Restore full project refactor with scripts and clean tracked artifacts

README.md

@@ -0,0 +1,34 @@
+# Credit Worthiness Risk Classification
+
+End-to-end classification case study for credit risk prediction, including preprocessing, model selection, and app delivery.
+
+## Objective
+
+Build and evaluate models to classify credit applicants by risk profile in a reproducible workflow.
+
+## What Is Included
+
+- data preparation and feature engineering steps;
+- supervised models: Logistic Regression, Decision Tree, Random Forest;
+- hyperparameter tuning with `GridSearchCV`;
+- model evaluation pipeline;
+- lightweight Gradio app for interactive inference (`app.py`).
+
+## Dataset
+
+- Local path: `data/raw/german_credit.csv`
+- Source: [Kaggle - German Credit Dataset](https://www.kaggle.com/datasets/mpwolke/cusersmarildownloadsgermancsv)
+
+## Run Locally
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Project Metadata
+
+- `sdk`: gradio
+- `sdk_version`: 3.27.0
+- `app_file`: `app.py`
+- `license`: Apache-2.0

app.py

@@ -0,0 +1,173 @@
+## IMPORTING LIBS
+
+import pandas as pd
+import plotly.express as px
+
+from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
+
+import pickle
+import gradio as gr
+
+## CREATING FUNCTION
+
+def predict_credit_worthiness(name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22):
+    path =  'german_credit_risk/model/model.pickle'
+    greet = 'Hey, ' + name + '!'
+    with open(path, 'rb') as file:
+        model = pickle.load(file)
+        inputs = {'Account Balance_1': int(x1),
+                  'Account Balance_2': int(x2),
+                  'Account Balance_3': int(x3),
+                  'Payment Status of Previous Credit_1': int(x4),
+                  'Payment Status of Previous Credit_3': int(x5),
+                  'Purpose_1': int(x6),
+                  'Purpose_4': int(x7),
+                  'Value Savings/Stocks_1': int(x8),
+                  'Value Savings/Stocks_3': int(x9),
+                  'Value Savings/Stocks_5': int(x10),
+                  'Length of current employment_1': int(x11),
+                  'Length of current employment_4': int(x12),
+                  'Instalment per cent_4': int(x13),
+                  'Guarantors_1': int(x14),
+                  'Duration in Current address_1': int(x15),
+                  'Duration in Current address_2': int(x16),
+                  'Most valuable available asset_1': int(x17),
+                  'Most valuable available asset_4': int(x18),
+                  'Concurrent Credits_3': int(x19),
+                  'Type of apartment_1': int(x20),
+                  'No of Credits at this Bank_1': int(x21),
+                  'Occupation_1': int(x22)
+                  }
+        prediction = model.predict([list(inputs.values())])
+        
+    y_test = pd.read_parquet('german_credit_risk/data/processed/y_test.parquet')
+    y_test = y_test.squeeze()
+
+    yhat = pd.read_parquet('german_credit_risk/data/processed/yhat.parquet')
+    yhat = yhat.squeeze()
+    
+    precision = precision_score(y_test, yhat).round(2)
+    recall = recall_score(y_test, yhat).round(2)
+    f1 = f1_score(y_test, yhat).round(2)
+
+    features_names =  ['No account', 'No balance', 'Some balance', 'No credit problems', 
+                       'Some credit problems', 'New car', 'Other purpose', 'No savings', 
+                       'DM betwenn [100, 1000]', 'DM >= 1000', 'Employment: <1 year (or unemployed)', 'Employment: 4<x<7 years', 
+                       'Installment smaller than 20%', 'No guarantors', 'Less than a year in same address', '1<x<4 years in address', 
+                       'Not available / no assets', 'Ownership of house or land',  'No further running credits', 'Free ap', 
+                       'One credit at thins bank','Unemployed or unskilled']
+    importance = model.feature_importances_
+    data = pd.DataFrame()
+    data['Feature Importance'] = importance
+    data['Feature'] = features_names
+    p = px.bar(data, y='Feature Importance', x='Feature', width=1200, height=500)
+    
+    cfm = confusion_matrix(y_test, yhat)
+    cfm_plot = px.imshow(cfm,
+                x=['Predicted 0', 'Predicted 1'],
+                y=['Actual 0', 'Actual 1'],
+                color_continuous_scale='Blues',
+                labels=dict(x="Predicted", y="Actual", color="Count"),
+                text_auto=True)    
+    
+    if prediction == 1:
+        return (greet + ' According to our model, your client is eligible for the loan.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    else:
+        return (greet + ' Unfortunately, according to our model, your client is not eligible for the loan for now :(.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    
+## creating the interface
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Credit Worthiness Prediction')
+    gr.Markdown("""
+                To predict our clients' creditworthiness, please use this application as follows:
+                
+                1. Enter your name and navigate through the client's information tabs. Select the boxes that best match your client's characteristics. Leave blank if none apply.
+
+                2. Once completed, click 'Predict' to determine if the client is creditworthy.
+                """)
+    with gr.Accordion('Name'):
+        name = gr.Textbox(lines=1, label='Your name')
+    with gr.Accordion("Enter your client's information"):
+        with gr.Tab('Account Balance'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x1 = gr.Checkbox(1, label='No account')
+            x2 = gr.Checkbox(0, label='No balance')
+            x3 = gr.Checkbox(0, label='Some balance')
+        with gr.Tab('Payment status of previous credit'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x4 = gr.Checkbox(1, label='Some problems')
+            x5 = gr.Checkbox(0, label='No problems in this bank')
+        with gr.Tab('Purpose'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x6 = gr.Checkbox(1, label='New car')
+            x7 = gr.Checkbox(0, label='Other')
+        with gr.Tab('Value savings/stocks'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x8 = gr.Checkbox(1, label='No savings')
+            x9 = gr.Checkbox(0, label='DM betwenn [100, 1000]')
+            x10 = gr.Checkbox(0, label='DM >= 1000')
+        with gr.Tab('Length of current employment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x11 = gr.Checkbox(1, label='Below 1 year (or unemployed)')
+            x12 = gr.Checkbox(0, label='Between 4 and 7 years')
+        with gr.Tab('Instalment per cent'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x13 = gr.Checkbox(0, label='Smaller than 20%')
+        with gr.Tab('Guarantors'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x14 = gr.Checkbox(0, label='No guarantors')
+        with gr.Tab('Duration in current address'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x15 = gr.Checkbox(1, label='Less than a year')
+            x16 = gr.Checkbox(0, label='Between 1 and 4 years')
+        with gr.Tab('Most valuable available asset'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x17 = gr.Checkbox(1, label='Not available / no assets')
+            x18 = gr.Checkbox(0, label='Ownership of house or land')
+        with gr.Tab('Concurrent credits'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x19 = gr.Checkbox(0, label='No further running credits')
+        with gr.Tab('Type of apartment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x20 = gr.Checkbox(0, label='Free apartment')
+        with gr.Tab('Number of credits at this Bank'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x21 = gr.Checkbox(0, label='One credit')        
+        with gr.Tab('Occupation'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x22 = gr.Checkbox(0, label='Unemployed or unskilled with no permanent') 
+    predict_button = gr.Button('Predict')
+    prediction_output = gr.Label(num_top_classes=2)
+    with gr.Accordion('Metrics and plots'):
+        with gr.Tab('Metrics'):
+            with gr.Row():
+                precision_output = gr.Label()
+            with gr.Row():
+                recall_output = gr.Label()
+            with gr.Row():
+                f1_output = gr.Label()
+        with gr.Tab('Feature Importances'):
+            fimp_output = gr.Plot()
+        with gr.Tab('Confusion Matrix'):
+            cfm_output = gr.Plot()
+    predict_button.click(fn=predict_credit_worthiness,
+                         inputs=[name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22],
+                         outputs=[prediction_output,precision_output, recall_output, f1_output, fimp_output, cfm_output])
+    gr.Markdown('''
+                Want to work in a project together or have interest in my services? Reach me:
+                [Linkedin](https://www.linkedin.com/in/marcilioduarte98/)
+                [Github](https://github.com/marcilioduarte)
+                @marcilioduarte | Economics and Data Science
+                ''')
+demo.launch()

code/processed/gcrisk.py

@@ -0,0 +1,173 @@
+## IMPORTING LIBS
+
+import pandas as pd
+import plotly.express as px
+
+from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
+
+import pickle
+import gradio as gr
+
+## CREATING FUNCTION
+
+def predict_credit_worthiness(name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22):
+    path =  'german_credit_risk/model/model.pickle'
+    greet = 'Hey, ' + name + '!'
