiKSyYzYfczE96X169fH8ePHERMTg9evX2uclv7GjRvistRZvRRd0zZv3ozNmzdrTZuYmIjBgwcDyPtNYUhQiPfyZQO7j5VRz549E5crVKigNe3t27fFoMDatWsl910tKsqj9tvb22tMd/DgQfj5+al9jR49ulB1ePTokRiQGThwoEpTUGUWFhZiWcnJybhw4YLK+4pgjmJcIQVFC6DAwEC0bt0atra24rhC+dMABccTCgsLg1wuh52dHd566y2t29KuXTtxmx48eKA2jZWVFQYMGKA1HylcXV0B5LWIevHihaR1Hz58qPUVFRVltHoSERERERlTYGAggLwWOtpazSvf67dp08bk9SqMa9euictVq1YtxppQYTAoVEY9f/5cXNYVFHr//feRmZmJfv36oXv37qaumsGUAzCFHfTYUFevXhWXlbtQqaP8vvJ6gOZxhRQth4KDg2FtbS0Ozqzcokix7O7uXqCb37lz5wAAr169goWFhThDmbpXz549xfUUA3jnV7t2bZWB8QpL+ViU+hnq6tdtzMEAiYiIiKgUMNGYQqagGHcUgMZWPLm5ufjxxx8B5A303L59e0llCIKg86WYBMfT01P8m7oxTHV5/fo1Nm3aJP5b8cCZSh8Ghcoo5R/y2qYy3LRpE8LDw+Ho6Ig1a9YURdUM5ubmJi4rt4TKb9iwYQUufsaiHGzT1S2vcuXKatcD8sYeUgwcp7gIZ2dni2MVKYJGiv8rX6i1jSf09OlT3RuhxqtXr9T+XVdAUSrlY9HS0tKoeRMRERERlVT+/v5o27YtAGDjxo3ifb+ylStXir0SpkyZUuB++ejRo+IDXlPOFB0eHq61VX9OTg7effddsa69evXi+J+lGMcUKqOUx9x5/vy5xm5OisHIgoKCcPz4cbVplAMNisHI7O3t0atXL2NVVy+NGjXCX3/9BQCIjo6Gt7d3kZafX2H60FpYWKBNmzb4448/xIDP2bNnkZGRAWdnZ3FwcEXgRzGu0PPnz8VmmuqCQnK5HABQsWJFhIeH610fTWMPmZub652HPpSDY87OzkbNm4iIiIjKGTMARhpTqCiaS6xZswZt2rRBRkYGunTpgk8++QTt27dHRkYGduzYgR9++AFA3qy9M2bMMH2FNNiyZQtCQkIQEhKC4OBg1KlTB05OTkhLS8P58+fxww8/iL9JPDw8SnzjAtKOQaEySjkolJycLDYTzC8rKwsA8Ntvv+G3337Tma9iMDJPT88iDwoFBQVh1apVAIDff/8d/fv3L9Lygf8fEwcAnjx5ojWtcpcs5fUUgoKC8Mcff4jjCimCQ4GBgWIwpmXLlrCxsRHHFbpz547Y8in/eELA/7emSk1NRb169Ywe1Cms5ORkAHlfHsbslkZERERE5Y+i1Yyx8jK1Jk2aYOfOnRg2bBhSUlLwySefFEjj6+uLsLAwjQ/1i0paWhq2bduGbdu2aUzj5+eHHTt2FHoiHype7D5WRvn5+YnLt27dKsaaGE/Xrl1RqVIlAHktlrR1ITOVhg0bistnzpzRmlZ54GPl9RTyjyuk6BamHOzJP66QIk3FihXRoEGDAnkqWhhlZWWJ4wuVJIpjUV3diYiIiIjKul69euHy5cuYNm0afH19YWdnBxcXFzRv3hxLly5FdHQ0fHx8irWOs2fPxldffYWBAweiYcOGqFSpEiwtLeHg4ABvb28MGjQIu3fvRnR0dIExTqn0YVCojGrevLnYEuPs2bMa092/f1/nYGTK3ZQUf7t//76pN6EAa2trTJ8+HUDeIMXjxo1Dbm5ukdahatWq4tSQu3btUpk5TJlcLkdoaCiAvHF5mjZtWiBN8+bNxVnUDh8+jFOnTgEo2AJIeVwhRWuidu3aqX2a0atXL/Hvq1evlrJpJpeSkoKbN28C0D1INxERERGRTmYy476KiKenJ1atWoWbN28iPT0dycnJOHv2LGbNmgU7OzuN6wUHB4u/xxS/NaRS/P7T9nuuXr16mDp1Knbu3IkrV67g8ePHyM7ORmpqKmJiYrBjxw7079+/xPVKIMMwKFRGWVlZiT+8i2qq7tDQULEJ58KFC01SxvTp08WR7X/99Vf0798fiYmJWtdRdFkylgkTJgDIG+x68uTJatN89tlnYj/bcePGwdraukAaS0tLtG7dGkDeYHPp6ekq4wkpKIJyf//9tziLmbrxhACgTp064hTyO3bsELvbaXLv3j1s375daxpjOXfunNj1rUuXLkVSJhEREREREWnGoFAZ1rt3bwB5QaHU1NRiro1xWFhYYPfu3fD39wcA7Nu3D2+88QbGjRuHbdu24cSJE7h06RJOnTqFn3/+Ge+99x68vLzE9bVF3vU1fvx4tGrVCkDedJIdO3bEL7/8ggsXLiAsLAz9+vXDokWLAADe3t749NNPNealCO68fPkSgOp4QgotW7aEtbU1UlNTtY4npLBu3TrUqlULADBjxgwEBQVh48aNOH36NKKjo3HkyBGsXLkSnTt3ho+PD3755RfDdoREikHCK1asiMDAwCIpk4iIiIjKsFI0JT1RScWBpsuwESNGYM6cOcjMzMS+ffswYsQIk5anPN248vTxxubh4YGIiAh88sknWLduHdLS0rBhwwZs2LBB4zpOTk6YOHGi2sHcpDI3N8dvv/2GkJAQnDx5En///Tf+/vvvAunq1auHQ4cOiVPPq6Opq5gyGxsbtGzZUhxPyNXVVWXMqPxcXV1x8uRJDBw4EMePH8exY8dw7NgxjemdnJw0vmdMihZJgwYN4nT0RERERFRoMhkgM1IzB8aEqLxiS6EyzM3NDX379gUAraPGG0tkZCSAvDF0Ro0aZdKybGxssGrVKty9excrV65E9+7d4eXlBQcHB1haWsLd3R2NGjXCmDFj8PPPPyMhIQFffPGFOIZPYbm6uuLYsWP48ccf0a1bN3HwNTc3NwQHB2Pt2rW4ePGixlnfFFq0aKHSeklTCyDlv2saT0hZ5cqVcezYMfz2228YOnQoatWqBTs7O3HftG7dGjNmzEBERAQ2bdqk93YbKjIyEvfu3QMAfPDBByYvj4iIiIiIiHSTCYr+KFQmnTlzBi1btoS5uTnu3LmjM0hRGF5eXoiNjcVnn32G+fPnm6wcKn3effddbNy4EV27dsXvv/9ukjLi4uJQo0YNAEDsvh9Q3aOifitaSGu1JORkS60aZGbSBuETcuWSy4DEdWT2ztLLkMrSSvo6EveVTOLnBwBCVobuRMplWNtKL0Mu8TN8Lf24kvxI01iPUrUV4WDAcSXxMzdoO6SWYW5AQ+qi2A5IvGUzZDIGqceVIbeRZtK2Xeo11DAGNBGQfA4WRTOEoiijCH46GHJ+SD0WJR6HAIrmM5RchgF1knCNi4uPRw3fvFljHz58iOrVq0svz8iU7/luvdUc1ewKjt1piPhXWfA9lDd7b0nZVqKiwJZCZVxAQAD69u0LuVyOJUuWmKyc2NhYxMbGwtnZGVOmTDFZOVT6PHjwAD/++COAvAG4iYiIiIiIqGRgUKgcWLx4MSwsLLB582bExcWZpAzFmDWTJ0+Gs3MRtECgUmPJkiXIycnBgAEDOBU9ERERERmPmQwyI72Kckp6opKEA02XA3Xq1MGmTZtw584dPHjwwCRNIYcPH47hw4cbPV8q3QRBgKenJxYsWIAxY8YUd3WIiIiIiIhICYNC5QQDNlQcZDIZPv744+KuBhERERGVRcacSp7Tj1E5xaAQERERERERlT7G7PbF7mNUTnFMISIiIiIiIiKicogthYiozDHzaQazalX1Syx5SmHp9ZG8kiFTPEtlyHbkSp3ut2Q+cZNJ3njpn4fkLZe6b4GiaeYuz5GUXDBgCnSZ1O0oiunJDZouXiJDpsOWzIDjSuoqggHT3ktmwLEudf8acj5JXceA80Pydhjy/SF1O4qijBKrrGxH2SGTyaR/j2jJi6g8YkshIiIiIiIiIqJyiC2FiIiIiIiIqPQxgxHHFDJONkSlDYNCREREREREVAoZcfYxdg+kcorxUCIiIiIiIiKicogthYiIiIiIiKjU4UDTRIXHlkJEpUx8fDx8fHzg4+OD2bNnF3d1iIiIiIiIqJRiSyGiUiYnJwd37twBADx58qSYa0NEREREVEzMZEYcaJothah8YkshIiIiIiIiIqJyiC2FiEoZLy8vCIJQ3NUgIiIiIipWHFOIqPAYFCIiIiIiIqLSh93HiAqN3ceozMjOzkbt2rUhk8mwZ8+e4q5OiRMaGio+Tbl//36RlCkIAvz8/CCTybB58+YiKZOIiIiIiIj0w6AQlRlr1qxBTEwMGjZsiH79+hV4/+jRo2JQRNdr4cKFJq2rcl30kZCQgNWrV6Nnz57w9vaGk5MTrKys4O7ujqZNm2Ls2LHYtWsXXr16ZdJ6q3P//n2NwSaZTIa5c+cCAObOnYv09PQirx8RERERlVEymXFfROUQg0JUJqSmpmLp0qUAgHnz5pWZPsFZWVmYOXMmvL29MW3aNISFheHu3btITU1FTk4OEhMTER0djU2bNmHQoEGoXLky5s+fXyzBIU0GDhyIOnXqICEhAd98801xV4eIiIiIiIj+xTGFqExYt24dkpKSULNmTQwYMEBn+k2bNqFFixYa3/fw8DBm9QySmJiIkJAQREZGAgDs7e0xcOBAdOjQATVr1oSjoyOSkpIQExODw4cP49ChQ0hNTcWiRYvQvXt3tGzZUiW/UaNGYdSoUUW+HWZmZpg2bRrGjx+PFStWYPLkybCxsSnyehARERFR2SKTySAz0lhAZeWhMpFUDApRqSeXy7F27VoAwODBg2FmprsB3BtvvIGGDRuaumoGk8vlGDBggBgQCgkJwfr169UGqzp16oTx48fjyZMnWL58OVavXl3EtdVtwIABmDRpEp49e4YdO3YUS3CKiIiIiMoYY3b7YlCIyil2H6NS7/Dhw3j48CEAYOjQocVcG+P46quvcPToUQBAz549sW/fPp2tlypVqoQVK1bg+PHjcHd3L4Ja6s/V1RXdunX7v/buO6yp6/8D+DtsARmiuBBBhpM6ENS6raNuxV3FUVddta21rdbZ4ajbotaJo3XvUVf9CiJqEcXaVq0VFAFBBZW9ye8PfrkNkkBuyGC8X8+T57mQc89IchP45JzPAQBs375dz70hIiIiIiIigEEhKgcOHjwIAHBzc4OHh4eee1NymZmZWLVqFQDA3Nwc27dvV2n2k0ybNm3g4uJS6Peq7D5269YtjB8/Hu7u7rCwsICZmRnq1KkDT09PTJs2DSdPnoRUKlVrXLLk38HBwUIQj4iIiIhIbQb4b1v6Et/0PRgi/eDyMSrzLl++DACFcuiUVefPn0dcXBwAYPjw4TrLb7RmzRp8/vnnyMvLK/D76OhoREdH4/bt29i4cSOSk5NhaWkpun7Z8yOVSnHu3DlMnDhRI/0mIiIiIiIi9TAoRGVadHS0MOulqMTRb/v6668RHR2NuLg4mJubw8nJCZ06dcKUKVPg7u6upd6qJjAwUDiWLbnStrt37woBIWdnZ0yfPh3NmjVDlSpVkJycjH/++QeXL1/GiRMn1G7D3d0dNjY2ePPmDQIDA0UHhaKjo4u8PzY2Vu2+EREREVHZI5sFr6m6iCoiBoWoTLt27Zpw3Lx5c7XOy8rKwp07d3Dnzh2sX78e8+fPx8KFC7X6wdCpUyely7Du3r0rHLdo0UJrfZB3+PBh5OXlwcLCAtevX0f16tUL3N++fXtMmDABiYmJMDc3L3Cfk5OTSkvKJBIJmjdvjsuXL+PWrVui+1inTh3R5xAREREREZFyDApRmSY/e0SVZVY1a9aEj48P2rVrh3r16sHIyAhPnz7F6dOnsXv3bmRnZ2Px4sXIysrCkiVLtNl1peLj44XjohJGJyYmKs3NY2FhAWdnZ5XblC1Xc3d3LxQQkmdtba1ynYrInqPHjx9DKpXyGxkiIiIiUp8sH5Cm6iKqgBgUojLt5cuXwrGtrW2RZb28vBAZGQljY+MCv2/RogUGDBiASZMmoXv37khMTMSyZcswbNgwNG3aVCv9LkpycrJwbGFhobTcqVOn4Ovrq/C+jh07CruXqaJmzZoAgHv37iEkJATe3t4qnytGlSpVAOQn037z5k2xz5m84pJTx8bGaq3fRERERFQKcUt6ohJjjnUq0169eiUcFxdgsLCwKBQQkuft7Q0/Pz8A+cmQZce6VrlyZeE4NTVVJ22OGDECxsbGyMzMRNu2bdG3b1/89NNP+Ouvv9TebUwR+edI7NgcHByKvMkCW0RERERERKQaBoWoTDMzMxOO09PTS1zf8OHDYWVlBaBgwmddsrOzE47lZ0K9bdSoUZBKpQVu6mrQoAH27dsHW1tb5OTk4PTp05gyZQo8PDxgb28PX19fBAUFqV2/jPxzVFSAjoiIiIioWLKZQpq6EVVADApRmSafc0d+1pC6jIyMhN3HYmJiSlyfOuSXrIWFhems3UGDBuHx48fYvHkzfHx8hMc2Pj4eP//8Mzp06ICxY8cW2rJeDPnnqKT5iYiIiIiIiKhkGBSiMk0+KPT69WuN1Knv5McdO3YUjs+dO6fTtq2trTFp0iQcOXIEL168wL1797B06VLUqlULALBr1y78+OOPatcve47s7e0LzPIiIiIiIhJPk7OEOFOIKiYGhahM8/DwEI4fPnxY4vpycnKEemSBEF3r0aOHsAPY/v37i1xCpm0NGzbEV199hRs3bghJrw8ePKh2fbLHtnHjxhrpHxERERFVYAYGmr0RVUB85VOZ1rJlS2HGyc2bN0tc34EDB5CYmAig4IwdXTI1NcVnn30GID8Z88SJE0u0ZEsT6tSpIyyri4+PV6uOpKQk/PPPPwCAVq1aaaxvREREREREpB4GhahMMzExEQIMISEhSsu9fv262C3aQ0JCMH36dAD5S8imTJmisNzOnTshkUggkUiwaNEitfpdnM8++wwdOnQAAJw4cQKDBw8uNhhTkuVzx48fx5s3b5TeHxUVhQcPHgAAnJ2d1WojNDRUSIbdvXt3teogIiIiIhIw0TRRiRnpuwNEJdW/f38EBgYiJCQEycnJBbZ0l0lMTETnzp3xzjvvYMCAAfD09ETNmjVhaGiIp0+f4vTp09izZw+ysrIAAJ9//jk8PT11PRSBkZERDh06hL59+yIkJATHjh3DxYsXMXz4cHTu3BmOjo6oXLkyUlNTERERgStXruDAgQPC+ebm5qLaW7t2LUaOHInevXujS5cuaNiwIaytrfH69WuEhobixx9/FHYO++ijj9Qa06VLlwAAVatWRbt27dSqg4iIiIiIiDSHQSEq80aPHo05c+YgIyMDx44dw+jRo5WWvXv3Lu7evav0fkNDQ8yfPx8LFixQWkZ+W3X57eM1zd7eHoGBgZg7dy42bdqElJQUbNu2Ddu2bVN6jpWVFaZPn465c+eKbi8tLQ2HDh3CoUOHFN5vYGCAxYsXY8CAAaLrBoB9+/YBAIYNG8bt6ImIiIio5CTQ3AwfThSiCopBISrz7Ozs4OPjg3379mHv3r0Kg0K1atXCoUOHcP36dYSEhCAmJgbx8fHIyMiAtbU16tevj06dOmHChAlwcnIqsr3r168DAGxtbTF27FgtjOg/ZmZmWL16NWbPno19+/bh0qVLuHfvHuLj45GZmQkbGxvUqlULnp6e6NKlCwYOHCh6lhCQH7A5ffo0AgICcO/ePcTFxSE+Ph5mZmaoW7cuOnTogI8++gjvvPOOWuO4fv06Hj9+DABKl+URERERERGRbkmksiQfRGXY77//jtatW8PQ0BDh4eGoW7eu1tpycnJCZGQkFi9eXOSMIvrPhAkTsH37dvTo0QPnzp3TShvR0dGoU6cOACDqz1twqK3i7nFiv11S61skkSfp4m1ZnXHkieyXQWn9yk1sv3TwfIh9bAHd5D7IzRZVXKpGUnyJ2HEYGIpuQzSJDlIu6mSXGzVeV2JPkepiIwQ1XutiH191riex56izaYTYcajz+SF2HLpoo9QqJ+MwVH1eQHRMDOq45+8aGxUVBQcHB231SmXyf/M9ntQTDpXFfyGqsN7kNDhvOQug9IyVSBeYaJrKhVatWsHHxwe5ublYunSp1tqJjIxEZGQkrK2tMXPmTK21U548ffoUu3fvBgAsXrxYz70hIiIionKDW9ITlRhf+VRuLFmyBEZGRvD390d0dLRW2rhy5QoA4OOPP4a1tbVW2ihvli5diuzsbAwZMoRb0RMREREREZUizClE5Ub9+vWxY8cOhIeH4+nTp1qZ8unr6wtfX1+N11teSaVS1K1bFwsXLsSHH36o7+4QERERUXmiya3ky80yRyJxGBSicoUBm9JFIpHgq6++0nc3iIiIiIiISAEGhYiIiIiIiKgM0uBMofKSSJxIJAaFiIiIiIiIqOzh8jGiEmNQiIjKHWlKAqRJxqoVFrEtKwD1thQWu2WzOltuG4o8JzdXfBtit+lWZ6tqseNQh9hxqLM9eZ7Yx1eNLZ4NTbTfhsgt6ZEnvg2p2MdK7DULiP9DX502RH7DLFFrHCJfi2rtpCP2nyJ1/olS47UougmxbajRJ6nIsZfWnY3U2WK+PLSh1vOhg3GIvaYYyCCiEmJQiIiIiIiIiMoeTW4lX1oDt0Raxlc+EREREREREVEFxJlCREREREREVPYwpxBRiXGmEBERERERERFRBcSgEOlcTEwMXF1d4erqii+//FLf3SEiIiIiorJIgv9mC5X4pu/BEOkHl4+RzmVnZyM8PBwA8Pz5cz33hoiIiIiIyiQuHyMqMc4UIiIiIiIiIiKqgDhTiHTOyckJUqlU390gIiIiIqIyTGJgAImGtpLXVD1EZQ1f+UREREREREREFRCDQqVIVlYW3NzcIJFIcPjwYX13p9TZuXMnJBIJJBIJnjx5ou/uVChjx46FRCKBk5OT6HN79+4NiUSChQsXar5jRERERFSBaSrJtATMNE0VFYNCpci6devw6NEjNGnSBIMGDSq2fEhICKZOnYqGDRvCysoKlpaWcHFxQe/evbF69Wq8fPlSa30NCAgQAjSqiI2Nxdq1a9GnTx+4uLjAysoKJiYmqFatGlq0aIHx48fj4MGDSEtL01qfSXtkr4WAgIBC982fPx8AsHLlSkRHR+u4Z0RERERUbmls5zENJqwmKmMYFColkpOTsXz5cgDAvHnzigy2ZGZmYuLEiWjdujU2bdqEBw8eIDk5GampqYiIiMCvv/6KWbNmITg4WFfdL7Kvs2fPhouLCz799FOcOXMGERERSE5ORnZ2NuLj4xEWFoYdO3Zg2LBhqFGjBhYsWFBhg0OLFi0SFWwrC1q3bo1u3bohLS0NS5Ys0Xd3iIiIiIiI6P8x0XQpsWnTJiQkJMDR0RFDhgxRWi4rKwsDBw7E2bNnAQBdunTByJEj0aBBA5iZmeHZs2e4du1aqVh+Fh8fj379+uH69esAAAsLCwwdOhRdunSBo6MjKleujISEBDx69AgXL17E2bNnkZycjG+//Ra9evVC69at9TwC0pRZs2bh4sWL2L59O+bPn4+aNWvqu0tEREREVNZxS3qiEmNQqBTIzc2Fn58fAGDEiBEwKCLz/XfffYezZ89CIpFg48aN+Oijjwrc36JFC/Tp0wdLlixBdna2VvtdlNzcXAwZMkQICPXr1w9bt26Fvb19obJdu3bFRx99hOfPn2PFihVYu3atjntL2ta1a1fY29vjxYsX2Lx5MxYtWqTvLhEREREREVV4XD5WCly8eBFRUVEAgJEjRyotFxERgWXLlgEApk6dWigg9DZjY2PNdVKkNWvWCPll+vTpg2PHjikMCMmrXr06Vq5ciaCgIFSrVk0HvSRdMTQ0xLBhwwAA/v7+kEqleu4REREREZV5BgaavelIZGQkZs2ahQYNGsDCwgJVqlSBl5cXVqxYUeI0Gvfv34efnx/GjBmDFi1awMHBAWZmZrCwsEC9evUwbNgwnDhxQuW/x3NycvDTTz+hffv2qFatGipVqgQXFxdMnjwZf//9d4n6SqUDg0KlwMGDBwEAbm5u8PDwUFpuy5YtyM7OhoGBAebMmaOr7omWmZmJVatWAQDMzc2xffv2Imc/va1NmzZwcXFRu/2XL19i3rx5aN68OWxsbGBmZgYnJyf4+vri6tWrKtWRmZmJLVu2oHfv3qhduzZMTU1hYWGBxo0bY8KECTh//rzCN9IbN25g3rx56NSpE2rUqAETExNYWVmhUaNGmDJlCu7du6ewPdnOaosXLxZ+J8stJH9TtOtabm4udu3ahT59+qBWrVowNTWFnZ0d2rVrh9WrVyM9Pb3Y8d6/fx9jx45FnTp1YGZmhjp16uCDDz7AzZs3VXq8VCFLnv706dNSke+KiIiIiMo4CTSYaFo3XT516hTeeecdrF69Gv/88w/S0tLw+vVrhIaG4osvvkDz5s3x6NEjtev//vvvMWPGDOzevRthYWGIiYlBZmYm0tLS8PjxYxw8eBADBgxA586dkZCQUGRd8fHxePfddzFlyhRcvXoV8fHxyMjIQEREBLZs2QJPT09s27ZN7b5S6cDlY6XA5cuXAaDYHDqHDh0CkL9ErHbt2gAAqVSKuLg4pKeno3r16rCwsNBuZ1Vw/vx5xMXFAQCGDx9e7AwhTbpw4QKGDBmCpKSkAr+PjIxEZGQkfv75Z0ybNg3r169XGqi6c+cOfHx88Pjx4wK/z8rKwr1793Dv3j1s374djx8/LrBF+86dOzFu3LhC9WVnZ+P+/fu4f/8+tm7divXr12Pq1KklHyzyAyz9+vXDH3/8UeD3r169QnBwMIKDg7Fp0yacOXMG7u7uCus4ePAgRo8ejczMTOF30dHR2LdvHw4dOoSffvpJI3318vKCoaEhcnNzcfbsWbRr104j9RIRERERlQVhYWEYNmwY0tPTYWlpiTlz5qBz585IT0/H/v37sXXrVjx8+BC9e/dGaGgoKleuLLoNIyMjtGrVCm3btoWHhwdq1KiBatWq4fXr13jw4AE2b96Mv/76C4GBgejbty+uXr2q8P+i3NxcDBw4UPiS2MfHBxMnTkSVKlXw+++/47vvvsOLFy8wefJk1K5dGz179izx40P6waCQnkVHRwuzP7y8vJSWe/nyJSIiIgAAHh4eyMrKwtKlS/HTTz8JARgDAwO0atUKn3/+OXx8fLTed2UCAwOF4/fff19n7d65cwd9+/ZFVlYWjI2NMX36dPTr1w8WFhYICwvDsmXL8PjxY2zYsAEWFhbCbm/y7t+/j/bt2yMlJQUAMHDgQAwfPhz16tVDbm4uHj58iAsXLuDYsWOFzs3JyYGtrS369++PDh06wM3NDRYWFnj27Blu376N9evXIz4+HtOnT0eDBg3QpUsX4dwBAwagZcuW2LhxIzZt2gQA+PPPPwu1IQsGAkBCQgLatWuHqKgomJqaYuLEiejYsSOcnJyQkpKCCxcuYN26dXj06BF69uyJ27dvw9raukB9N2/exMiRI5GTkwNTU1N8+umn6NWrF0xNTfH7779jyZIlmDJlCho1aqTekyLH3NwcjRs3xt27dwu8RlRV3Hb2sbGx6naNiIiIiMqiMpZoeubMmUhPT4eRkREuXLiANm3aCPd16dIFbm5u+OKLL/Dw4UOsWrVKrTyc27Ztg5GR4n/zu3btiilTpmDo0KE4evQorl+/jtOnT6Nfv36Fyu7atUtYZTF16lRs2LBBuM/b2xs9e/aEp6cnkpKS8PHHH+P+/ftK26XSTSJlcg+9OnjwoJBrJSgoSOnsicDAQHTq1AkAMG3aNNy6dQs3btxQWu+UKVOwceNGjfdXFd26dcNvv/0GAHj06FGJloLJk5+J8/YsHSD/zenmzZswNDTEr7/+iu7duxe4//Xr12jXrh3u3bsHAwMD3L17F40bNy5QxtPTE7dv34aBgQF++eUXDB8+XGFfEhISYG5ujkqVKgm/i4mJga2tLczNzRWek5iYiA4dOuDu3bto164dgoKCCpVZtGiRsISsuEtz5MiR2Lt3L+rWrYvLly/D2dm5UJmwsDC0b98eqampmDt3Lr7//vsC93t5eSE0NBTGxsb47bff0KFDhwL3x8TEoHXr1kJApm7dugqXsKnqww8/hL+/P8zNzZGSkgKJiA9fMWWfXr8Ah5o1VCtsKPLDKy9PXHkAkIo8x8BQfBuGIs/JzRXfhkTkimOx4wbEj0MdYschtjwA5Il9fNX4KDY00X4buSI3LMhTow2xj5XYaxYQ/4e+Om2IXHcgUWscIl+LauXH0MX6CbGvEzX6JPY5V2vYIk/SRb6Sivxnvdix6zB/jDhiX7tqvHhF/J0RHRODOu75fy9HRUXBwcFBfHsaFh0djTp16gAAnnz5ARysLTVTb2IKnJbvBaCdsYaEhKBVq1YAgMmTJyucjZ+Xl4cmTZrg/v37sLGxwYsXL7SSJ/bGjRtCQOrzzz/HihUrCpVp1KgR7t+/jypVqiAqKkrh/zjLli0T0pocPHiwyF20qfQqre+GFYb87Ieillm9evVKON6+fTtu3LgBb29v/O9//xPWoe7duxe1atUCkL/F/fr167XX8SLEx8cLx0UljE5MTMRff/2l8Pb20q3ihISECFMbJ06cWCggBAC2trbYsmULgPw33LeDZhcuXMDt27cBAB9//LHSgBAA2NnZFQgIAfmzeJQFhADA2toa33zzDQDg6tWrxa7hLcqTJ09w4MABAICfn5/CgBAANG/eHNOmTQOQH1STd/PmTYSGhgLI/2B6OyAE5I9Jlh9KE2Sv8bS0NGGGGxERERGRWjSWT0iDM46UOH78uHCsKOUEkL/yY/To0QCAN2/eCGlGNE1+WVpGRkah+x8+fIj79+8DAIYOHar0f5yxY8cKx4pWUlDZwKCQnr18+VI4trW1VVouNTVVOM7IyECTJk1w+fJldO7cGZUqVYKNjQ1GjBiBgIAAIa/Q4sWLS5y9Xh3JycnCcVE5jk6dOgUPDw+FN2VvlMrIZiYBwPjx45WWa9u2LRo2bFjoHAA4ffq0cPzJJ5+Ial+R1NRUPHnyBH///bcQ7JKP9L+dB0iMM2fOIDc3F+bm5sWu35UFe549e4anT58Kv5cff1GP98CBA2FjY6N2X+VVqVJFOBYbFIqKiiryFhISopE+EhERERFpmmwploWFBTw9PZWW69ixo3Csrc1Z9u/fLxw3aNCg0P3ym/PI9+dtNWrUEPKWciOZsouL/vRMfgZQUUEhMzOzAj9/8803CiO2bm5umDJlClauXIlXr17ht99+U7hGVJvkI8+pqamwsrLSept//fUXAMDExATNmjUrsmyrVq1w//59/Pvvv8jKyoKJSf4SjLCwMACAo6Mj6tatq1Y/4uPjsXr1ahw5cgT//vtvkUvA5GdUiSWb4ZOWliZq7W5cXBwcHR0B/JezyMTEBE2bNlV6jrGxMZo3b66RbyrkX+PygU5VlIbpykRERERUikg0uJW83BJdVXJViv3bVDbzxtXVtci/3+WDNLJzNCE+Ph7//vsvtm3bBn9/fwBA1apVMXLkyEJl5XdMVhQ0ktegQQM8fPgQUVFRSE1NLRUbH5E4DArpmXywJz09XWmGefnfSyQSdO3aVWmdPXr0wMqVKwHkLxHSdVDIzs5OOH758qXSoNCoUaMwatSoAr8TkzdGniy4VqVKlWKDJDVq5OeakUqleP36NapXrw7gvyBNzZo11erDrVu30KNHD5WXhamyVbwyL168UOs8+Zlj8o+ZYTF5XGSPUUnJj1kb66OJiIiIqALRUqJpb2/vYouLSc2bkZEh/K9RXDDJ1tYWFhYWSE1NRVRUlMptKNKpUyelG7xUrVoVx44dU7giQD7FSXH9leV3kkqliI6ORv369dXvMOkFg0J6Jp9z59WrV0qDQrKLDQBsbGyK3J5Qvqz88jRdadq0KS5dugQgf/aNphJNq0LdoFJJZWVlYejQoUhISICxsTFmzJiB/v37w93dHba2tjA1NQUARERECI9HSXK85/5/kuCqVauKmsGjKPeQLh8z+ZlxmlqSRkRERERUmsmn17C0LD4xtiwoJNsRWdM+/vhjzJ8/H1WrVlV4v5j+ys8M0lZ/SbsYFNIz+aDQ69evlS5bcnNzg7GxMbKzs4WAgDLy9+tjW8COHTti9erVAIBz585h8ODBWm9TlqsmISEBOTk5RY5blstGIpEUWM4ke1NUZ2vz//3vf4iIiAAAbNy4ERMmTFBYTj4oUhKy2VjJyclo2LBhsTN9FJGNPSEhAbm5uUXW8fz5c/U6+pbXr18Lx/LBSyIiIiIi0bQ0UygkJETt1QOKyCdzlqWuKIrsC+WSrCwAAH9/f6SmpkIqleLNmzcIDQ3Fpk2b4Ofnh4iICGzbtk3higAx/ZX1VRP9Jf1gomk98/DwEI4fPnyotJyxsbGwbWBSUlKR+WjCw8OF49q1a2ugl+L06NFDeHPZv3+/TmYrNWnSBED+jJ07d+4UWVaWkNjNza3Am1yLFi0AAE+fPkVkZKSo9v/++2/heNiwYUrLyXIBKaPqrJ3mzZsDADIzM4utUxnZay8rK6vIpNc5OTnFPqaqkr3GnZ2di9ypjYiIiIhIX2rWrAkHB4cib2LIpwzJysoqtnxmZiYAFNrtWCxnZ2c0adIEHh4eaN++PT799FPcvXsXvXr1wunTp+Hl5VVgqZg6/ZX1VRP9Jf1gUEjPWrZsKVx0si3VlRk0aJBwLL+l4duOHj0qHLdv375kHVSDqakpPvvsMwD5yYQnTpyIvLw8rbYpn2Npx44dSstdv35dSJz2dl6mvn37Csdr1qwR1X5OTo5wrCyBcl5eHrZu3VpkPfJvwPJvsG/r27evEEBau3atiJ7+R378u3btUlru2LFjBWb4lIQsgNWqVSuN1EdEREREFZiBgWZvWiKf+kOVJVay/ydUWWomlpmZGfz9/WFubo6oqCh88cUXhcqI6a/8/z7a6C9pH4NCemZiYiL8g1zcltoffvgh7O3tAQALFy7Es2fPCpUJDAzEnj17AOTPnmnbtm2hMjt37oREIoFEIsGiRYtKOALFPvvsM2Er9BMnTmDw4MHF7rZVksCDt7c3WrZsCQDYunWrkNNIXmJiIiZPngwAMDAwwJQpUwrc37VrV2F7yB9//LHAVo1vS0hIKDA90s3NTTjeuXOnwnPmzJmD27dvFzkO