+    with open(path, 'rb') as file:
+        model = pickle.load(file)
+        inputs = {'Account Balance_1': int(x1),
+                  'Account Balance_2': int(x2),
+                  'Account Balance_3': int(x3),
+                  'Payment Status of Previous Credit_1': int(x4),
+                  'Payment Status of Previous Credit_3': int(x5),
+                  'Purpose_1': int(x6),
+                  'Purpose_4': int(x7),
+                  'Value Savings/Stocks_1': int(x8),
+                  'Value Savings/Stocks_3': int(x9),
+                  'Value Savings/Stocks_5': int(x10),
+                  'Length of current employment_1': int(x11),
+                  'Length of current employment_4': int(x12),
+                  'Instalment per cent_4': int(x13),
+                  'Guarantors_1': int(x14),
+                  'Duration in Current address_1': int(x15),
+                  'Duration in Current address_2': int(x16),
+                  'Most valuable available asset_1': int(x17),
+                  'Most valuable available asset_4': int(x18),
+                  'Concurrent Credits_3': int(x19),
+                  'Type of apartment_1': int(x20),
+                  'No of Credits at this Bank_1': int(x21),
+                  'Occupation_1': int(x22)
+                  }
+        prediction = model.predict([list(inputs.values())])
+        
+    y_test = pd.read_parquet('german_credit_risk/data/processed/y_test.parquet')
+    y_test = y_test.squeeze()
+
+    yhat = pd.read_parquet('german_credit_risk/data/processed/yhat.parquet')
+    yhat = yhat.squeeze()
+    
+    precision = precision_score(y_test, yhat).round(2)
+    recall = recall_score(y_test, yhat).round(2)
+    f1 = f1_score(y_test, yhat).round(2)
+
+    features_names =  ['No account', 'No balance', 'Some balance', 'No credit problems', 
+                       'Some credit problems', 'New car', 'Other purpose', 'No savings', 
+                       'DM betwenn [100, 1000]', 'DM >= 1000', 'Employment: <1 year (or unemployed)', 'Employment: 4<x<7 years', 
+                       'Installment smaller than 20%', 'No guarantors', 'Less than a year in same address', '1<x<4 years in address', 
+                       'Not available / no assets', 'Ownership of house or land',  'No further running credits', 'Free ap', 
+                       'One credit at thins bank','Unemployed or unskilled']
+    importance = model.feature_importances_
+    data = pd.DataFrame()
+    data['Feature Importance'] = importance
+    data['Feature'] = features_names
+    p = px.bar(data, y='Feature Importance', x='Feature', width=1200, height=500)
+    
+    cfm = confusion_matrix(y_test, yhat)
+    cfm_plot = px.imshow(cfm,
+                x=['Predicted 0', 'Predicted 1'],
+                y=['Actual 0', 'Actual 1'],
+                color_continuous_scale='Blues',
+                labels=dict(x="Predicted", y="Actual", color="Count"),
+                text_auto=True)    
+    
+    if prediction == 1:
+        return (greet + ' According to our model, your client is eligible for the loan.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    else:
+        return (greet + ' Unfortunately, according to our model, your client is not eligible for the loan for now :(.', 
+                'Precision: '+ str(precision), 
+                'Recall: '+ str(recall), 
+                'F1 Score: '+ str(f1), 
+                p, 
+                cfm_plot)
+    
+## creating the interface
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Credit Worthiness Prediction')
+    gr.Markdown("""
+                To predict our clients' creditworthiness, please use this application as follows:
+                
+                1. Enter your name and navigate through the client's information tabs. Select the boxes that best match your client's characteristics. Leave blank if none apply.
+
+                2. Once completed, click 'Predict' to determine if the client is creditworthy.
+                """)
+    with gr.Accordion('Name'):
+        name = gr.Textbox(lines=1, label='Your name')
+    with gr.Accordion("Enter your client's information"):
+        with gr.Tab('Account Balance'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x1 = gr.Checkbox(1, label='No account')
+            x2 = gr.Checkbox(0, label='No balance')
+            x3 = gr.Checkbox(0, label='Some balance')
+        with gr.Tab('Payment status of previous credit'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x4 = gr.Checkbox(1, label='Some problems')
+            x5 = gr.Checkbox(0, label='No problems in this bank')
+        with gr.Tab('Purpose'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x6 = gr.Checkbox(1, label='New car')
+            x7 = gr.Checkbox(0, label='Other')
+        with gr.Tab('Value savings/stocks'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x8 = gr.Checkbox(1, label='No savings')
+            x9 = gr.Checkbox(0, label='DM betwenn [100, 1000]')
+            x10 = gr.Checkbox(0, label='DM >= 1000')
+        with gr.Tab('Length of current employment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x11 = gr.Checkbox(1, label='Below 1 year (or unemployed)')
+            x12 = gr.Checkbox(0, label='Between 4 and 7 years')
+        with gr.Tab('Instalment per cent'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x13 = gr.Checkbox(0, label='Smaller than 20%')
+        with gr.Tab('Guarantors'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x14 = gr.Checkbox(0, label='No guarantors')
+        with gr.Tab('Duration in current address'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x15 = gr.Checkbox(1, label='Less than a year')
+            x16 = gr.Checkbox(0, label='Between 1 and 4 years')
+        with gr.Tab('Most valuable available asset'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x17 = gr.Checkbox(1, label='Not available / no assets')
+            x18 = gr.Checkbox(0, label='Ownership of house or land')
+        with gr.Tab('Concurrent credits'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x19 = gr.Checkbox(0, label='No further running credits')
+        with gr.Tab('Type of apartment'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x20 = gr.Checkbox(0, label='Free apartment')
+        with gr.Tab('Number of credits at this Bank'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x21 = gr.Checkbox(0, label='One credit')        
+        with gr.Tab('Occupation'):
+            gr.Markdown('Select only one option. Leave all boxes blank if none of the options fits the client.')
+            x22 = gr.Checkbox(0, label='Unemployed or unskilled with no permanent') 
+    predict_button = gr.Button('Predict')
+    prediction_output = gr.Label(num_top_classes=2)
+    with gr.Accordion('Metrics and plots'):
+        with gr.Tab('Metrics'):
+            with gr.Row():
+                precision_output = gr.Label()
+            with gr.Row():
+                recall_output = gr.Label()
+            with gr.Row():
+                f1_output = gr.Label()
+        with gr.Tab('Feature Importances'):
+            fimp_output = gr.Plot()
+        with gr.Tab('Confusion Matrix'):
+            cfm_output = gr.Plot()
+    predict_button.click(fn=predict_credit_worthiness,
+                         inputs=[name, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22],
+                         outputs=[prediction_output,precision_output, recall_output, f1_output, fimp_output, cfm_output])
+    gr.Markdown('''
+                Want to work in a project together or have interest in my services? Reach me:
+                [Linkedin](https://www.linkedin.com/in/marcilioduarte98/)
+                [Github](https://github.com/marcilioduarte)
+                @marcilioduarte | Economics and Data Science
+                ''')
+demo.launch()

data/raw/german_credit.csv

@@ -0,0 +1,1001 @@
+Creditability,Account Balance,Duration of Credit (month),Payment Status of Previous Credit,Purpose,Credit Amount,Value Savings/Stocks,Length of current employment,Instalment per cent,Sex & Marital Status,Guarantors,Duration in Current address,Most valuable available asset,Age (years),Concurrent Credits,Type of apartment,No of Credits at this Bank,Occupation,No of dependents,Telephone,Foreign Worker
+1,1,18,4,2,1049,1,2,4,2,1,4,2,21,3,1,1,3,1,1,1
+1,1,9,4,0,2799,1,3,2,3,1,2,1,36,3,1,2,3,2,1,1
+1,2,12,2,9,841,2,4,2,2,1,4,1,23,3,1,1,2,1,1,1
+1,1,12,4,0,2122,1,3,3,3,1,2,1,39,3,1,2,2,2,1,2
+1,1,12,4,0,2171,1,3,4,3,1,4,2,38,1,2,2,2,1,1,2
+1,1,10,4,0,2241,1,2,1,3,1,3,1,48,3,1,2,2,2,1,2
+1,1,8,4,0,3398,1,4,1,3,1,4,1,39,3,2,2,2,1,1,2
+1,1,6,4,0,1361,1,2,2,3,1,4,1,40,3,2,1,2,2,1,2
+1,4,18,4,3,1098,1,1,4,2,1,4,3,65,3,2,2,1,1,1,1
+1,2,24,2,3,3758,3,1,1,2,1,4,4,23,3,1,1,1,1,1,1
+1,1,11,4,0,3905,1,3,2,3,1,2,1,36,3,1,2,3,2,1,1
+1,1,30,4,1,6187,2,4,1,4,1,4,3,24,3,1,2,3,1,1,1
+1,1,6,4,3,1957,1,4,1,2,1,4,3,31,3,2,1,3,1,1,1
+1,2,48,3,10,7582,2,1,2,3,1,4,4,31,3,2,1,4,1,2,1
+1,1,18,2,3,1936,5,4,2,4,1,4,3,23,3,1,2,2,1,1,1
+1,1,6,2,3,2647,3,3,2,3,1,3,1,44,3,1,1,3,2,1,1
+1,1,11,4,0,3939,1,3,1,3,1,2,1,40,3,2,2,2,2,1,1
+1,2,18,2,3,3213,3,2,1,4,1,3,1,25,3,1,1,3,1,1,1
+1,2,36,4,3,2337,1,5,4,3,1,4,1,36,3,2,1,3,1,1,1
+1,4,11,4,0,7228,1,3,1,3,1,4,2,39,3,2,2,2,1,1,1
+1,1,6,4,0,3676,1,3,1,3,1,3,1,37,3,1,3,3,2,1,1
+1,2,12,4,0,3124,1,2,1,3,1,3,1,49,1,2,2,2,2,1,1
+0,2,36,2,5,2384,1,2,4,3,1,1,4,33,3,1,1,2,1,1,1
+1,2,12,4,4,1424,1,4,4,3,1,3,2,26,3,2,1,3,1,1,1
+1,1,6,4,0,4716,5,2,1,3,1,3,1,44,3,2,2,2,2,1,1
+1,2,11,3,3,4771,1,4,2,3,1,4,2,51,3,2,1,3,1,1,1
+1,1,12,2,2,652,1,5,4,2,1,4,2,24,3,1,1,3,1,1,1
+1,2,9,4,3,1154,1,5,2,3,1,4,1,37,3,2,3,2,1,1,1
+1,4,15,2,0,3556,5,3,3,3,1,2,4,29,3,2,1,3,1,1,1
+1,3,42,4,1,4796,1,5,4,3,1,4,4,56,3,3,1,3,1,1,1
+1,3,30,4,3,3017,1,5,4,3,1,4,2,47,3,2,1,3,1,1,1
+1,4,36,4,0,3535,1,4,4,3,1,4,3,37,3,2,2,3,1,2,1
+1,4,36,4,0,6614,1,5,4,3,1,4,3,34,3,2,2,4,1,2,1
+1,4,24,2,3,1376,3,4,4,2,1,1,3,28,3,2,1,3,1,1,1
+1,1,15,2,0,1721,1,2,2,3,1,3,1,36,3,2,1,3,1,1,1
+1,1,6,4,0,860,1,5,1,2,1,4,4,39,3,2,2,3,1,2,1
+1,4,12,4,0,1495,1,5,4,3,1,1,1,38,3,2,2,2,2,1,1
+1,4,12,4,3,1934,1,5,2,3,1,2,4,26,3,2,2,3,1,1,1
+1,4,18,2,1,3378,5,3,2,3,1,1,2,31,3,2,1,3,1,2,1
+1,4,24,4,1,3868,1,5,4,2,1,2,3,41,3,1,2,4,1,2,1
+1,4,12,4,5,996,5,4,4,2,1,4,1,23,3,2,2,3,1,1,1
+1,1,24,2,10,1755,1,5,4,2,3,4,1,58,3,2,1,2,1,2,1
+1,4,18,4,0,1028,1,3,4,2,1,3,1,36,3,2,2,3,1,1,1
+1,2,24,4,9,2825,5,4,4,3,1,3,4,34,3,2,2,3,2,2,1
+1,2,18,2,6,1239,5,3,4,3,1,4,4,61,3,3,1,3,1,1,1