+Wmq8jO+3la/fn0MGTIEQP5sLNlyPWUeP36Mffv2Ffidt7e3MDtq06ZNBbaelImNjcXnn39eZN2qioiIEF4D3bt310idRERERFSBSfDfErIS37TXTTMzMyH9g6KZOfJev34tBFq0lW6hatWqwv+JJ06cQHZ2doH75WdCFddfWTJsiUTC3YLLKAaFSoH+/fsDyA8KySf1epulpSXWr18PiUSCZ8+ewcvLC35+frh58yauXr2KefPmoWfPnsjNzYWRkRF++uknvSVeNjIywqFDh4TM/ceOHYOzszMmTpyIvXv34urVq/jjjz9w7do1/Pzzz5g0aRKcnJyE89VZWrR161aYmJggJycHvXr1wueff47AwECEhoZi69ataNGihbAN++effy4sOZO3Z88eWFpaIi8vDyNGjMCgQYNw6NAh3Lp1CyEhIdi7dy/Gjh2LunXrFsiz06NHDyFgN2/ePHz00Uc4f/48bt26hQMHDqBr16744YcfFAbp5L377rvC8aeffoorV67g33//xaNHj/Do0aMCM5I2bdqEevXqAQBmzZqFjh07Yvv27bhx4wbCwsLw22+/YdWqVejWrRtcXV1x5MiRQu1t3LgRRkZGyM7ORrdu3TB37lxcvXoVN2/ehJ+fHzw9PREbG1vklvWqkgXqjIyM0KdPnxLXR0RERERUVjRq1AgACv1N/7YHDx4Ixw0bNtRaf2S5bdPS0gp9eS/r69v9UUR2f506dbgdfRnFRNOlwOjRozFnzhxkZGTg2LFjGD16tNKyw4YNQ3x8PD777DM8e/YMM2bMKFTG0tISP//8s9IAhPwMF/nt4zXN3t4egYGBmDt3LjZt2oSUlBRs27YN27ZtU3qOlZUVpk+fjrlz54pur1mzZjh16hSGDBmCpKQkrFq1CqtWrSpUbtq0aVi6dKnCOho2bIiAgAAMHDgQUVFROHr0aIHleMpYWFhg9+7dGDBgADIyMrB582Zs3ry5QJlOnTrBz89PYTBKxtXVFUOHDsXBgwdx4cIFXLhwocD9jx8/FoJnVapUQXBwMIYOHYqgoCBcuXIFV65cUVq3lZVVod+1atUKu3fvxtixY5GRkYGlS5cWeGyMjIywceNGBAcHF5l3SBV79+4FkB9Ak0+wTkRERESkFi0lmtaGdu3aISgoCKmpqbh165bSdAryW8gX94VyScTExAjHby/7ateuXYH+DB8+XGEdcXFxQs5QbfaVtIszhUoBOzs7+Pj4APjvH+eiTJs2Dbdv38aUKVPg6uqKSpUqwdLSEu+88w6++OILPHz4UJh9pMj169cB5O8+NXbsWI2MQRkzMzOsXr0aERERWLVqFXr16gUnJydYWlrC2NgY1apVQ9OmTfHhhx/i559/RmxsLL7//nu1o8zdu3fHo0ePMHfuXDRr1gxWVlYwNTWFo6MjRo4ciaCgIPj5+cGgiDXDnp6e+Oeff7B+/Xp06dIF9vb2MDIygqWlJTw8PDBp0iRcunSpwMwmID/YERoailGjRqFWrVrC+Dp27IgtW7bg0qVLKo3r559/xg8//ABvb29YW1sX2dcaNWrgypUrOH36NEaOHIl69erB3NxcaPvdd9/FrFmzEBgYqDTX0ogRIxAWFgZfX1/UqlULJiYmqF27NoYOHYqrV69i4sSJxfa5ODExMULAaurUqSWuj4iIiIioLBkwYIBw7O/vr7BMXl4edu/eDQCwsbFB586dtdKX6Oho4X/CunXrFsghBADu7u7CLKWDBw8iLS1NYT3yaTMGDhyolb6S9kmkUqlU350g4Pfff0fr1q1haGiI8PBwpVvTa4KTkxMiIyOxePFiLFiwQGvtEMl89913mD9/Pho2bIi///5bK8sao6OjhXXXT69fgEPNGqqdaChywqQ6SdOlIs8xMBTfhqHIc3JzxbchEfk9gthxA+LHoQ6x4xBbHgDyxD6+anwUGxa/pW2J28jNLr6MvDw12hD7WIm9ZgHx3/6q04bIZBQStcYh8rWoVtJUXSw7F/s6UaNPYp9ztYYt8iQtJrEVVOQ/68WOXRfPh1rEvnbVePGK+DsjOiYGddwbA8jPHVMacsbI/833ZNF4ONhULuYMFet9kwynRdsBaG+sHTp0QFBQEIyMjHDlyhVhd2mZFStWCImfFy5cWCj/a0BAgBAoGjNmTKFcpg8fPkR0dDS6dOmitA+JiYno27cvgoKCAADz58/HN998U6jcjh07MH78eAD5kxL8/PwK3B8eHo4WLVogKSkJrq6uuH//PoyMuBCpLOKzVkq0atUKPj4+OHr0KJYuXYqffvpJK+1ERkYiMjIS1tbWmDlzplbaIJKXkpIi7JC2cOFCveW5IiIiIiLSp3Xr1qFt27ZIT09H9+7dMXfuXHTu3Bnp6enYv38/tmzZAiB/ps6sWbNE1//s2TO89957aNq0KQYMGABPT0/UqFEDRkZGiIuLQ3BwMLZv3464uDgA+RsTffXVVwrrGjNmDHbs2IHg4GBs2LABcXFxmDhxImxtbRESEoJvv/0WSUlJMDAwwPr16xkQKsP4zJUiS5YswcmTJ+Hv74958+ZpJTotW8Lz8ccfw9raWuP1E71tw4YNSEhIgLe3N4YOHarv7hARERFReSHR4Fby6swMFql58+Y4cOAARo0ahaSkJIV5VN3d3XHmzJlCS7rE+OOPP4rNB9q7d29ha3pFDA0Ncfz4cfTq1Qs3b97EkSNHCm1cY2pqCj8/P/Ts2VPtvpL+MShUitSvXx87duxAeHg4nj59qpWgkK+vL3x9fTVeL5EylStXxsKFC+Hj48NZQkRERESkOWUo0bRM3759cffuXaxbtw5nzpxBdHQ0TExM4OrqiiFDhmD69Olq7cQM5Cd7Pn/+PH777TeEhoYiOjoaz58/R1paGqysrODs7IzWrVtjxIgRKiWGrlq1Kq5du4atW7di7969uH//PlJTU1GrVi289957mDlzJho3bqxWX6n0YE4hIioXmFOIOYVUxpxCqmNOITGNiCvNnEIiMKeQyiryn/XMKaS68pRT6JtJcLDVUE6h18lwWpC/fKu0jJVIFzhTiIiIiIiIiMqeMjhTiKi0Ka0hciIiIiIiIiIi0iLOFCIiIiIiIqKyR2KguQTROkg0TVQaMShEROWPiRlgWkm1sobGoqqWGKjztqn9HA9SsbmOjNXoU06WuPJ5akzDFpuTRp2cEGIfq9xM8W2I/cNSdA4iADki8/3oIjdSVoboJqTZIl9X2eLbEJunS2JmIb4NY3E5nqQ5OeLbUPV9TUadpRBi87KokztMbBtiX+sAYCTuvV0nj5U6MtPFlVdnHLrIFyc2V16uGteH6DxSarwnirzOYS5+p1+J2MdKnW3A1cldWFoZADDQ0LIvxoSoguJLn4iIiIiIiIioAuJMISIiIiIiIip7uHyMqMT4yiciIiIiIiIiqoA4U4iIiIiIiIjKHm5JT1RinClEVAbExMTA1dUVrq6u+PLLL/XdHSIiIiIiIioHOFOIqAzIzs5GeHg4AOD58+d67g0RERERUSkgMVBvJ1JldRFVQAwKERERERERUdnD5WNEJcagEFEZ4OTkBKlUqu9uEBERERERUTnCoBARERERERGVPdySnqjE+MqnMi0rKwtubm6QSCQ4fPiwvrtT6uzcuRMSiQQSiQRPnjzRSx969+4NiUSChQsX6qV9IiIiIiIiUoxBISrT1q1bh0ePHqFJkyYYNGhQoftlARExN20JCAgQ1UZsbCzWrl2LPn36wMXFBVZWVjAxMUG1atXQokULjB8/HgcPHkRaWprW+qwq2bgCAgIK3Td//nwAwMqVKxEdHa3jnhERERFRuSXBf3mFSnzT92CI9INBISqzkpOTsXz5cgDAvHnzNBLQcXd3L3EdJZWZmYnZs2fDxcUFn376Kc6cOYOIiAgkJycjOzsb8fHxCAsLw44dOzBs2DDUqFEDCxYsKBXBIUVat26Nbt26IS0tDUuWLNF3d4iIiIiovDAw0OyNqAJiTiEqszZt2oSEhAQ4OjpiyJAhCsv8+eefxdaza9curFy5EgAwZswYjfZRrPj4ePTr1w/Xr18HAFhYWGDo0KHo0qULHB0dUblyZSQkJODRo0e4ePEizp49i+TkZHz77bfo1asXWrduXaC+sWPHYuzYsXoYSUGzZs3CxYsXsX37dsyfPx81a9bUd5eIiIiIiIgqPAaFqEzKzc2Fn58fAGDEiBEwUBLZb9KkSbF1XblyBUD+EqhRo0ZprpMi5ebmYsiQIUJAqF+/fti6dSvs7e0Lle3atSs++ugjPH/+HCtWrMDatWt13FtxunbtCnt7e7x48QKbN2/GokWL9N0lIiIiIirruCU9UYlxjhyVSRcvXkRUVBQAYOTIkWrX888//yAkJAQA0KlTJzg6Omqkf+pYs2aNkJOnT58+OHbsmMKAkLzq1atj5cqVCAoKQrVq1XTQS/UYGhpi2LBhAAB/f39IpVI994iIiIiIiIgYFKIy6eDBgwAANzc3eHh4qF3P7t27hWN9Lh3LzMzEqlWrAADm5ubYvn270tlPirRp0wYuLi6Ffl/c7mOdOnWCRCJBp06dAAAxMTH47LPP4OrqikqVKsHOzg49evTA2bNn1RqXPFki8KdPnyI4OLjE9RERERFRBSfbkl5TN6IKiK98KpMuX74MAIVy6IghlUrx888/A8jP3aNo9zJdOX/+POLi4gAAw4cPL3aGkDYEBwejWbNmWLNmDcLDw5GRkYFXr17hwoUL6NWrl5B3SV1eXl4wNDQEAI0EmYiIiIiIiKhkmFOIypzo6Ghh1ouXl5fa9QQEBODp06cAAB8fH1haWmqie2oJDAwUjt9//32dtx8bG4sBAwbAwMAAy5YtQ7t27WBiYoKrV6/im2++wZs3bzBnzhz07NkTjRs3VqsNc3NzNG7cGHfv3i0wXlUVt519bGysWv0iIiIiojJKIgEMmFOIqCQYFKIy59q1a8Jx8+bN1a5HfunY6NGjS9QnVXTq1ElpLp27d+8Kxy1atNB6X9728OFD1K1bF8HBwahdu7bwey8vL3h5eaFDhw7IycnBli1bsG7dukLnq5ojyNPTE3fv3kVYWBikUikkIj5869Spo3JZIiIiIqoANLnsi8vHqIJiUIjKHPkZI+ous0pLS8ORI0cAAA4ODujSpYtG+qau+Ph44biohNGJiYlCgu23WVhYwNnZWe0+/PjjjwUCQjLt2rVDq1atcOPGDQQFBaldP/Df85WWloa4uDhuTU9ERERERKRHDApRmfPy5Uvh2NbWVq06jh8/juTkZADAqFGjRCV11gZZX4D84I4yp06dgq+vr8L7OnbsKOxeJpaNjQ169+6t9H5PT0/cuHEDERERatUvU6VKFeFYbFBIWTBMJjY2Ft7e3mr3jYiIiIjKGAk0uCW9ZqohKmsYFKIy59WrV8KxukGh0rLrmEzlypWF49TUVFhZWem0fTc3tyIDY7JgjnzwSh3yz1dqaqqocx0cHErUNhERERERERXEhZNU5piZmQnH6enpos+PjY3Fb7/9BiA/Z06DBg001jd12dnZCcfyM6HeNmrUKEil0gI3TTA3Ny/yflnAKC8vr0TtyD9fxsbGJaqLiIiIiCo4bklPVGJ85VOZI59zR37WkKp++eUX5ObmAtBNgmlVNG3aVDgOCwvTY0+0S/75srGx0V9HiIiIiKjsM5Bo9kZUATEoRGWOfFDo9evXos/fs2cPAMDExAQjRozQWL9KomPHjsLxuXPn9NgT7ZJ/vribGBERERERkX4xKERljoeHh3D88OFDUefeuXNH2P69V69eBZZt6VOPHj1QvXp1AMD+/fuLXEJWlsmeL2dn52KXrBERERERFUki0eyNqAJiUIjKnJYtWwp5hW7evCnq3NKWYFrG1NQUn332GYD8BMwTJ04scf6e0ig0NBQA0KpVKz33hIiIiIiIiBgUojLHxMRECCqEhISofF5ubi727t0LID+xc1FbsL9t586dkEgkkEgkWLRokaj+quqzzz5Dhw4dAAAnTpzA4MGDER8fX+Q56iyf05eIiAhhPN27d9dzb4iIiIiozGOiaaIS45b0VCb1798fgYGBCAkJQXJycoEt3ZU5f/48nj9/DgAYMWJEqdv9ysjICIcOHULfvn0REhKCY8eO4eLFixg+fDg6d+4MR0dHVK5cGampqYiIiMCVK1dw4MAB4fzSvhzr0qVLAPLH2adPHz33hoiIiIiIiBgUojJp9OjRmDNnDjIyMnDs2DGVdhGTXzomdtcx+a3UtZmHyN7eHoGBgZg7dy42bdqElJQUbNu2Ddu2bVN6jpWVFaZPn465c+dqrV+aIJul1aNHjwLJwomIiIiI1KLJXcO4+xhVUJwjR2WSnZ0dfHx8APwXbChKUlISTp48CQBo2LAhvLy8RLV3/fp1AICtrS3Gjh0rrrMimZmZYfXq1YiIiMCqVavQq1cvODk5wdLSEsbGxqhWrRqaNm2KDz/8ED///DNiY2Px/fffw8LCQqv9KomYmBhcuXIFADB16lQ994aIiIiIygeJBpeOMShEFZNEKpVK9d0JInX8/vvvaN26NQwNDREeHo66detqrS0nJydERkZi8eLFWLBggdbaKa++++47zJ8/Hw0bNsTff/8NiRZ2d4iOjha2uX966wocatVU7URDccsIJQbqTLDU/tusVHRicjX6lJMlrrw6ydLFfiQZqPHdhtg2crPFtyE2L0FerhptiLyO1MmVILZfWRmim5Bmi3xdZYtvAwaGoopLzNQIshubiCufkyO+DdNK4sqr814r9vqQ6uA6z1HjGjQSuURcF4+VOjLTiy8jT51xiH1vUOc5F3kNIleN60MX74lir3Nza9FNSMQ+VkZq/F1ibKZy0eiYGNRxbwwAiIqKgoODg/j2NEz+b74nWxbDoaqtZuqNfw2nSQsBlJ6xEukCZwpRmdWqVSv4+PggNzcXS5cu1Vo7kZGRiIyMhLW1NWbOnKm1dsqrlJQUrF27FgCwcOFCrQSEiIiIiKgC4pb0RCXGoBCVaUuWLIGRkRH8/f0RHR2tlTZky54+/vhjWFuL/8anotuwYQMSEhLg7e2NoUOH6rs7RERERERE9P+YaJrKtPr162PHjh0IDw/H06dPtTLN09fXF76+vhqvt6KoXLkyFi5cCB8fH84SIiIiIiLN0eRW8tySniooBoWozGPApnRjYmkiIiIi0gruPkZUYgyHEhERERERERFVQJwpRERERERERGUPl48RlRhf+UREREREREREFRBnChFRuSMxNoPE2Ey1wobG4iqXSsV3CCLPMRAfr5dAB+vgTVR8TEtE5DjUSV4u9hy1nvNyQgdjl5TGx1ety0nsSeqMu7zkuxA59lL4EgFQOt8bpHn67oFiunisdLGZhdjHV52ZJ2LHkVcKX4e6pMmt5LkhClVQnClERERERERERFQBcaYQERERERERlT0GErVmWCuti6gCYlCIiIiIiIiIyiANLh8rN0t0icTh8jEiIiIiIiIiogqIQSEiLYiJiYGrqytcXV3x5Zdf6rs7RERERETlj2xLek3diCogLh8j0oLs7GyEh4cDAJ4/f67n3hAREREREREVxqAQERERERERlT3ckp6oxBgUItICJycnSKVSfXeDiIiIiKj8MjDQ4O5jXD5GFRNf+UREREREREREFRCDQqRTWVlZcHNzg0QiweHDh/XdnTJt7NixkEgkcHJy0ndXijRt2jRIJBKMGTNG310hIiIiovJEtnxMUzeiCohBIdKpdevW4dGjR2jSpAkGDRqktNyLFy/wzTff4N1330WVKlVgbGwMGxsbeHp64osvvsCTJ0+03teAgABIJBJIVPyAiI2Nxdq1a9GnTx+4uLjAysoKJiYmqFatGlq0aIHx48fj4MGDSEtL03LPdc/JyQkSiQQ7d+4sdN+XX34JExMT7NmzB7du3dJ954iIiIiIiEghBoVIZ5KTk7F8+XIAwLx585QGWy5cuIAGDRpg4cKFuH79Ol6/fo2cnBwkJibi9u3bWLFiBRo1aoQ9e/bosvtKZWZmYvbs2XBxccGnn36KM2fOICIiAsnJycjOzkZ8fDzCwsKwY8cODBs2DDVq1MCCBQsUBod27twpBKJ0EfjSBUdHR4wZMwZSqRTz58/Xd3eIiIiIqLyQSDS4JT1nClHFxKAQ6cymTZuQkJAAR0dHDBkyRGGZiIgIDBgwAK9fvwYA9O/fH4cPH0ZISAiOHz8uLJlKT0/H2LFjERwcrMshFBIfH4/OnTtj5cqVSE9Ph4WFBcaNG4c9e/YgMDAQt2/fxsWLF7Fp0yb4+PigUqVKSE5Oxrfffou7d+/qte+6NGvWLADA2bNnOVuIiIiIiDSDy8eISoy7j5FO5Obmws/PDwAwYsQIGCjJ7r969Wqkp6cDAGbPno0ffvihwP39+/dHixYt8PHHHyMvLw9Lly7F6dOntdt5JXJzczFkyBBcv34dANCvXz9s3boV9vb2hcp27doVH330EZ4/f44VK1Zg7dq1Ou6tftWvXx8tWrTA7du38eOPPypcZkZERERERES6xZlCpBMXL15EVFQUAGDkyJFKy127dg0AIJFIMG/ePIVlpk+fjipVqgCAEJDRhzVr1iAgIAAA0KdPHxw7dkxhQEhe9erVsXLlSgQFBaFatWo66GXpIXveDx06hOTkZD33hoiIiIjKPI0tHfv/G1EFxFc+6cTBgwcBAG5ubvDw8FBaLisrCwBgZ2cHKysrhWUkEgnq1atXoLyuZWZmYtWqVQAAc3NzbN++XensJ0XatGkDFxcX4WdZUutx48YJv3N2dhbyC8lusiCUIm/evMGCBQvQuHFjWFhYwMbGBh06dMAvv/yiUp8SExOxdOlStG3bFtWqVYOJiQlq1qyJvn374vDhw5BKpSqPTxFZYvG0tDScOHGiRHURERERERFRyXH5GOnE5cuXAQCtW7cuslz9+vXx999/IyEhAUlJSUoDQxEREUJ5fTh//jzi4uIAAMOHDy92hpC2/fPPP3j//fcLJacOCgpCUFAQrl+/LizfU+TSpUsYNmwYEhISCvw+Li4Op0+fxunTp9GrVy8cOHAAlpaWavWxbt26qFGjBuLi4nD27FmMGjVKrXqIiIiIiADk5wEy0FAuIOYUogqKM4VI66Kjo4VghZeXV5FlP/roIwCAVCrFkiVLFJbZsGEDXr16VaC8rgUGBgrH77//fonr8/Lywp9//onvvvtO+N358+fx559/FrgpevzS0tLQt29fJCQkYN68eQgICEBoaCi2bt0KBwcHAPmP2fnz5xW2HRwcjJ49eyIhIQHVq1fHd999h1OnTuHWrVs4deqUELz59ddfMWbMmBKN09vbG0DBx09V0dHRRd5iY2NL1DciIiIiIqKKhjOFSOtkeYIAoHnz5kWW7datG77++mt8//33WL58Of7991+MHDkSjo6OiI2NxfHjx4UkxWPGjCmw3ErTOnXqpHTJlPzOYS1atChxWxYWFmjSpAlCQ0OF37m7u8PJyanYc1++fImsrCxcv34djRs3Fn7v6emJTp06wcPDAxkZGdi4cSN69OhR4Nzs7GyMGjUK2dnZeP/993HkyBGYm5sXGFufPn3QoUMHTJo0CUePHsXFixfRrVu3AvW8PUNJGU9PT5w8eRIxMTF4/vw5qlevrtJ5AFCnTh2VyxIRERFRBaDJXEDMKUQVFINCpHXR0dHCsSrLrL777jt06tQJS5YswdGjR3H06NEC9zdv3hxff/21kKNGH+Lj44XjohJGJyYmCgm232ZhYQFnZ2eN9Ofbb78tEBCScXV1xYABA7B//35cvXq10P379+/HkydPYGZmht27dxcICMmbOHEitm3bhpCQEOzcubNQUEhV8s9/RESEqKAQEREREVEBmtxKnsvHqIJiUIi07uXLl8Kxra1tseWfPXsGf39/pTuL3b17Fzt37kTDhg3RqFEjjfVTDPndsywsLJSWO3XqFHx9fRXe17FjxyITR6tKIpHggw8+UHq/p6cn9u/fj1evXuHNmzewsbER7jt58qTQl+J2Q+vQoQNCQkJKtOObbNc4AEJOJlUpC67JxMbGCsvTiIiIiIiIqHicI0daJ8v/AxQfFLp//z68vb2xd+9eWFhYYOPGjYiKikJWVhbi4uKwe/du1K5dG6dPn0abNm3Uyk2jCZUrVxaOU1NT9dIHmapVq8LOzk7p/fKBmLe3gpctVzt//nyhnc7evq1cuRKA+GCOPPnnX+zj5uDgUOStZs2aaveLiIiIiMqgMrolfWRkJGbNmoUGDRrAwsICVapUgZeXF1asWIG0tLQS1Z2Xl4d79+5h586dmDp1Kry8vGBqaqrSbsbyOnXqVOz/B7IblW2cKURaZ2ZmJhynp6cXCKi8bfTo0YiJiYG5uTmCgoLQsGFD4b7q1avD19cXXbt2RfPmzfH8+XN88MEHiIiIgKmpqVbH8Db5IMzLly+V7pI2atSoQrtsafqNU9mSLxkDg/8+4HJzcwvc9+LFC9Htpaeniz5H0bnGxsZq10NEREREVBbJNnJJSkoSfpeWlobQ0FCEhoZi27ZtOHPmDFxdXdWqf8+ePRg7dqyGeksVAYNCpHXyy5JevXqlNCj0xx9/CDNXRo4cWSAgJK9mzZqYMWMG5s2bh2fPnuHcuXPo37+/5jtehKZNm+LSpUsAgLCwMLi4uOi0fU2RBYl69uyJH374Qevtyc8ak1/GRkREREQkVn5KIc184aqLCS9hYWEYNmwY0tPTYWlpiTlz5qBz585IT0/H/v37sXXrVjx8+BC9e/dGaGhokV+mKyO/UY6xsTE8PDyQnZ2NP//8U60+t2zZEv7+/mqdS2UDg0KkdfJBodevX6Nu3boKy92/f184Lm5HL09PT+H4wYMHOg8KdezYEatXrwYAnDt3DoMHD9Zp+5piZ2eHZ8+eISsrC02aNNF6e69fvxaOHR0dtd4eEREREZVjZWz3sZkzZyI9PR1GRka4cOEC2rRpI9zXpUsXuLm54YsvvsDDhw+xatUqLFq0SHQbjRo1wvr16+Hl5YVmzZrBzMwMixYtUjsoJNslmcov5hQirfPw8BCOHz58qLSckdF/McqcnJwi68zOzlZ4nq706NFD2Dlr//79BZJpl4Su1+Q2b94cQH5uoaysLK23J3v+TU1N1Z4SS0RERERU1oSEhCAoKAgAMH78+AIBIZlZs2YJqyXWrVtX4H8eVXl7e2PGjBlo3bp1gTQeRMowKERa17JlS+EN6ebNm0rLyW/PLnvDVEY+wbSmtnUXw9TUFJ999hmA/ITJEydORF5eXonrlX/jzszMLHF9xenXrx8AIDExUSfTQmXPf/PmzZlTiIiIiIhKpgwlmj5+/LhwPG7cOIVlDAwMMHr0aADAmzdvcPnyZa32iQhgUIh0wMTEBK1atQKQHyFXpnnz5qhduzYA4MiRI0LOnrfdvn0bP/30E4D8JMtdunQpVGbnzp1CNnx1pl2q4rPPPkOHDh0AACdOnMDgwYMRHx9f5Dnyy6cUkd9BKzw8vOSdLMaYMWNQp04dAMDnn3+OK1euFFn+6tWrau/4lpmZibt37wIAunfvrlYdRERERERl0dWrVwHkL8eST4Xxto4dOwrHwcHBWu8XEXMKkU70798fgYGBCAkJQXJyssKkaQYGBli6dClGjx6N3Nxc9OzZE5MnT0bfvn1hb2+PV69e4cKFC/jxxx+FrRrnzJmjt4TFRkZGOHToEPr27YuQkBAcO3YMFy9exPDhw9G5c2c4OjqicuXKSE1NRUREBK5cuYIDBw4I5yvaNax58+YwMzNDRkYG5s+fD2NjY9StW1fYQax27dqoVKmSxsZgamqKgwcPolOnTkhJSUGXLl0wfPhwDBgwAM7OzsjLy0NsbCxu3bqFY8eO4c8//8SPP/5Y4MNKVVeuXBGmwA4cOFBjYyAiIiKiCkoiAQw0lH5By2kcZPlTXV1di0x/0aBBg0Ln6NODBw/QqlUr/PPPP8jIyEDVqlXh6emJQYMGYcSIEZz9Xw4wKEQ6MXr0aMyZMwcZGRk4duyYMC3ybb6+vnj+/Dnmzp2L7Oxs+Pn5wc/Pr1A5iUSCTz75BF9//bXCeuS3PpffPl7T7O3tERgYiLlz52LTpk1ISUnBtm3bsG3bNqXnWFlZYfr06Zg7d26h+ypXroyPP/4YP/zwA27fvl1oRs3ly5fRqVMnjY6hdevWCAgIwNChQxEVFYVffvkFv/zyS5H9V8fevXsBAI0bN0azZs3UqoOIiIiISNtiY2OLLePg4KByfRkZGcKKguLOs7W1hYWFBVJTUxEVFaVyG9ry/PlzPH/+XPg5JiYGMTExOHnyJJYvX47Dhw8r3TWaygYGhUgn7Ozs4OPjg3379mHv3r1Kg0JA/jKmvn37YsuWLQgMDMSjR4+QkpICc3Nz1K1bF+3atcOECROKnHZ5/fp1APlvqmPHjtX0cAowMzPD6tWrMXv2bOzbtw+XLl3CvXv3EB8fj8zMTNjY2KBWrVrw9PREly5dMHDgQIWzhGSWLVsGNzc37N69G3///TcSExOFreO1pXXr1vj333+xc+dOnDp1CmFhYYiPj4eBgQGqVauGhg0bomPHjhg0aBDq168vuv6MjAwcPXoUADB16lRNd5+IiIiIKiIt7T7m7e1dbHH5rd+Lk5ycLBxbWloWW14WFEpJSVG5DU0zMDDAe++9h169eqFp06aws7NDcnIybt++jc2bN+P+/fu4d+8eOnfujJCQEO4sXIZJpGJezUQl8Pvvv6N169YwNDREeHi40q3pNcHJyQmRkZFYvHgxFixYoLV2SDU///wzfH19YWdnhydPnqj0YShWdHS0kB8p6m4IHGrVLOaM/2cocsqrWm+ZIs8xUOePG13sXKeLjwuR41BnqrfYcyryx6Quxl4aH1+1LiexJ6kzbt3uUKk9IsdeCl8iAErna1da8k0vtEIXj5UudnAV+/iqE6wQPQ41xm2qeiqC6JgY1HFvDACIiooSNTtGW+T/5os8vRsO1atqpt7n8ajbR/kX128T8290VFSUEDTx9fXF7t27iyzv6OiIqKgouLi44NGjRyq3o8yiRYuwePFiAKqvPHjz5o3SNB3Z2dmYOHEidu3aBSA/NYTsC2Aqe5homnSmVatW8PHxQW5uLpYuXaq1diIjIxEZGQlra2vMnDlTa+2QavLy8rBkyRIAwOzZs7USECIiIiIi0pSQkBBERUUVeRNDfofhrKysYsvLdiHWZC5RsYrK22psbIxt27YJKwiOHTuGmJgYHfWMNI1BIdKpJUuWwMjICP7+/oiOjtZKG7IdtD7++GNYW1trpQ1S3aFDh3D//n04Ojri448/1nd3iIiIiKi8kEg0uCX9f7OuatasCQcHhyJvYshvsqPKkrDU1FQAqi010xcjIyOMHz9e+FndHYpJ/5hTiHSqfv362LFjB8LDw/H06VOtTEH19fWFr6+vxusl9eTm5mLhwoXo0qWLXr/tICIiIiLSBzMzM9jZ2SEhIaHYL8Zfv34tBIVky+RKq0aNGgnHnClUdjEoRDrHgE3F8sEHH+i7C0RERERUHkkkmssnpeW8VI0aNUJQUBAePXqEnJwcpdvSP3jwQDgu7bt6SXSRy4u0jsvHiIiIiIiIqOzR2NIxDe5ipkS7du0A5C8Nu3XrltJy8suw2rZtq9U+ldS9e/eE41q1aumxJ1QSDAoRERERERERadGAAQOEY39/f4Vl8vLyhJ3JbGxs0LlzZ110TS05OTnYsWOH8HOHDh302BsqCS4fI6JyR/rmJaSVVIx5i9xeVpqbo0aPxFFrKq6BochGxH8nIM1ME9eEhZXoNmBkIq68gRrfbRgaiyuv1nOug+2XRT/nIsurQxfTyNVpo5xshy0Re92Kfa0DgIEOtsMWe46hDl67Otn3XhfLLNQZRwVd/qGL95LS2kZ5YiBR432riLq0yNvbG+3bt0dQUBC2b9+OMWPGoE2bNgXKrFq1Cvfv3wcAzJw5E8bGBd/HAwIChEDRmDFjsHPnTq309fLly2jevHmxW9LL+tq3b99Sn/+IlGNQiIiIiIiIiEjL1q1bh7Zt2yI9PR3du3fH3Llz0blzZ6Snp2P//v3YsmULAMDd3R2zZs1Su523g0V37twRjs+dO4cnT54IP7u6ugpL22R27dqFfv36oV+/fujUqRPq168PKysrpKSk4NatW9iyZYuwdMze3h7r1q1Tu6+kfwwKERERERERUdmjyVxAWs4pBADNmzfHgQMHMGrUKCQlJWHu3LmFyri7u+PMmTMFtrEXa9y4cUrvW758eYGfx4wZUygoBAApKSnYu3cv9u7dq7QuDw8P7N+/H87Ozmr3lfSPQSEiIiIiIiIiHejbty/u3r2LdevW4cyZM4iOjoaJiQlcXV0xZMgQTJ8+Hebm5nrt45dffolmzZrh+vXruHfvHl6+fIlXr17B1NQU1atXR8uWLTF48GAMHDgQhjpZ4kvaxKAQERERERERlUEa3JJeh/m16tati9WrV2P16tWizuvUqROkKuSdUqVMURo2bIiGDRvik08+KVE9VDYwKERERERERERlj0SiweVjFTxpN1VY3JKeiIiIiIiIiKgC4kwhIiIiIiIiKnskGlw+xplCVEFxphCRBri6usLV1RUjR47Ud1eIiIiIiIiIVMKZQkQaEB4eDgBwcHDQc0+IiIiIiCqIMrYlPVFpxKAQERERERERlT0GBvk3TdVFVAExKESkASXd9pGIiIiIiIhI1xgUIiIiIiIiojJHIpFAoqEE0Zqqh6is4Rw50pqsrCy4ublBIpHg8OHD+u5OuSP7EFy0aJG+u1Kk3r17QyKRYOHChfruChEREREREclhUIi0Zt26dXj06BGaNGmCQYMGKS33/PlzzJs3D56enrCxsUGlSpXg7OyMMWPG4Pr16zrp65MnT4Qgy5MnT4ot/+bNG/z0008YMmQI3NzcYGtrC2NjY9jZ2cHDwwO+vr7w9/fHmzdvtN730kD22AUEBBS6b/78+QCAlStXIjo6Wsc9IyIiIqJySyL5L9l0iW+cKUQVE4NCpBXJyclYvnw5AGDevHlKp2OePHkS7u7u+P7773H79m0kJiYiIyMDT548we7du9G2bVvMmTNHl10vUl5eHpYvXw5nZ2dMmTIFhw8fxqNHj/DmzRvk5OTg1atX+Ouvv/Dzzz/jww8/RI0aNTBjxgzEx8frtJ8BAQFFBmp0qXXr1ujWrRvS0tKwZMkSvfaFiIiIiIiI/sOcQqQVmzZtQkJCAhwdHTFkyBCFZYKCgjB48GBkZ2fD1NQUM2bMQO/evVG5cmU8ePAAa9aswa1bt7Bs2TJUqVIFs2fP1vEoCkpPT8eIESNw4sQJAICxsTF8fHzQrVs31KtXDzY2Nnj9+jUiIyNx6dIlnD59GomJifDz80Pbtm0xfPhwjfanLCW3njVrFi5evIjt27dj/vz5qFmzpr67RERERERlnUSiuRk+nClEFRSDQqRxubm58PPzAwCMGDECBgq2d5RKpZg6dSqys7NhaGiIM2fO4L333hPu9/T0xJAhQ9CnTx9cvHgRCxYswPDhw1GnTh2djeNtkydPFgJCrVu3xt69e+Hs7Kyw7Lhx45CUlAQ/Pz989913uuxmqdS1a1fY29vjxYsX2Lx5c6nPg0REREREZcH/L/3SVF1EFRBf+aRxFy9eRFRUFABg5MiRCsvcunULf/31FwBg+PDhBQJCMiYmJtiwYQMAICMjA+vWrdNSj4t3+PBh7NmzB0B+wOp///uf0oCQjJWVFebOnYvbt2/D1dVVF90stQwNDTFs2DAAgL+/f5ma5URERERERFReMShEGnfw4EEAgJubGzw8PBSWCQ0NFY579uyptC43Nze4uLgAAI4cOaLBXoojy4UjkUjg7++PSpUqqXxugwYN0LJlyyLL/PXXX5gxYwY8PDyEpNU1atRA165d8cMPPyA2NrbQOYp2H5MlzO7cubPwu86dOwtlZbedO3ciOzsbNWrUgEQiwfvvv1/sOP766y/h/B9++EHl8cvIko0/ffoUwcHBos+Pjo4u8qboMSIiIiKicky2fExTN6IKiMvHSOMuX74MIH+JlTIJCQnCcfXq1Yusr3r16ggPD8eTJ0/w9OlTODo6aqajKrp79y7CwsIA5AdYlAW61JGbm4vZs2dj7dq1hWbPPH/+HM+fP8elS5dw79497Ny5U2PtAvk5kUaPHo0VK1bg4sWLiImJQe3atZWW37FjBwDAyMgIo0ePFt2el5cXDA0NkZubi7Nnz6Jdu3aiztfn0kEiIiIiIqLyiDOFSKOio6OFLd29vLyUlrO0tBSOExMTi6xT/v579+6VrINqCAwMFI5VmVEjxqRJk7BmzRpIpVLUrFkT33//PS5fvozbt2/j/Pnz+Pbbb9G0aVOV66tduzb+/PNPIYAD5Adz/vzzzwK3AQMGAAAmTJgAIH9Xtd27dyutNzs7Gz///DOA/JldNWrUED1Wc3NzNG7cGEDBx5SIiIiISC0GBpq9EVVAnClEGnXt2jXhuHnz5krLNWzYUDgODAwUlha97cWLF3jw4IHw89OnTzXQy8KcnJyU5rm5e/eucNyiRQuNtXny5EkheNOmTRv8+uuvsLGxKVCme/fumDdvnpCjqTjGxsZo0qQJ4uPjhd85OzujSZMmCsu7u7ujffv2CAoKws6dOzFnzhyF5U6fPo2XL18CAD788MNC96uaI8jT01OYeSWVSiERMU23uMcgNjYW3t7eKtdHRERERGUcdx8jKjEGhUijoqOjhWN7e3ul5dq1awdbW1u8fv0a/v7+mDFjBtzc3AqVmz9/PnJzc4Wfk5OTNdthFcgHWKpVq6a0XFpaGiIiIhTeZ2JiAnd39wK/W7ZsGYD8GTSHDx8uFBCSp82lUxMmTEBQUBAePnyI4OBgtG3btlAZf39/APnPaZ8+fdRuS/aaSEtLQ1xcnKit6R0cHNRul4iIiIiIiArjHDnSKNlsEgCwtbVVWs7c3Bxff/01ACAlJQUdO3bEnj17kJCQgKysLNy9exejRo3Cli1bYGJiIpyXnp6uvc4rIR+IsrCwUFru9u3b8PDwUHjr3r17gbIJCQm4ceMGAGDYsGGoVauWdjqvgiFDhsDa2hrAf8Efec+fP8fZs2cBAL6+vjAyUj+WXKVKFeE4Li5O7XqIiIiIiPJnChlo6MaZQlQxMShEGvXq1SvhuKigEAB89tlnQk6b2NhYjB49GlWrVoWpqSmaNm2KX375Bc7Ozvjiiy+EcypXrqydjhdBvs3U1FSN1Hnnzh1hyVX79u01Uqe6KlWqhA8++ABA/s5xaWlpBe7fs2cPcnJyACheOiaG/GtCU48lERERERERqYdBIdIoMzMz4bi4WT0SiQRbt27FoUOH8O6778LQ0FC4z9raGtOmTcPt27cLJKUuLtCkDXZ2dsKx/Eyot7Vr1w5SqbTArW7dugrLyi9JE7OESltkwbnk5GQcPny4wH2y2UOtWrVCo0aNStSO/GvC2Ni4RHURERERUQXHLemJSoxBIdIo+Zw78rOGijJ48GAEBwcjKSkJ4eHhiIyMREJCAvz8/GBjY4N///1XKCvbvUqX5Hf/km1NX960aNFCSAwuv4Ts999/F3Z8K+ksIaDga6KoHEpERERERESkfQwKkUbJB4Vev34t6lxzc3PUq1cPjo6OBWYN3bp1C0D+LKR33nlHMx0VoWPHjsLxuXPnNFJn1apVhePY2FiN1FlSstlCgYGBePz4MYD/AkTm5uYYPnx4iduQf01oM3k2EREREVUEEg3fiCoeBoVIozw8PITjhw8flri+R48e4c6dOwCAgQMH6mXJ0TvvvINmzZoBAP73v/8JM2dKonnz5sJ27FeuXClxfW8Ts9W7zMiRI1GpUiVIpVLs3LkT6enp2L9/PwBg0KBBsLKyKnG/ZK8JZ2dnmJubl7g+IiIiIqrAuHyMqMQYFCKNatmypZBX6ObNmyWub8GCBcLxtGnTSlyfuubMmQMAkEqlGDt2LDIyMkpUX5UqVfDuu+8CyE/u/OzZsxL3UZ58bqfMzEyVzrG2tsbgwYMBALt27cLhw4eRmJgIQDNLxwAgNDQUQH5+IiIiIiIiItIvBoVIo0xMTIR/+ENCQoosm5ycjJSUFKX3L1++HPv27QMAjB49Gm3btlVYLiAgABKJBBKJBGPHjlWv48UYOnSosEPXzZs30a1bN0RGRhZ5TlpaWpEBmS+//FIoN2TIECEAo0h0dLSo/sonrw4PD1f5PNkSssjISGHXNxcXlwJL6NQVEREhJNju3r17iesjIiIiogqOM4WISsxI3x2g8qd///4IDAxESEgIkpOTlW4j/88//6Bbt24YPHgwunbtinr16iE3NxcPHjyAv7+/sKzK09MT69ev1+UQFNq6dSsSExNx5swZXL16Fe7u7hg8eDC6deuGevXqwdraGunp6Xj69CmuXbuGffv24cWLFwCgcKlU3759MX78eGzfvh3Xrl1Do0aNMH36dLRt2xZWVlaIj49HaGgoDhw4gKZNm2Lnzp0q99XR0REODg6Ijo7GypUr4eDggPr16wu5mqpXr67weenQoQPc3d3x8OFDxMXFAQDGjh2r1nK0t126dAkAYGRkhD59+pS4PiIiIiIiIioZBoVI40aPHo05c+YgIyMDx44dw+jRo5WWffPmDbZt24Zt27YpvL9v377YtWsXrK2tldYhv825/PbxmmZubo6TJ09i6dKlWLFiBRITE7F3717s3btX6TlmZmYYN24cvvnmG4X3b968GZUqVcKGDRvw7NkzzJ07V2E5+R3QVDV37lxMnToVjx8/Rv/+/Qvc5+/vr3RW1fjx44VZTAYGBhqbfSV7nHr06FEgITkRERERkXo0mSCaM4WoYuLyMdI4Ozs7+Pj4AECRAZP69evDz88PAwcOhKurKypXrizsQObr64vz58/j5MmTsLW1LbK969evA8ifgTJjxgzNDUQBAwMDfP3113jy5Ak2btwIHx8fuLi4wNraGkZGRrCzs0OjRo0wcuRIbN68GbGxsdi4cWOB3cbkGRoa4scff0RoaCgmTZoEd3d3WFhYwNjYGDVq1ED37t2xevVqrFy5UnRfp0yZgiNHjqB79+6wt7eHkZFqMWBfX1/huFu3bnBwcBDd9ttiYmKEmV9Tp04tcX1ERERERIAml44xKEQVE2cKkVbMnDkT+/btw2+//YbIyEjUrVu3UJnKlStj2rRpJU4gLQs2+Pr6wsnJqUR1qcrGxgZTpkzBlClTNFJfixYtsHnzZlHnSKXSYsv4+PgIATpV/fXXX8KxphJM+/v7Iy8vDw0bNkTPnj01UicRERERERGVDGcKkVa0atUKPj4+yM3NxdKlS7XWTmZmJn7//XcYGhri66+/1lo7FcmOHTsA5M/4envZmTpSUlKwdu1aAMDChQs1kp+IiIiIiEhYPaapG1EFxKAQac2SJUtgZGQEf39/0btnqSokJAQZGRn44IMP4OLiopU2KpLw8HAcPnwYADBu3DiYmpqWuM4NGzYgISEB3t7eGDp0aInrIyIiIiIiIs3g8jHSmvr162PHjh0IDw/H06dPNZKb5m3t27dXaRkVKRcTE4O0tDRERETgyy+/RE5ODszMzPDpp59qpP7KlStj4cKF8PHx4SwhIiIiItIgJpomKikGhUir5JMWU+k0cuRIBAYGFvjdt99+i1q1ammkfiaWJiIiIiKtEJJEa6guogqIQSEiAgCYm5vD3d0dn3zyCcaMGaPv7pSMVRXAWvGObyUlUedbpLwcceXVmf2Wky2yjTzRTYgduTQlUXQbyMsVVz41SXwbhoaiiqs1DrF/WBoZi28jK0Nc+ews8W2IfS2+jBPfhtixJ70W30a2yOsjJUV8G2Llib8GpfHx4pp4I34ceZkiHys1iG0j53Wa6DZSEsVdH9I88e+7Ca9EtqHGW3tKprj3xCw13tvFDl2d/5tfZYv7HDRSoxEDkeekq3EN5op8Eu+nib+eMkU+IUm54schRgq0Wz8R6R+DQkQVXEBAgL67QEREREQkngQanCmkmWqIyhommiYiIiIiIiIiqoA4U4iIiIiIiIjKICaaJiopzhQiIiIiIiIiIqqAOFOIiIiIiIiIyh7uPkZUYpwpRFTKHT16FK6urnB1dcXRo0f13R0iIiIiolJCouEbUcXDmUJEpVxSUhLCw8OFYyIiIiIiIiJNYFCIiIiIiIiIyh4uHyMqMQaFiEq5sWPHYuzYsfruBhEREREREZUzDAoRERERERFR2cOZQkQlxkTTpHNZWVlwc3ODRCLB4cOH9d0dUsGZM2fQo0cPVK1aFYaGhpBIJLCxsVHp3N69e0MikWDhwoXa7SQRERERVTBMNE1UUgwKkc6tW7cOjx49QpMmTTBo0CCFZR49eoR9+/bh008/Rdu2bWFubg6JRAKJRIKdO3eKai8nJwc//fQT2rdvj2rVqqFSpUpwcXHB5MmT8ffff2tgRMVbtGgRJBIJOnXqpFL5P/74AwsXLkSHDh3g6OiISpUqoVKlSqhZsyY6dOiAzz77DAEBAcjLy9NuxwFs3LgRffr0wYULF5CQkKCwTdlzExAQUOi++fPnAwBWrlyJ6OhobXeXiIiIiIiIVMTlY6RTycnJWL58OQBg3rx5kCiYphkYGKhy8KQ48fHx6NWrF27evFng9xEREdiyZQt27doFPz8/TJgwQSPtlVR0dDQ+/vhjHD9+HFKptND9cXFxiIuLQ1BQENasWQMnJycsXLhQazmH0tLSMHfuXABAgwYN8N1338HFxQVGRkYwNDRUqY7WrVujW7duuHjxIpYsWYKNGzdqpa9EREREVLHIvpjUVF1EFRFnCpFObdq0CQkJCXB0dMSQIUMUlpEPhhgYGKBx48bw9vYW3VZubi4GDhwoBIR8fHxw9uxZ/P7771i/fj3s7e2RmZmJyZMn4+zZs+oNSINu3boFb29vHDt2DFKpFA4ODpg1axaOHj2Ka9eu4ebNmzhz5gyWLl2Kjh07QiKR4MmTJ/joo4+01qfQ0FAkJiYCyJ/pM2jQIDRr1gxNmjRBw4YNVa5n1qxZAIDt27cjNjZWK30lIiIiIiIicThTiHQmNzcXfn5+AIARI0bAwEBxTLJ27dpYsWIFvLy84OnpCUtLS+zcuRMhISGi2tu1axeuXr0KAJg6dSo2bNgg3Oft7Y2ePXvC09MTSUlJ+Pjjj3H//n0YGennkoiNjUXv3r3x/PlzAMCCBQswd+5cmJqaFirbq1cvfPXVV7h79y7mzZuHCxcuaK1fMTExwrG7u7va9XTt2hX29vZ48eIFNm/ejEWLFmmgd0RERERUoTHRNFGJcaYQ6czFixcRFRUFABg5cqTScm5ubvj888/RsWNHWFpaqt3eypUrAQBVqlTBihUrCt3v6uqKOXPmAMjPYXTs2DG12yqpiRMnCgGh5cuXY/HixQoDQvLeeecdnDx5Elu3btVavzIzM4VjY2NjtesxNDTEsGHDAAD+/v4Kl8YRERERERGRbjEoRDpz8OBBAPlBHw8PD6229fDhQ9y/fx8AMHToUJibmyssJ5+LR19BoT/++ANnzpwBAHh5eWH27Nmizvf19S3y/pcvX2LevHlo3rw5bGxsYGZmBicnJ/j6+gozqd7WqVMnSCQSjBs3Tvids7OzsG5bWVLposiSij99+hTBwcGiziUiIiIiUow7jxGVBINCpDOXL18GkJ94WNvkgx0dO3ZUWq5GjRrCsih9BSp27dolHM+YMUOjSe4uXLgAV1dXfP/997hz5w4SExORmZmJyMhI/Pzzz2jfvj2mT5+uk13MvLy8hOTUpSGHExERERGVdZL/lpCV9MbAEFVQzClEOhEdHY0nT54AyA8OaNu9e/eE4wYNGhRZtkGDBnj48CGioqKQmpoKCwsLbXevgMDAQOH4/fff11i9d+7cQd++fZGVlQVjY2NMnz4d/fr1g4WFBcLCwrBs2TI8fvwYGzZsgIWFhbArHJC/xCs1NRUnTpzAvHnzAADnz59HrVq1hDLOzs6i+mNubo7GjRvj7t27BcasquK2s2cCayIiIiIiInEYFCKduHbtmnDcvHlzrbcnH0BwcHAosmydOnUA5O96Fh0djfr162u8P4sWLVKaXPnu3bsA8vtZrVo1jbU5adIkZGVlwdDQEKdPn0b37t2F+7y8vDBkyBC0a9cO9+7dw8qVKzF69Gg0btwYwH8Bn9DQUOEcd3d3ODk5KWxL1RxBnp6euHv3LsLCwiCVSkXNipI9T0REREREAJhomkgDGBQinZAP0tjb22u9veTkZOG4uGTV8jODUlJStNYnRRITE5GTkwMAxQaEnj17hlevXim8z97evsDjGhISgps3bwLIT2ItHxCSsbW1xZYtW9CuXTvk5eVh48aNBXZo0wZZH9PS0hAXF4eaNWtqtT0iIiIiIiJSjkEh0omXL18Kx7a2tlpvLyMjQzg2MTEpsqz8Ll/p6ela65Mi8sGr4patLViwANu3b1d438KFCwvMRPrtt9+E4/Hjxyuts23btmjYsCHu379f4BxtqVKlinAsNigk27lOmdjYWHh7e6vdNyIiIiIqazSZC4gzhahiYlCIdEJ+hosugkJmZmbCcVZWVoGf3ya/7XqlSpW02q+3Va5cWThOTU3VWL1//fUXgPyAWLNmzYos26pVK9y/fx///vsvsrKyig2ilYT8cy92vMUtAyQiIiKiCobLx4hKjLuPkU7IB2V0MRtHPthS3JIw+eBEcUvNNM3a2hpGRvmxWfnZVIps27YNUqlUuMl2c1NEFoSrUqWKUL8yNWrUAJCfF+j169diui+a/HNvbGys1baIiIiIiIioaAwKkU7I58tRlhdHk+RnlRS3a5VsWZJEItHLbJR33nkHQH4/4+PjNVq3Jre31wT5597GxkZ/HSEiIiKisk+i4RtRBcSgEOmEfFBI27NRAKBRo0bC8YMHD4osK7u/Tp06Ot+OHgA6duwoHJ87d04jdcpy9yQkJAiJrJWJi4sDkB9A0vbSPvnnnruJERERERER6ReDQqQTHh4ewvHDhw+13l67du2E48DAQKXl4uLihP60bdtW6/1SZPTo0cKxn5+fytu7F6VJkyYA8vMp3blzp8iyISEhAAA3Nzet5hMC/nvunZ2dYW5urtW2iIiIiKi841QhopJiUIh0omXLlkJeIdlW6drk7u6Ohg0bAgAOHjyItLQ0heV27twpHA8cOFDr/VKkWbNm6NmzJwDg999/x5o1a0pcZ9euXYXjHTt2KC13/fp13Lt3r9A52hIaGgogP7k1ERERERER6ReDQqQTJiYmQiBANjNF2z7//HMA+Xlsvvjii0L3h4eHY+nSpQAAV1dXpUGhsWPHQiKRQCKRICAgQCt93bp1q7DE7vPPP8e3336LrKysIs8pahmet7c3WrZsKdR96dKlQmUSExMxefJkAICBgQGmTJmibvdVEhERIeRM6t69u1bbIiIiIqIKQLb7mKZuRBUQt6Qnnenfvz8CAwMREhKC5OTkAjuEve3w4cMFdg27evWqwmMgf/es999/v1AdY8aMwY4dOxAcHIwNGzYgLi4OEydOhK2tLUJCQvDtt98iKSkJBgYGWL9+fbG7dGlT7dq1cfr0afTr1w/Pnz/HggULsG3bNgwfPhxt27ZF9erVYWpqijdv3uCff/7B2bNncfr0aeF8RUuxtm7dilatWiErKwu9evXCjBkz0LdvX1hYWCAsLAzLli1DREQEgPxAlGzJmbbIAlNGRkbo06ePVtsiIiIiogqAW9ITlRiDQqQzo0ePxpw5c5CRkYFjx44VyKXzts8//xyRkZEK79u+fTu2b98u/NyxY0eFQSFDQ0McP34cvXr1ws2bN3HkyBEcOXKkQBlTU1P4+fkJy7cUkd9G3c7OTmm5kvL29kZISAhmzJiBkydP4unTp/jhhx+KPKdu3bpYuHAhxo4dW+i+Zs2a4dSpUxgyZAiSkpKwatUqrFq1qlC5adOmCTOmtGnv3r0AgB49ehRIPE5EREREVJFERkZi/fr1OHPmDKKiomBqagoXFxcMHToU06ZN01juzbNnz2LLli24efMmXr58iWrVqsHLywuTJk0q8v8feTk5Odi2bRt++eUXPHjwACkpKahVqxa6du2Kjz/+GI0bN9ZIX0l/GBQinbGzs4OPjw/27duHvXv3FhkU0pSqVavi2rVr2Lp1K/bu3Yv79+8jNTUVtWrVwnvvvYeZM2cW+0Z248YNAMB7771XIGG2Njg6OuLEiRO4c+cOjhw5gsuXL+PJkydISEgAANja2sLZ2RleXl7o06cPunTpAgMD5atAu3fvjkePHmHt2rX49ddfERERgczMTFSvXh3t27fHRx99VCApt7bExMTgypUrAICpU6dqvT0iIiIiqgg0mSBaNzOFTp06hVGjRiEpKUn4XVpaGkJDQxEaGopt27bhzJkzcHV1VbuNvLw8TJo0qcAX6UD+3+QxMTE4fvw4JkyYgM2bNxf5v0R8fLzwBbu8iIgIbNmyBbt27YKfnx8mTJigdl9J/yRSTWx1RKSi33//Ha1bt4ahoSHCw8NRt25dfXepSE+ePIGzszOA/F3MOnTooOcelU3fffcd5s+fj4YNG+Lvv/+GRAvTc6Ojo4Vt7p/euQ6HWjU13kY+NfqelyOuvDpvyznZItvIE99GZnrxZeSbyEgV30ZerrjyqUnFl3mboaGo4tKURPFtiH2NGxmLbyMrQ1z57KLzlCkk9rX4Mk58G2LHnqQ8n5pS2SKvD7nly1qTJ/4alP5/XjaVm3gjfhx5mSIfKzWIbSPnteLNIoqSkiju+pDmiX/fTXglsg013tpTMsW9J2ap8d4udujqfIS/yhb3OWikRiMGIs9JV+MazBX5JN5PE389ZYp8QpJy1fg8FyEFefgF+Z/nUVFRcHBw0Gp7qpD/my/q/l041K6lmXpjnqFOw3fy69XSWMPCwtC2bVukp6fD0tISc+bMQefOnZGeno79+/dj69atAPI3zQkNDS0y3UZR5syZg2XLlgEAmjdvji+++AIuLi4IDw/HDz/8gLCwMKHckiVLFNaRm5uLTp06Cak7fHx8MHHiRFSpUgW///47vvvuO7x48QIGBgY4ffq0yjOPqPRhomnSqVatWsHHxwe5ubk6WbJUUrLt7Dt27MiAkJpSUlKwdu1aAMDChQu1EhAiIiIiogpIAg0mmtZ+d2fOnIn09HQYGRnhwoULmDt3Ltq0aYMuXbpgy5YtQuqIhw8fKkz7oIqHDx9i5cqVAPJ3gA4ODsbw4cPh5eWF4cOH4+rVq8KGNCtWrMCjR48U1rNr1y4hIDR16lQcOXIE77//Pry9vTFjxgwEBwfDysoKeXl5+Pjjj5GTI/JLUCo1GBQinVuyZAmMjIzg7++P6OhofXenSLIlTwsWLNBzT8quDRs2ICEhAd7e3hg6dKi+u0NERERE5UUZ2n0sJCQEQUFBAIDx48ejTZs2hcrMmjULDRs2BACsW7cO2WJnugJYu3atEKD58ccfUalSpQL3m5ub48cffwSQny9ozZo1CuuRBZaqVKmCFStWFLrf1dUVc+bMAQA8evQIx44dE91XKh0YFCKdq1+/Pnbs2IE5c+bg6dOn+u5OkbZv3w6pVIouXbrouytlVuXKlbFw4UJs3bqVs4SIiIiIqEI6fvy4cDxu3DiFZQwMDIS8q2/evMHly5dFtSGVSnHixAkAQIMGDdC6dWuF5Vq3bo369esDAE6cOIG3M8o8fPgQ9+/fBwAMHTpUaeJr+c1uGBQqu5homvTC19dX310gHWFiaSIiIiLSjrKTaFq2FMvCwgKenp5Ky3Xs2FE4Dg4ORvfu3VVu4/Hjx3j27FmhepS1888//yAmJqZAHlX5vhZXT40aNeDu7o6HDx8iODhY5X5S6cKZQkRERERERERaJJt54+rqCiMj5XMzGjRoUOgcVd27d09hPWLbUaeeqKgopKaqsckI6R1nChFRuSCf3C72+QsttlRKdx/LFduGOruPidxpJ1P8jkGi+5WWLL4NA5G7j6WpscOZ2KWShmrsPpadKbK8GruPQeRr8ZUaO7WJHXuKGs+HyJ2PkKaDP2rV2O1KmixuB8C8VJE71AHIy9J+olCp2N3HMsS/dlOzxLWhzu5jb0QmVVXnrT01V9zuY9lqNKKL3cfeiNxZ0lAHu49l6GD3sSQ1PmuzRLaRAu3uPpYmV39pTCQc+/yFxnIByf/9GBsbW2x5MbuTZWRkIP7/d5As7jxbW1tYWFggNTUVUVFRKrcBoEC+1uLake3gBqBQO+rUI5VKER0dLSxLo7KDQSEiKhdevnwpHLfq0V+PPSEiIiIqf16+fAknJyd9d6MA7w7ayfvp7e1dbJm38/AUJTn5vy+xLC0tiy0vCwqlpKSo3IbYdiwsLITjt9vRVD1UNnD5GBEREREREZGWZGT8N3PTxMSk2PKmpqYAgPR0cTNExbQja0NRO5qqh8oGzhQionLBw8MDISEhAIBq1aoJa7VjY2OFb3tCQkJQs2ZNvfWxJDiO0oXjKF04jtKF4yhdOI7SpayNIycnR5iN7eHhoefe5KtRo4boZVVixMbGFvhbUhPMzMyE46ys4pfEZmbmLxF/ezt5TbYja0NRO2/XI/+zmHqobGBQiIjKBTMzM3h5eRVZpmbNmqLWf5dWHEfpwnGULhxH6cJxlC4cR+lSVsZR2paMGRkZafVx00bdlStXFo5VWWIlS9isylIzdduRTwr9djtv11NUUKioeqhs4PIxIiIiIiIiIi0xMzODnZ0dgIJJnBV5/fq1EGiRTwatCvmAVnHtyM+2ersddeqRSCRlIshJhTEoRERERERERKRFjRo1AgA8evSoyJ3cHjx4IBw3bNhQrTberkdsO+rUU6dOnQJJp6nsYFCIiIiIiIiISIvatWsHIH+51a1bt5SWCwwMFI7btm0rqg1nZ2fUqlWrUD2KXLlyBQBQu3btQksEZX0trp64uDg8fPhQrb5S6cGgEBEREREREZEWDRgwQDj29/dXWCYvLw+7d+8GANjY2KBz586i2pBIJOjfvz+A/Bk8N27cUFjuxo0bwgyf/v37QyKRFLjf3d1dmD108OBBpKWlKaxn586dwvHAgQNF9ZVKDwaFiIiIiIiIiLTI29sb7du3BwBs374d169fL1Rm1apVuH//PgBg5syZMDY2LnB/QEAAJBIJJBIJxo4dq7CdTz75BIaGhgCAGTNmFNomPj09HTNmzACQn7T7k08+UVjP559/DgB49eoVvvjii0L3h4eHY+nSpQAAV1dXBoXKMAaFiIiIiIiIiLRs3bp1qFSpEnJyctC9e3csXboUN27cwOXLlzF58mQh+OLu7o5Zs2ap1Ya7uztmz54NAAgNDUXbtm1x4MABhIaG4sCBA2jbti1CQ0MBALNn7Vx4/AAAHRZJREFUz4abm5vCesaMGSMsCduwYQMGDx6M8+fPIyQkBH5+fnj33XeRlJQEAwMDrF+/HkZG3Ni8rOIzR0RERERERKRlzZs3x4EDBzBq1CgkJSVh7ty5hcq4u7vjzJkzBbaFF+v777/HixcvsGPHDoSFhWH48OGFyowfPx7fffed0joMDQ1x/Phx9OrVCzdv3sSRI0dw5MiRAmVMTU3h5+eHnj17qt1X0j+JVCqV6rsTRERERERERBVBZGQk1q1bhzNnziA6OhomJiZwdXXFkCFDMH36dJibmys8LyAgQMgzNGbMmAI5fRT59ddfsWXLFty8eRPx8fGoWrUqvLy8MHnyZJUDOTk5Odi6dSv27t2L+/fvIzU1FbVq1cJ7772HmTNnonHjxqLGTqUPg0JERERERERERBUQcwoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoREREREREREVVADAoRUbkVGRmJWbNmoUGDBrCwsECVKlXg5eWFFStWIC0tTd/dU9uXX34JiUQi3AICAvTdpWJlZWVh27Zt6NGjB2rWrAlTU1NYWlqifv36GDduHK5du6a3vr148QKnT5/GggUL0LNnT1StWlV4bMeOHatSHWlpaTh69CimTJkCLy8v2NrawtjYGHZ2dmjTpg0WLVqEuLi4Uj+Ot/32228YO3YsXF1dYWFhAWtra7i7u2Pw4MHYtGkTUlJSNDsIAKGhofjmm2/QvXt3ODg4CK8Vd3d3jBs3DlevXhVV39mzZzFw4EChLgcHBwwcOBBnz57VeN/laXoc8tLS0lCvXj3h+XVyctJcx9+iyXE8efIEX375JTw9PWFjYwNjY2NUqVIF7777Lr755hu8ePFCK2NISkrC/v37MWvWLHTs2BGurq6wtraGiYkJ7O3t0alTJ/zwww9ISEhQWkd2djbOnTuHTz/9FO+++y6qVq0KY2Nj2NjYoEWLFpg9ezYiIiK00n9NjuNtISEhmDp1Kho2bAgrKytYWlrCxcUFvXv3xurVq/Hy5UstjkgxdT7j9HWdF6Wkn9W6vM6JqIKTEhGVQydPnpRaWVlJASi8ubu7S//99199d1O0sLAwqZGRUYGxXL58Wd/dKtKTJ0+kjRs3VvpcyG4zZsyQ5uXl6bx/RfVpzJgxxZ7/xx9/SC0tLYsdn5WVlXT//v2ldhzyXr16Je3fv3+xYwoLC9PoGNq3b19smwCko0ePlmZmZhZZV25urnT8+PFF1jNhwgRpbm6uRseg6XEoMmvWrAL11K1bV+NjkEo1O47du3dLK1WqVGQ9VapUkV64cEHj47h48aJK46hatar03Llzhc5/8eKF1M7OrtjzTUxMpGvXrtV4/zU1DnkZGRnSCRMmSCUSSZF1HTt2TGvjUUTsZ5w+r/OiaOKzWlfXORGREYiIypmwsDAMGzYM6enpsLS0xJw5c9C5c2ekp6dj//792Lp1Kx4+fIjevXsjNDQUlStX1neXVZKXl4dJkyYhJycH9vb2WvtWXZOys7PRu3dv/P333wCAd955B5999hnq16+P5ORkXL16FatWrUJqaip+/PFH1KpVC1999ZXe+uvo6IgGDRrgwoULKp+TlJQkzJhp27Yt+vTpg5YtW8LOzg4vX77E0aNHsXXrViQlJWHkyJGwsrJCz549tTUEAOqNQyYxMRHdunXDrVu3AAADBw7E4MGD4eLiAkNDQ0RFRSEwMBBHjhzRdLfx7NkzAECtWrUwZMgQtG/fHo6OjsjNzcX169exatUqxMTEYPfu3cjOzsbevXuV1vX1119j+/btAIDmzZvjiy++gIuLC8LDw/HDDz8gLCwM27ZtQ7Vq1bBkyZJSO463hYWFYe3atTAzM4OxsTGSk5M12nd5mhpHcHAwxo4di7y8PBgYGGDMmDHo378/atWqhadPn2LXrl04deoUXr16hf79++Ovv/5CvXr1NDqWOnXqoHPnzvD09ESdOnVQs2ZN5OXlITo6GocPH8bRo0cRHx+Pfv36ISQkBE2bNhXOzczMFGbfNGvWDP3790erVq1QvXp1JCYm4uzZs/jxxx+RkZGBTz75BJUqVcKkSZM02n9NjEMmKyurwCyaLl26YOTIkWjQoAHMzMzw7NkzXLt2DYcPH9bKGJRR5zNOn9e5Mpr4rNbldU5ExJlCRFTuyL7dNjIykl67dq3Q/T/88IPwzdvChQt130E1rVmzRgpA2qBBA+mcOXPU/vZRlw4dOiT0s02bNtKcnJxCZUJDQ6XGxsZSAFIbGxtpdna2Tvu4YMEC6alTp6RxcXFSqVQqffz4sagZNsHBwdKhQ4dK//77b6Vljh8/Lnwj7+LiopUZUSUdh4yvr68UgNTU1FR64sQJpeXy8vI0/lz17t1beuDAAYWvE6lUKn358qXU3d1dGFdgYKDCcv/884/wLX3Lli2laWlpBe5PTU2VtmzZUnif0PSsQU2N4205OTlST09PKQDpN998I61bt65WZxBoahy9e/cWymzYsEFhmc8++0woM23aNI2NQSqVKu2/vGPHjgntDxw4sMB90dHR0m7dukmvX7+u9PwbN24IM6Gsra2lSUlJJe7320o6Dpn58+dLAUglEol006ZNRdaXlZWlVl/VIfYzTt/XuTIl/azW9XVORMSgEBGVK7///rvwB9jkyZMVlsnNzZU2bNhQCELo8o9edUVGRgpLlAICAqQLFy4sE0GhTz/9VOjnyZMnlZYbOHCgUO7u3bs67GFh6gZTijNo0CCh3lu3bmmsXmXUGUdQUJBwzooVK7TbQTWdOnVK6OOMGTMUlpkyZYpQRtk/8tevXxfKTJ06VZtdVkiVcbxt1apVUgDS+vXrSzMzM0vFP4uqjMPW1lYKQGpnZ6e0njdv3gj1