+0,1,18,2,0,1216,1,2,4,2,1,3,3,23,3,1,1,3,1,2,1
+1,4,24,2,9,1258,1,4,4,3,1,1,1,25,3,2,1,3,1,2,1
+0,4,18,4,6,1864,2,3,4,2,1,2,1,30,3,2,2,3,1,1,1
+1,4,24,2,0,1474,2,2,4,4,1,3,1,33,3,2,1,3,1,2,1
+1,1,24,4,9,1382,2,4,4,3,1,1,1,26,3,2,2,3,1,2,1
+1,4,12,2,0,640,1,3,4,1,1,2,1,49,3,2,1,2,1,1,1
+1,3,36,2,3,3919,1,3,2,3,1,2,1,23,3,2,1,3,1,2,1
+1,4,9,4,0,1224,1,3,3,3,1,1,1,30,3,2,2,3,1,1,1
+1,4,12,4,3,2331,5,5,1,3,2,4,1,49,3,2,1,3,1,2,1
+1,4,24,2,1,6313,5,5,3,3,1,4,3,41,3,2,1,4,2,2,1
+1,1,12,4,3,385,1,4,4,2,1,3,1,58,3,2,4,2,1,2,1
+1,4,12,4,3,1655,1,5,2,3,1,4,1,63,3,2,2,2,1,2,1
+1,1,15,2,3,1053,1,2,4,4,1,2,1,27,3,2,1,3,1,1,2
+1,4,21,2,3,3160,5,5,4,3,1,3,2,41,3,2,1,3,1,2,1
+1,4,36,2,0,3079,5,3,4,3,1,4,1,36,3,2,1,3,1,1,1
+1,4,12,4,0,1163,3,3,4,3,1,4,1,44,3,2,1,3,1,2,1
+1,4,24,2,1,2679,1,2,4,2,1,1,4,29,3,2,1,4,1,2,1
+1,4,48,4,3,3578,5,5,4,3,1,1,1,47,3,2,1,3,1,2,1
+1,4,36,3,0,10875,1,5,2,3,1,2,3,45,3,2,2,3,2,2,1
+1,1,12,3,0,1344,1,3,4,3,1,2,1,43,3,2,2,2,2,1,1
+1,4,6,4,3,1237,2,3,1,2,1,1,2,27,3,2,2,3,1,1,1
+1,4,12,2,3,3077,1,3,2,3,1,4,3,52,3,2,1,3,1,2,1
+1,4,24,2,3,2284,1,4,4,3,1,2,3,28,3,2,1,3,1,2,1
+1,2,12,2,3,1567,1,3,1,2,1,1,3,22,3,2,1,3,1,2,1
+1,4,24,3,0,2032,1,5,4,3,1,4,4,60,3,3,2,3,1,2,1
+1,2,21,4,2,2745,4,4,3,3,1,2,3,32,3,2,2,3,1,2,1
+1,4,30,2,3,1867,5,5,4,3,1,4,3,58,3,2,1,3,1,2,1
+1,4,36,2,3,2299,3,5,4,3,1,4,3,39,3,2,1,3,1,1,1
+1,4,24,2,2,929,5,4,4,3,1,2,3,31,2,2,1,3,1,2,1
+1,3,12,2,3,3399,5,5,2,3,1,3,3,37,3,2,1,4,1,1,1
+1,2,9,2,2,2030,5,4,2,3,1,1,3,24,3,2,1,3,1,2,1
+1,4,21,4,1,3275,1,5,1,3,1,4,3,36,3,2,1,4,1,2,1
+1,4,24,4,0,1940,4,5,4,3,1,4,1,60,3,2,1,3,1,2,1
+1,1,21,4,0,1602,1,5,4,4,1,3,3,30,3,2,2,3,1,2,1
+1,4,15,2,3,1979,5,5,4,3,1,2,3,35,3,2,1,3,1,1,1
+1,4,24,4,0,2022,1,3,4,2,1,4,3,37,3,2,1,3,1,2,1
+1,4,36,4,3,3342,5,5,4,3,1,2,3,51,3,2,1,3,1,2,1
+1,2,18,2,0,5866,2,3,2,3,1,2,3,30,3,2,2,3,1,2,1
+1,3,15,4,1,2360,3,3,2,3,1,2,3,36,3,2,1,3,1,2,1
+1,4,15,4,2,1520,5,5,4,3,1,4,2,63,3,2,1,3,1,1,1
+1,1,12,2,0,3651,4,3,1,3,1,3,2,31,3,2,1,3,2,1,1
+1,4,24,4,1,2346,1,4,4,3,1,3,3,35,3,2,2,3,1,2,1
+1,4,36,3,3,4454,1,3,4,2,1,4,1,34,3,2,2,3,1,1,1
+1,1,6,4,0,666,4,4,3,2,1,4,1,39,3,2,2,2,1,2,1
+1,2,24,3,0,1965,5,3,4,2,1,4,3,42,3,1,2,3,1,2,1
+1,2,12,4,0,1995,2,2,4,3,1,1,3,27,3,2,1,3,1,1,1
+1,2,30,2,3,2991,5,5,2,2,1,4,3,25,3,2,1,3,1,1,1
+1,2,30,0,9,4221,1,3,2,2,1,1,3,28,3,2,2,3,1,1,1
+1,1,9,2,3,1364,1,4,3,3,1,4,1,59,3,2,1,3,1,1,1
+1,2,18,4,2,6361,1,5,2,3,1,1,4,41,3,2,1,3,1,2,1
+1,4,27,4,2,4526,4,2,4,3,1,2,1,32,2,2,2,2,2,2,1
+1,2,12,4,3,3573,1,3,1,2,1,1,1,23,3,2,1,2,1,1,1
+0,2,36,3,9,4455,1,3,2,1,1,2,1,30,2,2,2,4,1,2,1
+1,1,9,2,2,2136,1,3,3,3,1,2,1,25,3,2,1,3,1,1,1
+1,2,42,4,9,5954,1,4,2,2,1,1,1,41,1,2,2,2,1,1,1
+1,4,24,4,2,3777,4,3,4,3,1,4,1,40,3,2,1,3,1,2,1
+1,1,15,2,9,806,1,3,4,2,1,4,2,22,3,2,1,2,1,1,1
+1,2,24,3,9,4712,5,3,4,3,1,2,2,34,1,2,2,4,1,2,1
+1,2,36,3,0,7432,1,3,2,2,1,2,2,54,3,1,1,3,1,1,1
+1,4,24,4,3,1851,1,4,4,4,3,2,3,33,3,2,2,3,1,2,1
+1,4,24,2,0,1393,1,3,2,3,3,2,1,31,3,2,1,3,1,2,1
+1,4,12,4,9,1412,1,3,4,2,3,2,1,29,3,2,2,4,1,2,1
+1,4,18,2,3,1473,1,2,3,4,1,4,1,39,3,2,1,3,1,2,1
+1,4,24,2,3,1533,1,2,4,2,1,3,3,38,2,2,1,3,1,2,1
+1,4,12,4,6,2012,5,4,4,2,1,2,3,61,3,2,1,3,1,1,1
+0,1,15,2,0,3959,1,3,3,2,1,2,2,29,3,2,1,3,1,2,1
+1,1,6,2,2,428,1,5,2,2,1,1,2,49,1,2,1,3,1,2,1
+1,2,12,4,0,2366,3,4,3,1,1,3,3,36,3,2,1,4,1,2,1
+1,4,12,2,2,763,1,3,4,2,1,1,1,26,3,2,1,3,1,2,1
+1,2,21,2,2,3976,5,4,2,3,1,3,3,35,3,2,1,3,1,2,1
+1,2,18,2,0,6260,1,4,3,3,1,3,1,28,3,1,1,2,1,1,1
+1,2,9,4,2,1919,1,4,4,3,1,3,3,35,3,1,1,3,1,2,1
+1,4,24,2,1,2603,4,3,2,2,1,4,3,28,3,1,1,3,1,2,1
+1,4,9,4,6,936,3,5,4,3,1,2,3,52,3,2,2,3,1,2,1
+1,4,24,2,2,3062,3,5,4,3,1,3,4,32,3,1,1,3,1,2,1
+1,2,36,2,3,4795,1,2,4,2,1,1,4,30,3,2,1,4,1,2,1
+1,4,36,4,1,5842,1,5,2,3,1,2,2,35,3,2,2,3,2,2,1
+1,2,6,2,3,2063,1,2,4,4,1,3,3,30,3,1,1,4,1,2,1
+1,4,15,4,3,1459,1,3,4,2,1,2,3,43,3,2,1,2,1,1,1
+1,4,15,2,3,1213,3,5,4,3,1,3,2,47,2,2,1,3,1,2,1
+1,4,24,4,3,5103,1,2,3,4,1,3,4,47,3,3,3,3,1,2,1
+1,4,15,2,4,874,5,2,4,2,1,1,1,24,3,2,1,3,1,1,1
+1,4,6,2,2,2978,3,3,1,3,1,2,3,32,3,2,1,3,1,2,1
+1,4,18,2,0,1820,1,3,2,4,1,2,2,30,3,2,1,4,1,2,1
+1,4,24,4,3,2872,2,5,3,3,1,4,1,36,3,2,1,3,2,2,1
+1,3,24,2,2,1925,1,3,2,3,1,2,1,26,3,2,1,3,1,1,1
+1,4,18,2,2,2515,1,3,3,3,1,4,1,43,3,2,1,3,1,2,1
+1,3,6,2,2,2116,1,3,2,3,1,2,1,41,3,2,1,3,1,2,1
+1,4,18,2,3,1453,1,2,3,2,1,1,1,26,3,2,1,3,1,1,1
+1,4,10,2,0,1364,1,3,2,2,1,4,3,64,3,2,1,3,1,2,1
+1,4,6,2,2,1543,4,3,4,1,1,2,1,33,3,2,1,3,1,1,1
+1,2,12,2,0,1318,4,5,4,3,1,4,1,54,3,2,1,3,1,2,1
+1,1,24,1,0,2325,2,4,2,3,1,3,3,32,1,2,1,3,1,1,1
+1,2,6,4,8,932,5,4,1,2,1,3,2,39,3,2,2,2,1,1,1
+1,3,24,4,3,3148,5,3,3,3,1,2,3,31,3,2,2,3,1,2,1
+1,4,36,2,3,3835,5,5,2,2,1,4,1,45,3,2,1,2,1,2,1
+1,4,9,2,6,3832,5,5,1,3,1,4,1,64,3,2,1,2,1,1,1
+1,2,24,2,3,5084,5,5,2,2,1,4,3,42,3,2,1,3,1,2,1
+1,4,9,4,2,2406,1,1,2,3,1,3,3,31,3,2,1,4,1,1,1
+1,4,36,2,3,2394,5,3,4,2,1,4,3,25,3,2,1,3,1,1,1
+1,4,21,2,1,2476,5,5,4,3,1,4,1,46,3,2,1,4,1,2,1
+1,1,24,2,1,2964,5,5,4,3,1,4,4,49,1,3,1,3,2,2,1
+1,1,12,2,2,1262,5,5,2,1,1,4,2,49,3,2,1,2,1,2,1
+1,4,12,2,9,1542,1,4,2,3,1,4,3,36,3,2,1,3,1,2,1
+1,2,24,4,3,1743,1,5,4,3,1,2,2,48,3,2,2,2,1,1,1
+1,3,12,1,3,409,4,3,3,2,1,3,1,42,3,1,2,3,1,1,1
+1,4,12,2,3,2171,1,2,2,2,1,2,3,29,1,2,1,3,1,1,1
+1,4,48,4,1,8858,5,4,2,3,1,1,4,35,3,3,2,3,1,2,1
+1,2,24,2,0,3512,2,4,2,3,1,3,3,38,1,2,2,3,1,2,1
+1,2,12,2,3,1158,3,3,3,1,1,1,3,26,3,2,1,3,1,2,1
+1,4,24,4,3,2684,1,3,4,3,1,2,1,35,3,2,2,2,1,1,1
+1,1,12,2,3,1498,1,3,4,2,1,1,3,23,1,2,1,3,1,1,1
+1,4,30,4,3,5954,1,4,3,3,2,2,3,38,3,2,1,3,1,1,1
+0,2,48,1,9,6416,1,5,4,2,1,3,4,59,3,1,1,3,1,1,1
+1,2,12,4,2,3617,1,5,1,3,1,4,3,28,3,1,3,3,1,2,1
+1,4,12,4,3,1291,1,3,4,2,1,2,2,35,3,2,2,3,1,1,1
+1,3,24,4,9,1275,4,3,2,1,1,4,1,36,3,2,2,3,1,2,1
+1,4,24,2,2,3972,1,4,2,2,1,4,2,25,3,1,1,3,1,2,1
+1,4,15,4,2,3343,1,3,4,3,1,2,4,28,3,3,1,3,1,2,1
+1,3,15,2,6,392,1,2,4,2,1,4,2,23,3,1,1,3,1,2,1
+1,4,9,4,0,2134,1,3,4,3,1,4,3,48,3,2,3,3,1,2,1
+1,4,30,4,3,5771,1,4,4,2,1,2,3,25,3,2,2,3,1,1,1
+1,4,24,4,9,4526,1,3,3,3,1,2,1,74,3,2,1,4,1,2,1
+1,4,15,4,2,2788,1,4,2,2,2,3,3,24,1,2,2,3,1,1,1
+1,4,6,4,3,1382,1,3,1,2,1,1,3,28,3,2,2,3,1,2,1
+1,3,36,2,3,5848,1,3,4,3,1,1,3,24,3,2,1,3,1,1,1
+0,1,12,2,0,1228,1,3,4,2,1,2,1,24,3,2,1,2,1,1,1
+1,3,12,2,3,1297,1,3,3,4,1,4,1,23,3,1,1,3,1,1,1
+1,4,24,2,3,1552,1,4,3,3,1,1,3,32,1,2,1,3,2,1,1
+1,4,12,2,3,1963,1,4,4,3,1,2,3,31,3,1,2,4,2,2,1
+1,4,24,2,3,3235,3,5,3,1,1,2,3,36,3,2,1,4,1,2,1
+1,4,24,4,9,4139,2,3,3,3,1,3,2,27,3,2,2,2,1,2,1
+1,2,12,4,1,1804,2,2,3,3,1,4,2,44,3,2,1,3,1,1,1
+1,4,18,2,9,1950,1,4,4,3,1,1,3,34,2,2,2,3,1,2,1
+1,4,48,3,3,12749,3,4,4,3,1,1,3,37,3,2,1,4,1,2,1
+1,4,9,2,4,1236,1,2,1,2,1,4,1,23,3,1,1,3,1,2,1
+1,4,18,4,0,1055,1,2,4,2,1,1,2,30,3,2,2,3,1,1,1
+1,1,30,0,9,8072,5,2,2,3,1,3,3,25,1,2,3,3,1,1,1
+1,4,30,4,3,2831,1,3,4,2,1,2,3,33,3,2,1,3,1,2,1
+1,4,9,2,9,1449,1,4,3,2,1,2,3,27,3,2,2,3,1,1,1
+1,4,36,2,9,5742,2,4,2,3,1,2,3,31,3,2,2,3,1,2,1
+1,4,12,2,0,2390,5,5,4,3,1,3,3,50,3,2,1,3,1,2,1
+1,4,24,2,3,3430,3,5,3,3,1,2,3,31,3,2,1,3,2,2,1
+1,2,36,2,6,2273,1,4,3,3,1,1,3,32,3,2,2,3,2,1,1
+1,3,21,2,0,2923,2,3,1,2,1,1,3,28,1,2,1,4,1,2,1
+1,4,24,2,3,1901,2,3,4,3,1,4,3,29,3,1,1,4,1,2,1
+1,2,36,2,6,3711,5,3,2,4,1,2,3,27,3,2,1,3,1,1,1
+1,2,48,2,0,8487,5,4,1,2,1,2,3,24,3,2,1,3,1,1,1
+1,4,24,2,0,2255,5,2,4,3,1,1,2,54,3,2,1,3,1,1,1
+1,4,12,2,3,1262,1,3,3,3,1,2,3,25,3,2,1,3,1,1,1
+1,4,33,4,1,7253,1,4,3,3,1,2,3,35,3,2,2,4,1,2,1
+1,4,6,4,0,6761,1,4,1,3,1,3,4,45,3,2,2,4,2,2,1
+1,4,18,4,2,1817,1,3,4,2,1,2,4,28,3,2,2,3,1,1,1
+1,4,12,2,3,2141,2,4,3,3,1,1,4,35,3,2,1,3,1,1,1
+1,4,48,1,9,3609,1,3,1,2,1,1,1,27,2,2,1,3,1,1,1
+1,4,30,2,3,2333,3,5,4,3,1,2,3,30,1,2,1,4,1,1,1
+1,4,28,1,1,7824,5,2,3,3,3,4,1,40,1,1,2,3,2,2,1
+1,3,18,1,3,1445,5,4,4,3,1,4,3,49,1,2,1,2,1,1,1
+1,1,24,2,2,7721,5,2,1,2,1,2,2,30,3,2,1,3,1,2,2
+1,1,21,2,0,3763,5,4,2,3,2,2,1,24,3,2,1,2,1,1,2
+1,2,18,2,9,4439,1,5,1,3,2,1,1,33,1,2,1,4,1,2,1