tGjRQlvdLVL9+vWF5VfqkF/uc+TIEQ33TnVFjSM8PFwIwms6+FYS6nzGlcbrXBOf1aXxOiei8o2JpomoXDl+/LhwPG7cOIVlDAwMMHr0aADAmzdvcPnyZV10rUSmTZuGlJQUjBkzBh07dtR3d1SWlZUlHBe1HMTFxUXhOeVJ586dhePw8HA99kQ5Pz8/AIC1tTWmT5+u594oVtzjKJVKceLECQBAgwYN0Lp1a4X1tG7dGvXr1wcAnDhxAlKpVAu9VU7s6yEyMhILFiwAAPz0008wMTHRWt/EUGUcsmva2dlZaT3W1taoWrVqgfK6JltKnJGRodb5peUaL2ocW7ZsQXZ2NgwMDDBnzhxdd00psZ9xpfU6L+lndWm9zomofGNQiIjKFdlOOBYWFvD09FRaTv6PteDgYK33qyQOHjyI06dPo0qVKli5cqW+uyOK7I9xAEXuzCP7B0oikcDNzU3r/dKHzMxM4djQ0FCPPVEsKytL+CerW7duMDMzAwDk5uYiKioKT548UfufZU0q7nF8/PixkAunuH/KZPfHxMTgyZMnmuukCsS+HqZOnYrU1FT4+vqiU6dOWuyZOKqMQ/Y+8PjxY6X1JCUlIT4+vkB5Xfrnn39w584dAPlBBnWUhmu8uHEcOnQIANCiRQvUrl0bQH6AJTY2FhEREUhNTdVZX2XU+Ywrjde5Jj6rS+t1TkTlG4NCRFSu3L9/HwDg6uoKIyPlufTl/1iWnVMavXnzBjNnzgQALF++XPgmvawYMWIErKysAOT3Pzc3t1CZsLAwnDlzBgDwwQcfCOXLm8DAQOG4YcOGeuyJYn/88YcQ9PHw8EBSUhI++eQTVK1aFY6OjnB2doa1tTW6desmemtlTSrucbx3755wXNw/9/p8HxDzeti/fz9+/fVX2NraYtWqVdrumiiqjOOjjz4CACQkJOCnn35SWObbb78tVF7b0tLS8O+//2L16tXo2LEjcnJyAACffPKJWvXp6xpXdRwvX74UgvMeHh7IysrC4sWLUatWLdSqVQsuLi6wsrLCu+++i6NHj+qk7+p+xpW261wTn9Wl+TonovKNu48RUbmRkZEhfNPs4OBQZFlbW1tYWFggNTUVUVFRuuieWr744gvExcWhbdu2GD9+vL67I1rVqlWxZ88ejBgxAsHBwfDy8sInn3wCd3d3pKSkIDg4GKtWrUJWVhZatGhRbv8Q/uOPP4TAl4eHR6kMCsn/k5WXl4eWLVvi33//LVAmKysLv/32Gy5duoSlS5fiyy+/1Gkf8/LysGzZMuHnoUOHFioTHR0tHBf3PlCnTh3hWJfvA6qMQ+b169fCP/fLli1DtWrVtN09lak6jg8//BBXr17F7t27MW3aNNy6dQv9+vVDzZo18fTpU+zZs0dY+vv111+ja9euWuvzzp07lS4tBoCvvvoKH3zwgeh6Y2Nj4e/vDwCoVq1agaVk2qDOOOSvcXNzc3Ts2BE3btwoUCYvLw/Xr1/HoEGDMGXKFGzcuFGzHX+Lup9xpe06L+lndWm+zomo/ONMISIqN+S3bLW0tCy2vIWFBQAI24mXNkFBQdi2bRuMjIzw008/QSKR6LtLaunXrx9u3bqFCRMm4M6dOxgzZgzatGmDbt26YdGiRTA3N8fatWsRFBSE6tWr67u7GpeZmYkJEyYIs6S+//57PfdIsVevXgnHy5cvx7///ov3338fISEhyMjIwIsXL7Bp0yZYW1tDKpXiq6++Epab6cqaNWsQEhICAPDx8VG4RFTM+4DsPQDQ7fuAKuOQmT17Np4/f442bdpg4sSJuuqiSlQdh6GhIXbt2oVDhw6hadOm2LZtG/r16wcvLy8MGjQIx48fR+fOnXHx4kV89913uhyCoFmzZggJCcHSpUtFv9dKpVJMnjxZeO3Nnz9fWH6pa0WNQ/4a3759O27cuAFvb2/873//Q1paGl6/fo29e/eiVq1aAIBNmzZh/fr1WutrST7jStN1ronP6tJ8nRNR+cegEBGVG/L5TlRJzmhqagoASE9P11qf1JWVlYVJkyZBKpXi008/RZMmTfTdJbVlZWVh9+7dSpN8Pn/+HD///DN+++03PfRO+6ZPn47Q0FAAwJgxY9C3b18990gx+VwiGRkZ6NatG06fPg0vLy+YmpqiWrVq+Oijj3D69GkYGOT/+TBnzhydJWgODAzEV199BQCwt7fHpk2bFJYT8z4gew8AdPc+oOo4AODKlSvYsWNHqQwMixkHkL9sZ/fu3fjzzz8V3n/9+nVs374dMTExGu+rvAEDBuDPP//En3/+iZCQEOzbtw8DBw7EnTt3MGLECJw+fVp0nUuWLMGpU6cA5CebnjZtmqa7XYg643j7Gm/SpAkuX76Mzp07o1KlSrCxscGIESMQEBAgBFIWL16MtLQ0jfe/pJ9xpeU618RndWm+zomoYmBQiIjKDflvZlXZvUaWFLRSpUpa65O6lixZggcPHsDR0RELFy7Ud3fUlpqaiq5du2Lp0qV49eoVvvjiC9y/fx+ZmZlITEzEhQsX0K5dO4SGhmLAgAFYvXq1vrusUUuXLsW2bdsAAF5eXtiwYYOee6Tc2zMbli9frjBZbrt27eDj4wMg/x99Zf/ka9Lff/+NgQMHIicnB2ZmZjh06BDs7e0VlhXzPiCfGFgX7wNixpGZmSn8szlz5ky88847Wu+fqsSMA8ifSdGmTRucOnUKtWvXxp49exAXF4esrCxERUVhw4YNMDc3x/79++Ht7Y2///5ba323sbFBkyZN0KRJE3h5eWH48OE4evQodu/ejYiICPTv3x87d+5Uub5ffvkF8+fPB5C/u9revXuFoKk2qTOOt6/xb775Bubm5oXqdnNzw5QpUwDkzy7SRsC+pJ9xpeU6L+k4SvN1TkQVB4NCRFRuyLbhBVSbIi771lSVpWa69ODBAyxduhQA8OOPPxaY+l7WLFq0CEFBQQDylyssX74cDRo0gImJCaysrNCtWzfhm2qpVIrZs2fjjz/+0HOvNWPz5s2YO3cugPxEp7/++mupfi7lr59q1aqhefPmSsv26NFDOL5586ZW+/X48WN0794dr1+/hqGhIfbv348OHTooLS/mfUB+5oS23wfEjuP777/HP//8gzp16mDx4sVa7ZsYYseRmZmJESNGIDExETVq1MCNGzcwatQoVK9eHcbGxnBwcMDUqVNx5coVmJmZ4dmzZxgzZowOR5TP19cXQ4YMQV5eHqZPn15gqZUyZ86cwbhx4yCVSlGjRg1cvHgRNWrU0EFvlStqHPLXhkQiKTJ3kzavcU18xpWG61wT4yit1zkRVSxMNE1E5YaZmRns7OyQkJBQIAmlIq9fvxb+UJRPQlkarFmzBllZWahXrx7S0tKwf//+QmX++usv4fh///sf4uLiAAB9+/YtNYEHqVSKHTt2AADc3d2V/qNnZGSEb7/9Fu3atUNeXh527tyJNWvW6LKrGrdv3z5MnToVAFC3bl1cvHix1O8cJ38diEnc+vLlS6316dmzZ+jatSuePXsGiUSCHTt2oH///kWeI9/34t4H5JPOavN9QJ1xLF++HADQtWtXYWnS22TvYampqcL7hL29Pbp06aLB3v9HnXGcO3dOWBI2Y8YMpUGTxo0bY9SoUdi2bRtu3bqFP/74A02bNtX4GIrSv39/HDx4EKmpqTh37lyRCacDAgIwePBgZGdnw9bWFufPn4eLi4sOe6ucsnHIv8ZtbGwKBFbeps1rXBOfcaXhOtfEOErjdU5EFQ+DQkRUrjRq1AhBQUF49OgRcnJylG5L/+DBA+G4tO0EJZvqHhERgREjRhRbXn4r58ePH5eaoNDz58+Fb6mLmnUCoECCWvnnpiw6efIkRo8ejby8PNSsWROXLl0qNshSGjRu3Fg4liXFVkb+fmXXWEnFx8ejW7duwhbaP/74I0aPHl3seY0aNRKOi3st6eJ9QN1xyJbE+Pv7C7taFdWG7L2iY8eOWvlnUd1xyG8B3qJFiyLLenp6CsstHzx4oPOgkPyOT5GRkUrLhYSEoG/fvsjIyIClpSXOnj1bqpb9KBuHm5sbjI2NkZ2drddrXBOfcaXhOtfEOErbdU5EFROXjxFRudKuXTsA+d+o3bp1S2m5wMBA4bht27Za71dFJP+PRE5OTpFls7OzFZ5X1ly6dAlDhw5FTk4O7OzscPHixVIze6A4devWhaOjIwDgyZMnRSaQDg8PF45r166t8b4kJiaiR48ewhbay5YtUzl5r7Ozs7B7kvx1rsiVK1cA5I/ByclJ/Q4rUZJxlCYlGUdZeh+QT3KtbJnR3bt38f777yMlJQVmZmY4deoUWrVqpasuqkTZOIyNjdGmTRsAQFJSEuLj45XWoe1rvKRK03VORFTWMShEROXKgAEDhGNl37rl5eVh9+7dAPKn0Hfu3FkXXVPZzp07IZVKi7zJJ7S8fPmy8PvS9AdvlSpVYGVlBSB/Z6Gi/iGU/6Pe2dlZ633ThmvXrqF///7IzMyEtbU1zp8/X2D2TVkwaNAgAPn/MF66dElpuaNHjwrHskCspqSlpaF37964ffs2AODrr7/Gl19+qfL5EolEWNL04MED3LhxQ2G5GzduCDMI+vfvr/Edf0o6juLeA6RSKerWrQsgP6An+11AQECpGof89SzLL6aMvt8HDh06JBx7eHgUuv/hw4dCPiVjY2McOXIEnTp10mEPVVPUOGTXOAAcP35caR3y13j79u011zlo5jOuNFznmhhHabnOiaiCkxIRlTPt27eXApAaGRlJr127Vuj+H374QQpACkC6cOFC3XdQAxYuXCiM4fLly/rujlIjRowQ+rlo0SKFZV69eiVt1KiRUO78+fM67mVBjx8/FvoyZswYlc4JCwuT2tjYSAFILSwspFevXtVuJ1WgzjgiIyOlZmZmUgBSDw8PaWJiYqEye/bsEert3bu3RvucmZkp7d69u1D/zJkz1arnn3/+kRoaGkoBSFu2bClNS0srcH9aWpq0ZcuWwvvEw4cPNdD7/2hqHMWpW7euFIC0bt26WqlfE+N4/fq11NzcXApAWrlyZendu3cVlvv111+lBgYGUgDS2rVrS3Nzc0vY+//4+/tL09PTiyyzevVqYZzOzs7SnJycAvdHRkZK69SpIwUgNTQ0lB46dEhj/VOVJsaRnJwstbe3lwKQ1qpVSxoTE1OojoCAAOH6adKkiTQvL0+j41CFKp9x+r7OVaGJz2ptX+dERGV3jj4RkRLr1q1D27ZtkZ6eju7du2Pu3Lno3Lkz0tPTsX//fmzZsgVAfvLjWbNm6bm35duCBQtw4sQJpKWlYdGiRbh16xbGjBmDevXqISMjAzdu3MDatWvx9OlTAMB7772H7t2767SPV69exaNHj4Sf5ZdUPHr0qNC2zmPHji3wc3h4OHr06IE3b94AAL777jtYW1sXSDD6Nnt7+yK38FZHSccBAI6Ojvjmm2/wxRdf4M8//4S3tze+/PJLvPPOO0hKSsLRo0exadMmAICVlZXGE4KPGDECFy5cAAB06dIF48ePL/JxNDExgbu7e6Hfu7u7Y/bs2Vi2bBlCQ0PRtm1bfPnll3BxcUF4eDiWL1+OsLAwAMDs2bPh5uZWKsehb5oYh42NDb766issWLAAycnJePfddzFjxgx069YNtra2eP78OU6cOIGtW7ciLy8PQP7yNE1u675o0SLMmjULgwYNQrt27eDi4gJLS0skJyfjzz//xC+//ILg4GBhDFu2bIGhoaFwfkJCArp27SokLJ41axYaNGhQ5GNha2ur8WVXJR0HkL+cbP369RgxYgSePXsGLy8vzJkzB61atUJmZibOnTuH1atXIzc3F0ZGRvjpp580PotOU/R9nRMRlRv6jkoREWnDyZMnpVZWVsI3dG/f3N3dpf/++6++u6m2sjJTSCqVSi9evCitWrWq0udCduvSpYv01atXOu/fmDFjiu2b/O1t/v7+os6HlmaolXQc8r766iupRCJReq69vb3CWXglJfZxLOqb89zcXOmHH35Y5Pnjx4/X6IwUbYyjKNqeQaCpceTl5Uk/+eSTIl9TAKTGxsbSFStWaHwcssepuJuDg4P0woULhc6/fPmy6MdC1dl5uhyHPD8/P6mJiYnSOiwtLaXHjx/X+BhUpepnnD6vc1VwphARlQWcKURE5VLfvn1x9+5drFu3DmfOnEF0dDRMTEzg6uqKIUOGYPr06TA3N9d3NyuErl274sGDB9i+fTvOnj2Lv//+G2/evIGRkRFq1KgBLy8vfPDBB+jXr1+p/Ua6olm6dCn69euHTZs2ISgoCLGxsTAzM4O7uzv69euHGTNmwNraWt/dLJKBgQG2b9+OQYMGYcuWLbh58ybi4+NRtWpVeHl5YfLkyejZs6e+u1khSCQSrFmzRthy/urVq4iMjERaWhosLS3h6uqKjh07YvLkyVqZMXX+/HmcOXMGwcHBePToEZ4/f46EhARUqlQJ9vb2aNasGfr06YOhQ4eW6s8FTY5j2rRp6NSpEzZs2ICLFy8iJiYGhoaGqFevHt5//3188sknqFmzpo5Gpj5e50REJSeRSovYXoSIiIiIiIiIiMol7j5GRERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQBMShERERERERERFQB/R9gGdU3qg6Z1wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
diff --git a/inseq/attr/step_functions.py b/inseq/attr/step_functions.py
index 423092ed..d85c86a9 100644
--- a/inseq/attr/step_functions.py
+++ b/inseq/attr/step_functions.py
@@ -9,7 +9,7 @@
 from ..data import FeatureAttributionInput
 from ..data.aggregation_functions import DEFAULT_ATTRIBUTION_AGGREGATE_DICT
 from ..utils import extract_signature_args, filter_logits, top_p_logits_mask
-from ..utils.contrast_utils import _get_contrast_output, _setup_contrast_args, contrast_fn_docstring
+from ..utils.contrast_utils import _get_contrast_inputs, _setup_contrast_args, contrast_fn_docstring
 from ..utils.typing import EmbeddingsTensor, IdsTensor, SingleScorePerStepTensor, TargetIdsTensor
 
 if TYPE_CHECKING:
@@ -225,14 +225,17 @@ def kl_divergence_fn(
             "method used. Use --contrast_force_inputs in the model.attribute call to proceed."