+1,1,12,2,3,1107,1,3,2,3,1,2,1,20,3,1,1,4,2,2,1
+1,2,15,2,3,1444,5,2,4,3,1,1,2,23,3,2,1,3,1,1,1
+1,2,48,1,0,12169,5,1,4,3,2,4,4,36,3,3,1,4,1,2,1
+1,4,9,2,3,2753,2,5,3,3,2,4,3,35,3,2,1,3,1,2,1
+1,3,4,2,0,1494,5,2,1,3,1,2,1,29,3,2,1,2,2,1,2
+1,1,24,1,2,2828,3,3,4,3,1,4,1,22,2,2,1,3,1,2,1
+1,1,24,1,2,2483,3,3,4,3,1,4,1,22,2,2,1,3,1,2,1
+1,3,6,4,0,1299,1,3,1,3,1,1,1,74,3,2,3,1,2,1,2
+1,2,9,2,0,1549,5,2,4,3,1,2,1,35,3,2,1,1,1,1,1
+1,3,10,2,0,3949,1,2,1,3,3,1,2,37,3,2,1,2,2,1,1
+1,4,10,2,1,2901,5,2,1,2,1,4,1,31,3,1,1,3,1,1,1
+1,3,6,2,0,709,4,2,2,4,1,2,1,27,3,2,1,1,1,1,2
+1,1,47,2,0,10722,1,2,1,2,1,1,1,35,3,2,1,2,1,2,1
+1,4,10,2,0,1287,5,5,4,3,2,2,2,45,3,2,1,2,1,1,2
+1,1,18,1,3,1940,1,2,3,3,2,4,4,36,1,3,1,4,1,2,1
+1,3,30,4,3,3656,5,5,4,3,1,4,2,49,2,2,2,2,1,1,1
+1,4,24,3,1,4679,1,4,3,3,1,3,3,35,3,2,2,2,1,2,1
+1,4,27,3,1,8613,4,3,2,3,1,2,3,27,3,2,2,3,1,1,1
+1,1,18,2,2,2659,4,3,4,3,1,2,3,28,3,2,1,3,1,1,1
+1,4,24,4,3,1516,4,3,4,2,1,1,1,43,3,2,2,2,1,1,1
+1,1,18,2,0,4380,2,3,3,3,1,4,3,35,3,2,1,2,2,2,1
+1,4,14,3,0,802,1,3,4,3,1,2,3,27,3,2,2,2,1,1,1
+1,4,21,2,9,1572,4,5,4,2,1,4,1,36,1,2,1,2,1,1,1
+1,2,48,1,9,3566,2,4,4,3,1,2,3,30,3,2,1,3,1,1,1
+1,4,24,2,3,1278,1,5,4,3,1,1,1,36,3,2,1,4,1,2,1
+1,4,6,0,3,426,1,5,4,4,1,4,3,39,3,2,1,2,1,1,1
+1,4,39,2,1,8588,2,5,4,3,1,2,3,45,3,2,1,4,1,2,1
+1,1,30,2,1,3857,1,3,4,1,1,4,2,40,3,2,1,4,1,2,1
+0,2,12,2,0,685,1,4,2,4,1,3,3,25,1,2,1,2,1,1,1
+1,1,24,2,3,1603,1,5,4,2,1,4,3,55,3,2,1,3,1,1,1
+1,4,21,2,2,2241,1,5,4,3,1,2,1,50,3,2,2,3,1,1,1
+1,1,24,2,3,2384,1,5,4,3,1,4,1,64,1,1,1,2,1,1,1
+1,4,4,2,2,601,1,2,1,2,1,3,1,23,3,1,1,2,2,1,1
+1,4,39,2,1,2569,3,3,4,3,1,4,3,24,3,2,1,3,1,1,1
+1,4,15,4,3,1316,3,3,2,4,1,2,2,47,3,2,2,2,1,1,1
+1,4,60,2,0,10366,1,5,2,3,1,4,2,42,3,2,1,4,1,2,1
+1,4,18,2,9,1568,2,3,3,2,1,4,2,24,3,1,1,2,1,1,1
+1,4,18,4,3,629,3,5,4,3,1,3,2,32,1,2,2,4,1,2,1
+1,4,6,1,3,1750,3,5,2,3,1,4,2,45,1,2,1,2,2,1,1
+1,4,24,2,1,3488,2,4,3,2,1,4,3,23,3,2,1,3,1,1,1
+1,4,18,4,3,1800,1,3,4,3,1,2,3,24,3,2,2,3,1,1,1
+1,4,24,3,2,4151,2,3,2,3,1,3,2,35,3,2,2,3,1,1,1
+1,2,15,2,5,2631,2,3,3,2,1,2,1,25,3,2,1,2,1,1,1
+1,4,21,2,1,5248,5,3,1,3,1,3,3,26,3,2,1,3,1,1,1
+1,2,18,3,0,2899,5,5,4,3,1,4,3,43,3,2,1,3,2,1,1
+1,2,18,3,5,6204,1,3,2,3,1,4,1,44,3,2,1,2,2,2,1
+1,4,12,2,3,804,1,5,4,3,1,4,3,38,3,2,1,3,1,1,1
+1,4,36,2,3,3595,1,5,4,3,1,2,3,28,3,2,1,3,1,1,1
+1,4,36,4,1,5711,4,5,4,3,1,2,3,38,3,2,2,4,1,2,1
+1,3,15,2,9,2687,1,4,2,3,1,4,2,26,3,1,1,3,1,2,1
+1,1,15,3,2,3643,1,5,1,2,1,4,2,27,3,2,2,2,1,1,1
+1,4,10,4,2,2146,1,2,1,2,1,3,1,23,3,1,2,3,1,1,1
+1,1,10,2,3,2315,1,5,3,3,1,4,1,52,3,2,1,2,1,1,1
+1,4,5,2,9,3448,1,4,1,3,1,4,1,74,3,2,1,2,1,1,1
+1,4,15,2,2,2708,1,2,2,3,1,3,2,27,1,2,2,2,1,1,1
+1,4,11,4,0,1393,1,2,4,2,1,4,3,35,3,2,2,4,1,1,1
+1,3,10,2,2,1275,1,2,4,2,1,2,2,23,3,2,1,3,1,1,1
+1,4,9,2,2,1313,1,5,1,3,1,4,3,20,3,2,1,3,1,1,1
+1,4,12,2,3,1493,1,2,4,2,1,3,3,34,3,2,1,3,2,1,1
+1,4,22,2,3,2675,3,5,3,3,1,4,3,40,3,2,1,3,1,1,1
+1,2,9,2,3,2118,1,3,2,3,1,2,1,37,3,2,1,2,2,1,1
+1,4,36,2,0,909,3,5,4,3,1,4,2,36,3,2,1,3,1,1,1
+1,4,12,4,2,1258,1,2,2,2,1,4,2,22,3,1,2,2,1,1,1
+1,4,15,1,3,1569,2,5,4,3,1,4,3,34,1,2,1,2,2,1,1
+1,4,6,2,1,1236,3,3,2,3,1,4,2,50,3,1,1,3,1,1,1
+1,4,36,3,2,7678,3,4,2,2,1,4,3,40,3,2,2,3,1,2,1
+1,4,6,2,5,660,3,4,2,4,1,4,1,23,3,1,1,2,1,1,1
+1,4,24,2,2,2835,3,5,3,3,1,4,2,53,3,2,1,3,1,1,1
+1,4,24,2,1,2670,1,5,4,3,1,4,3,35,3,2,1,4,1,2,1
+1,4,12,1,8,3447,3,3,4,2,1,3,1,35,3,2,1,2,2,1,1
+1,4,15,2,3,3568,1,5,4,2,1,2,3,54,1,1,1,4,1,2,1
+1,2,21,4,9,3652,1,4,2,3,1,3,2,27,3,2,2,3,1,1,1
+1,1,24,2,3,3660,1,3,2,2,1,4,3,28,3,2,1,3,1,1,1
+1,3,9,2,3,1126,2,5,2,1,1,4,1,49,3,2,1,3,1,1,1
+1,3,6,3,3,683,1,2,2,2,1,1,2,29,1,2,1,3,1,1,1
+1,3,12,2,2,2251,1,3,1,2,1,2,3,46,3,2,1,2,1,1,1
+1,4,12,2,1,4675,5,2,1,2,1,4,3,20,3,1,1,3,1,1,1
+1,2,21,3,0,2353,1,3,1,1,1,4,2,47,3,2,2,3,1,1,1
+1,1,21,2,3,3357,4,2,4,2,1,2,3,29,1,2,1,3,1,1,1
+1,4,6,2,0,672,1,1,1,2,1,4,1,54,3,2,1,1,1,2,1
+1,1,6,4,3,338,3,5,4,3,1,4,3,52,3,2,2,3,1,1,1
+1,4,9,2,3,2697,1,3,1,3,1,2,1,32,3,2,1,3,2,1,1
+1,4,9,2,0,2507,3,5,2,3,1,4,4,51,3,3,1,2,1,1,1
+1,4,15,3,3,1478,1,3,4,4,1,3,1,33,1,2,2,3,1,1,1
+1,4,12,4,6,3565,5,2,2,3,1,1,2,37,3,2,2,2,2,1,1
+1,4,15,2,2,2221,3,3,2,2,1,4,3,20,3,1,1,3,1,1,1
+1,4,6,4,3,1898,5,3,1,3,1,2,1,34,3,2,2,2,2,1,1
+1,2,6,3,9,1449,2,5,1,1,1,2,3,31,1,2,2,3,2,1,1
+1,4,15,3,2,960,4,4,3,2,1,2,2,30,3,2,2,3,1,1,1
+1,4,36,2,1,8133,1,3,1,2,1,2,2,30,1,2,1,3,1,1,1
+1,4,9,2,2,2301,2,2,2,2,1,4,2,22,3,1,1,3,1,1,1
+1,4,6,3,9,1743,2,3,1,3,1,2,1,34,3,2,2,2,1,1,1
+1,2,12,2,2,983,4,2,1,2,1,4,1,19,3,1,1,2,1,1,1
+1,4,18,3,3,2320,1,1,2,4,1,3,1,34,3,2,2,3,1,1,1
+1,1,12,1,8,339,1,5,4,4,1,1,3,45,1,2,1,2,1,1,1
+1,3,24,2,3,5152,1,4,4,3,1,2,3,25,1,2,1,3,1,1,1
+1,3,24,2,2,3749,1,2,2,2,1,4,3,26,3,2,1,3,1,1,1
+1,4,9,4,3,3074,5,3,1,3,1,2,1,33,3,2,2,3,2,1,1
+0,3,9,2,3,745,1,3,3,2,1,2,1,28,3,2,1,2,1,1,1
+1,4,24,2,0,1469,2,5,4,4,1,4,1,41,3,1,1,2,1,1,1
+1,1,6,2,2,1374,1,3,1,3,1,2,1,36,1,2,1,2,1,2,1
+1,4,6,1,0,783,5,3,1,3,3,2,1,26,2,2,1,2,2,1,1
+1,1,21,2,3,2606,1,2,4,2,1,4,2,28,3,1,1,4,1,2,1
+1,4,54,0,1,9436,5,3,2,3,1,2,2,39,3,2,1,2,2,1,1
+1,4,12,4,3,930,5,5,4,3,1,4,1,65,3,2,4,3,1,1,1
+1,4,48,4,1,2751,5,5,4,3,1,3,3,38,3,2,2,3,2,2,1
+1,4,6,4,0,250,4,3,2,2,1,2,1,41,1,2,2,2,1,1,1
+1,2,24,2,0,1201,1,2,4,3,1,1,2,26,3,2,1,3,1,1,1
+1,1,6,2,0,662,1,2,3,3,1,4,1,41,3,2,1,2,2,2,1
+1,4,15,2,1,1300,5,5,4,3,1,4,4,45,1,3,1,3,2,1,1
+1,4,24,1,9,1559,1,4,4,3,1,4,3,40,1,2,1,3,1,2,1
+1,3,12,2,3,3016,1,3,3,4,1,1,3,24,3,2,1,3,1,1,1
+1,4,15,4,3,1360,1,3,4,3,1,2,2,31,3,2,2,3,1,1,1
+1,4,6,0,0,1204,2,3,4,3,1,1,4,35,1,1,1,3,1,1,2
+1,4,10,2,0,1597,3,3,3,3,1,2,4,40,3,1,1,2,2,1,2
+1,4,12,2,3,2073,2,3,4,2,2,2,1,28,3,2,1,3,1,1,1
+1,4,11,2,9,2142,4,5,1,1,1,2,1,28,3,2,1,3,1,2,1
+1,1,10,4,2,2132,5,2,2,2,2,3,1,27,3,1,2,3,1,1,2
+1,4,10,2,0,1546,1,3,3,3,1,2,1,31,3,2,1,2,2,1,2
+1,4,24,4,0,1287,4,5,4,2,1,4,1,37,3,2,2,3,1,2,1
+1,4,10,2,0,1418,2,3,3,3,1,2,1,35,3,1,1,2,1,1,2
+1,3,6,4,0,1343,1,5,1,3,1,4,1,46,3,2,2,3,2,1,2
+1,4,18,2,0,2662,5,4,4,3,1,3,2,32,3,2,1,3,1,1,2
+1,4,18,4,3,6070,1,5,3,3,1,4,3,33,3,2,2,3,1,2,1
+1,4,24,4,6,1927,5,3,3,2,1,2,3,33,3,2,2,3,1,2,1
+1,4,18,4,3,2404,1,3,2,2,1,2,3,26,3,2,2,3,1,1,1
+1,4,6,4,3,1554,1,4,1,2,1,2,3,24,3,1,2,3,1,2,1
+1,4,22,2,0,1283,5,4,4,2,1,4,2,25,3,1,1,3,1,1,1
+1,4,24,3,0,717,5,5,4,4,1,4,3,54,3,2,2,3,1,2,1
+1,1,24,2,2,1747,1,2,4,3,2,1,2,24,3,2,1,2,1,1,2
+1,1,9,4,5,1288,2,5,3,3,3,4,1,48,3,2,2,3,2,1,2
+1,1,10,4,0,1038,1,4,4,3,2,3,2,49,3,2,2,3,1,2,1
+1,4,10,2,1,2848,2,3,1,3,2,2,1,32,3,2,1,3,2,1,1
+1,4,12,2,1,1413,4,4,3,3,1,2,2,55,3,2,1,3,1,1,2
+1,4,30,4,3,3077,5,5,3,3,1,2,3,40,3,2,2,3,2,2,1
+1,1,24,1,1,3632,1,3,1,2,3,4,3,22,1,1,1,3,1,1,2
+1,4,18,4,1,3229,5,1,2,3,1,4,4,38,3,2,1,4,1,2,1
+1,4,9,2,0,3577,2,3,1,3,3,2,1,26,3,1,1,3,2,1,2
+1,4,12,4,0,682,2,4,4,2,1,3,3,51,3,2,2,3,1,2,1
+1,4,10,2,3,1924,1,3,1,3,1,4,2,38,3,2,1,3,1,2,2
+1,4,10,2,6,727,3,5,4,3,1,4,4,46,3,3,1,3,1,2,1
+1,3,10,4,0,781,1,5,4,3,1,4,4,63,3,3,2,3,1,2,1
+1,1,12,4,0,2121,1,3,4,3,1,2,2,30,3,2,2,3,1,1,1
+1,4,12,4,6,701,1,3,4,3,1,2,3,32,3,2,2,3,1,1,1
+1,4,10,4,2,2069,5,3,2,4,1,1,3,26,3,2,2,3,1,1,2
+1,4,24,2,0,1525,4,4,4,2,1,3,3,34,3,2,1,3,2,2,1
+1,4,48,4,9,7629,5,5,4,1,1,2,3,46,1,2,2,4,2,1,1
+0,1,12,4,0,3499,1,3,3,2,2,2,1,29,3,2,2,3,1,1,1
+1,4,6,2,3,1346,2,5,2,3,1,4,4,42,1,3,1,3,2,2,1
+1,4,36,4,1,10477,5,5,2,3,1,4,4,42,3,3,2,3,1,1,1
+1,1,24,2,1,2924,1,3,3,3,3,4,4,63,1,2,1,3,2,2,1
+1,4,10,4,0,1231,1,5,3,3,1,4,1,32,3,2,2,2,2,1,2
+1,3,18,2,0,1961,1,5,3,2,1,2,3,23,3,2,1,4,1,1,1
+1,4,15,4,0,5045,5,5,1,2,1,4,3,59,3,2,1,3,1,2,1
+1,4,12,4,0,1255,1,5,4,3,1,4,1,61,3,2,2,2,1,1,1
+1,1,12,2,2,1858,1,2,4,2,1,1,3,22,3,1,1,3,1,1,1
+1,4,6,4,2,1221,5,3,1,4,1,2,2,27,3,2,2,3,1,1,1
+1,4,9,2,2,1388,1,3,4,2,1,2,1,26,3,1,1,3,1,1,1
+1,4,12,2,3,2279,5,3,4,3,1,4,4,37,3,3,1,3,1,2,1
+1,4,12,0,2,2759,1,5,2,3,1,4,2,34,3,2,2,3,1,1,1
+1,3,24,2,3,1258,3,3,3,2,1,3,3,57,3,2,1,2,1,1,1