         )
     original_logits: torch.Tensor = args.attribution_model.output2logits(args.forward_output)
-    contrast_output = _get_contrast_output(
+    contrast_inputs = _get_contrast_inputs(
         args=args,
         contrast_sources=contrast_sources,
         contrast_targets=contrast_targets,
         contrast_targets_alignments=contrast_targets_alignments,
         return_contrastive_target_ids=False,
     )
-    contrast_logits: torch.Tensor = args.attribution_model.output2logits(contrast_output.forward_output)
+    c_forward_output = args.attribution_model.get_forward_output(
+        contrast_inputs.batch, use_embeddings=args.attribution_model.is_encoder_decoder
+    )
+    contrast_logits: torch.Tensor = args.attribution_model.output2logits(c_forward_output)
     filtered_original_logits, filtered_contrast_logits = filter_logits(
         original_logits=original_logits,
         contrast_logits=contrast_logits,
diff --git a/inseq/data/viz.py b/inseq/data/viz.py
index df6c6d4d..86d924cb 100644
--- a/inseq/data/viz.py
+++ b/inseq/data/viz.py
@@ -177,8 +177,7 @@ def get_heatmap_type(
         )
     elif heatmap_type == "Target":
         if attribution.target_attributions is not None:
-            mask = np.where(attribution.target_attributions.numpy() == 0, float("nan"), 0)
-            target_attributions = attribution.target_attributions.numpy() + mask
+            target_attributions = attribution.target_attributions.numpy()
         else:
             target_attributions = None
         return heatmap_func(
diff --git a/inseq/utils/alignment_utils.py b/inseq/utils/alignment_utils.py
index 7a3ac877..86a1a18d 100644
--- a/inseq/utils/alignment_utils.py
+++ b/inseq/utils/alignment_utils.py
@@ -302,6 +302,10 @@ def get_adjusted_alignments(
             ).alignments
             alignments = [(a_idx, b_idx) for a_idx, b_idx in alignments if start_pos <= a_idx < end_pos]
             is_auto_aligned = True
+            logger.warning(
+                f"Using {ALIGN_MODEL_ID} for automatic alignments. Provide custom alignments for non-linguistic "
+                f"sequences, or for languages not covered by the aligner."
+            )
         else:
             raise ValueError(
                 f"Unknown alignment method: {alignments}. "
@@ -310,11 +314,21 @@ def get_adjusted_alignments(
     # Sort alignments
     alignments = sorted(set(alignments), key=lambda x: (x[0], x[1]))
 
+    # Filter alignments (restrict to one per token)
+    filter_aligns = []
+    for pair_idx in range(start_pos, end_pos):
+        match_pairs = [(p0, p1) for p0, p1 in alignments if p0 == pair_idx and 0 <= p1 < len(contrast_tokens)]
+        if match_pairs:
+            # If found, use the first match that containing an unaligned target token, first match otherwise
+            match_pairs_unaligned = [p for p in match_pairs if p[1] not in [f[1] for f in filter_aligns]]
+            valid_match = match_pairs_unaligned[0] if match_pairs_unaligned else match_pairs[0]
+            filter_aligns.append(valid_match)
+
     # Filling alignments with missing tokens
     if fill_missing:
-        filled_alignments = []
+        filled_alignments = filter_aligns.copy()
         for step_idx, pair_idx in enumerate(reversed(range(start_pos, end_pos)), start=1):
-            match_pairs = [pair for pair in alignments if pair[0] == pair_idx and 0 <= pair[1] < len(contrast_tokens)]
+            match_pairs = [(p0, p1) for p0, p1 in filter_aligns if p0 == pair_idx and 0 <= p1 < len(contrast_tokens)]
 
             # Default behavior: fill missing alignments with 1:1 position alignments starting from the bottom of the
             # two sequences
@@ -323,24 +337,23 @@ def get_adjusted_alignments(
                     filled_alignments.append((pair_idx, len(contrast_tokens) - 1))
                 else:
                     filled_alignments.append((pair_idx, len(contrast_tokens) - step_idx))
-            else:
-                # If found, use the first match that containing an unaligned target token, first match otherwise
-                match_pairs_unaligned = [p for p in match_pairs if p[1] not in [f[1] for f in filled_alignments]]
-                valid_match = match_pairs_unaligned[0] if match_pairs_unaligned else match_pairs[0]
-                filled_alignments.append(valid_match)
-        if alignments != filled_alignments:
-            alignments = filled_alignments
+
+        if filter_aligns != filled_alignments:
+            existing_aligns_message = (
+                f"Provided target alignments do not cover all {end_pos - start_pos} tokens from the original sequence."
+            )
+            no_aligns_message = (
+                "No target alignments were provided for the contrastive target. "
+                "Use e.g. 'contrast_targets_alignments=[(0,1), ...] to provide them in model.attribute"
+            )
             logger.warning(
-                f"Provided alignments do not cover all {end_pos - start_pos} tokens from the original"
-                " sequence.\nFilling missing position with right-aligned 1:1 position alignments.\n"
-                f"Generated alignments: {alignments}"
+                f"{existing_aligns_message if filter_aligns else no_aligns_message}\n"
+                "Filling missing position with right-aligned 1:1 position alignments."
             )
-    if is_auto_aligned:
-        logger.warning(
-            f"Using {ALIGN_MODEL_ID} for automatic alignments. Provide custom alignments for non-linguistic "
-            f"sequences, or for languages not covered by the aligner.\nGenerated alignments: {alignments}"
-        )
-    return alignments
+            filter_aligns = sorted(set(filled_alignments), key=lambda x: (x[0], x[1]))
+    if is_auto_aligned or (fill_missing and filter_aligns != filled_alignments):
+        logger.warning(f"Generated alignments: {filter_aligns}")
+    return filter_aligns
 
 
 def get_aligned_idx(a_idx: int, alignments: List[Tuple[int, int]]) -> int:
diff --git a/inseq/utils/contrast_utils.py b/inseq/utils/contrast_utils.py
index ca342181..c67a07ce 100644
--- a/inseq/utils/contrast_utils.py
+++ b/inseq/utils/contrast_utils.py
@@ -3,7 +3,7 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
 
-from transformers.modeling_outputs import ModelOutput
+import torch
 
 from ..data import (
     DecoderOnlyBatch,
@@ -52,13 +52,12 @@ def docstring_decorator(fn: "StepFunction") -> "StepFunction":
 
 
 @dataclass
-class ContrastOutput:
-    forward_output: ModelOutput
+class ContrastInputs:
     batch: Union[EncoderDecoderBatch, DecoderOnlyBatch, None] = None
     target_ids: Optional[TargetIdsTensor] = None
 
 
-def _get_contrast_output(
+def _get_contrast_inputs(
     args: "StepFunctionArgs",
     contrast_sources: Optional[FeatureAttributionInput] = None,
     contrast_targets: Optional[FeatureAttributionInput] = None,
@@ -66,7 +65,7 @@ def _get_contrast_output(
     return_contrastive_target_ids: bool = False,
     return_contrastive_batch: bool = False,
     **forward_kwargs,
-) -> ContrastOutput:
+) -> ContrastInputs:
     """Utility function to return the output of the model for given contrastive inputs.
 
     Args:
@@ -93,10 +92,13 @@ def _get_contrast_output(
                 f" ({args.decoder_input_ids.size(0)}). Multi-sentence attribution and methods expanding inputs to"
                 " multiple steps (e.g. Integrated Gradients) are not yet supported for contrastive attribution."
             )
-
-        args.decoder_input_ids = c_batch.target_ids
-        args.decoder_input_embeds = c_batch.target_embeds
-        args.decoder_attention_mask = c_batch.target_mask
+        if (
+            args.decoder_input_ids.shape != c_batch.target_ids.shape
+            or torch.ne(args.decoder_input_ids, c_batch.target_ids).any()
+        ):
+            args.decoder_input_ids = c_batch.target_ids
+            args.decoder_input_embeds = c_batch.target_embeds
+            args.decoder_attention_mask = c_batch.target_mask
     if contrast_sources:
         from ..attr.step_functions import StepFunctionEncoderDecoderArgs
 
@@ -106,12 +108,15 @@ def _get_contrast_output(
                 "Use `contrast_targets` to set a contrastive target containing a prefix for decoder-only models."
             )
         c_enc_in = args.attribution_model.encode(contrast_sources)
-        args.encoder_input_ids = c_enc_in.input_ids
-        args.encoder_attention_mask = c_enc_in.attention_mask
-        args.encoder_input_embeds = args.attribution_model.embed(args.encoder_input_ids, as_targets=False)
+        if (
+            args.encoder_input_ids.shape != c_enc_in.input_ids.shape
+            or torch.ne(args.encoder_input_ids, c_enc_in.input_ids).any()
+        ):
+            args.encoder_input_ids = c_enc_in.input_ids
+            args.encoder_input_embeds = args.attribution_model.embed(args.encoder_input_ids, as_targets=False)
+            args.encoder_attention_mask = c_enc_in.attention_mask
     c_batch = args.attribution_model.formatter.convert_args_to_batch(args)
-    return ContrastOutput(
-        forward_output=args.attribution_model.get_forward_output(c_batch, use_embeddings=is_enc_dec, **forward_kwargs),
+    return ContrastInputs(
         batch=c_batch if return_contrastive_batch else None,
         target_ids=c_tgt_ids if return_contrastive_target_ids else None,
     )
@@ -124,7 +129,7 @@ def _setup_contrast_args(
     contrast_targets_alignments: Optional[List[List[Tuple[int, int]]]] = None,
     contrast_force_inputs: bool = False,
 ):
-    c_output = _get_contrast_output(
+    c_inputs = _get_contrast_inputs(
         args,
         contrast_sources=contrast_sources,
         contrast_targets=contrast_targets,
@@ -132,8 +137,6 @@ def _setup_contrast_args(
         return_contrastive_target_ids=True,
         return_contrastive_batch=True,
     )
-    if c_output.target_ids is not None:
-        args.target_ids = c_output.target_ids
     if args.is_attributed_fn:
         if contrast_force_inputs:
             warnings.warn(
@@ -141,7 +144,6 @@ def _setup_contrast_args(
                 "gradient-based attribution methods.",
                 stacklevel=1,
             )
-            args.forward_output = c_output.forward_output
         else:
             warnings.warn(
                 "Contrastive inputs do not match original inputs when using a contrastive attributed function.\n"
@@ -151,6 +153,18 @@ def _setup_contrast_args(
                 "set --contrast_force_inputs to True to use the contrastive inputs for attribution instead.",
                 stacklevel=1,
             )
+    use_original_output = args.is_attributed_fn and not contrast_force_inputs
+    if use_original_output:
+        forward_output = args.forward_output
     else:
-        args.forward_output = c_output.forward_output
-    return args
+        forward_output = args.attribution_model.get_forward_output(
+            c_inputs.batch, use_embeddings=args.attribution_model.is_encoder_decoder
+        )
+    c_args = args.attribution_model.formatter.format_step_function_args(
+        args.attribution_model,
+        forward_output=forward_output,
+        target_ids=c_inputs.target_ids if c_inputs.target_ids is not None else args.target_ids,
+        batch=c_inputs.batch,
+        is_attributed_fn=args.is_attributed_fn,
+    )
+    return c_args


Test patch:
diff --git a/tests/attr/feat/test_step_functions.py b/tests/attr/feat/test_step_functions.py
index b4a8fcec..e9fe78be 100644
--- a/tests/attr/feat/test_step_functions.py
+++ b/tests/attr/feat/test_step_functions.py
@@ -2,7 +2,7 @@
 from pytest import fixture
 
 import inseq
-from inseq.attr.step_functions import StepFunctionArgs, _get_contrast_output, probability_fn
+from inseq.attr.step_functions import StepFunctionArgs, _get_contrast_inputs, probability_fn
 from inseq.models import DecoderOnlyAttributionModel, EncoderDecoderAttributionModel
 
 
@@ -62,7 +62,7 @@ def attr_prob_diff_fn(
     logprob: bool = False,
 ):
     model_probs = probability_fn(args, logprob=logprob)
-    c_out = _get_contrast_output(
+    c_out = _get_contrast_inputs(
         args,
         contrast_targets=contrast_targets,
         contrast_targets_alignments=contrast_targets_alignments,