+1,2,12,0,8,1410,1,3,2,3,1,2,1,31,3,2,1,2,1,2,1
+1,1,15,2,0,1403,1,3,2,2,1,4,3,28,3,1,1,3,1,1,1
+1,1,24,2,2,3021,1,3,2,1,1,2,1,24,3,1,1,2,1,1,1
+1,1,24,2,9,6568,1,3,2,4,1,2,3,21,2,2,1,2,1,1,1
+1,4,24,4,3,2578,4,5,2,3,1,2,3,34,3,2,1,3,1,1,1
+1,2,24,4,1,7758,4,5,2,2,1,4,4,29,3,1,1,3,1,1,1
+1,1,6,2,4,343,1,2,4,2,1,1,1,27,3,2,1,3,1,1,1
+1,4,21,3,2,1591,2,4,4,3,1,3,1,34,3,2,2,4,1,1,1
+1,1,27,2,3,3416,1,3,3,3,1,2,3,27,3,2,1,4,1,1,1
+0,1,12,0,5,1108,1,4,4,3,1,3,1,28,3,2,2,3,1,1,1
+1,2,27,3,1,5965,1,5,1,3,1,2,3,30,3,2,2,4,1,2,1
+1,2,15,2,5,1514,2,3,4,3,3,2,1,22,3,2,1,3,1,1,1
+1,4,30,4,3,6742,5,4,2,3,1,3,2,36,3,2,2,3,1,1,1
+1,1,18,2,2,3650,1,2,1,2,1,4,3,22,3,1,1,3,1,1,1
+1,1,21,2,2,3599,1,4,1,2,1,4,3,26,3,1,1,2,1,1,1
+1,4,60,4,0,13756,5,5,2,3,1,4,4,63,1,3,1,4,1,2,1
+1,2,9,2,0,276,1,3,4,4,1,4,1,22,3,1,1,2,1,1,1
+1,4,42,4,2,4041,3,3,4,3,1,4,1,36,3,2,2,3,1,2,1
+1,2,9,2,3,458,1,3,4,3,1,3,1,24,3,2,1,3,1,1,1
+0,2,9,2,2,918,1,3,4,2,1,1,2,30,3,2,1,3,1,1,1
+1,4,24,2,0,7393,1,3,1,3,1,4,2,43,3,2,1,2,2,1,1
+1,3,10,2,4,1225,1,3,2,3,1,2,3,37,3,2,1,3,1,2,1
+1,1,24,2,1,2812,5,5,2,2,1,4,1,26,3,1,1,3,1,1,1
+1,4,15,2,1,3029,1,4,2,3,1,2,3,33,3,2,1,3,1,1,1
+1,3,12,4,0,1480,3,1,2,3,1,4,4,66,1,3,3,1,1,1,1
+1,3,6,4,6,1047,1,3,2,2,1,4,2,50,3,2,1,2,1,1,1
+1,4,15,4,3,1471,1,3,4,3,1,4,4,35,3,3,2,3,1,2,1
+1,4,24,2,2,5511,2,3,4,3,1,1,3,25,2,2,1,3,1,1,1
+1,2,9,2,3,1206,1,5,4,2,1,4,1,25,3,2,1,3,1,1,1
+1,2,24,3,3,6403,1,2,1,3,1,2,3,33,3,2,1,3,1,1,1
+1,4,12,2,3,707,1,3,4,3,1,2,1,30,1,2,2,3,1,1,1
+1,4,12,3,1,1503,1,3,4,4,1,4,1,41,3,1,1,3,1,1,1
+1,2,12,2,0,6078,1,4,2,3,1,2,3,32,3,2,1,3,1,1,1
+1,2,27,2,9,2528,1,2,4,2,1,1,2,32,3,2,1,3,2,2,1
+1,2,12,2,9,1037,2,4,3,3,1,4,1,39,3,2,1,2,1,1,1
+1,1,6,2,1,1352,3,1,1,2,1,2,2,23,3,1,1,1,1,2,1
+1,4,24,2,3,3181,1,2,4,2,1,4,2,26,3,2,1,3,1,2,1
+1,4,18,2,3,4594,1,2,3,3,1,2,3,32,3,2,1,3,1,2,1
+1,2,48,2,10,5381,5,1,3,3,1,4,4,40,1,3,1,1,1,2,1
+1,4,15,2,1,4657,1,3,3,3,1,2,3,30,3,2,1,3,1,2,1
+1,2,9,2,9,1391,1,3,2,4,1,1,1,27,1,2,1,3,1,2,1
+1,2,18,2,9,1913,4,2,3,4,1,3,1,36,1,2,1,3,1,2,1
+1,4,42,2,3,7166,5,4,2,4,1,4,2,29,3,1,1,3,1,2,1
+1,4,13,2,3,1409,2,1,2,2,1,4,1,64,3,2,1,3,1,1,1
+1,4,24,3,9,2978,5,3,4,3,1,4,1,32,3,2,2,3,2,2,1
+1,4,12,4,3,976,5,5,4,3,1,4,3,35,3,2,2,3,1,1,1
+1,4,24,3,9,2375,3,3,4,3,1,2,3,44,3,2,2,3,2,2,1
+1,4,12,4,3,522,3,5,4,3,1,4,2,42,3,2,2,3,2,2,1
+1,4,28,4,3,2743,1,5,4,3,1,2,3,29,3,2,2,3,1,1,1
+1,4,11,4,3,1154,2,1,4,2,1,4,1,57,3,2,3,2,1,1,1
+1,4,24,4,1,5804,4,3,4,3,1,2,1,27,3,2,2,3,1,1,1
+1,4,18,4,3,1169,5,3,4,3,1,3,2,29,3,2,2,3,1,2,1
+1,1,15,4,2,1478,1,5,4,3,1,4,3,44,3,2,2,3,2,2,1
+1,4,12,2,3,776,1,3,4,4,1,2,1,28,3,2,1,3,1,1,1
+1,2,11,4,0,1322,4,3,4,2,1,4,3,40,3,2,2,3,1,1,1
+1,2,16,4,0,1175,1,1,2,3,1,3,3,68,3,3,3,1,1,2,1
+1,4,12,2,0,2133,5,5,4,2,1,4,4,52,3,3,1,4,1,2,1
+1,4,15,4,3,1829,1,5,4,3,1,4,3,46,3,2,2,3,1,2,1
+1,4,12,4,3,717,1,5,4,3,1,4,1,52,3,2,3,3,1,1,1
+1,2,39,3,6,11760,2,4,2,3,1,3,4,32,3,1,1,3,1,2,1
+0,2,9,4,6,1501,1,5,2,2,1,3,3,34,3,2,2,4,1,2,1
+1,1,12,2,6,1200,5,3,4,2,1,4,2,23,1,1,1,3,1,2,1
+1,2,9,2,0,3195,5,3,1,2,1,2,1,33,3,2,1,2,1,1,1
+1,4,30,4,3,4530,1,4,4,2,1,4,3,26,3,1,1,4,1,2,1
+0,4,12,3,5,1555,4,5,4,3,1,4,4,55,3,3,2,3,2,1,1
+1,2,15,4,9,2326,3,3,2,3,1,4,3,27,1,2,1,3,1,1,1
+1,2,18,4,9,1887,5,3,4,4,1,4,1,28,1,2,2,3,1,1,1
+1,4,12,4,9,1264,5,5,4,3,1,4,4,57,3,1,1,2,1,1,1
+1,4,7,3,3,846,5,5,3,3,1,4,4,36,3,3,1,3,1,1,1
+1,4,15,4,6,1532,2,3,4,2,1,3,3,31,3,2,1,3,1,1,1
+1,4,6,3,3,935,1,3,3,2,1,2,1,24,3,2,1,3,1,1,1
+1,1,27,4,9,2442,1,5,4,3,1,4,3,43,2,2,4,4,2,2,1
+1,2,18,4,9,3590,1,1,3,4,1,3,3,40,3,2,3,1,2,2,1
+1,4,21,4,2,2288,1,2,4,2,1,4,2,23,3,2,1,3,1,2,1
+1,4,27,3,9,5117,1,4,3,3,1,4,3,26,3,2,2,3,1,1,1
+1,1,39,4,2,14179,5,4,4,3,1,4,2,30,3,2,2,4,1,2,1
+1,4,15,2,3,1386,5,3,4,4,1,2,1,40,3,1,1,3,1,2,1
+1,4,12,4,3,618,1,5,4,3,1,4,1,56,3,2,1,3,1,1,1
+1,4,12,2,2,1574,1,3,4,3,1,2,1,50,3,2,1,3,1,1,1
+1,4,6,4,3,700,5,5,4,3,1,4,4,36,3,3,2,3,1,1,1
+1,4,12,2,3,886,5,3,4,2,1,2,3,21,3,2,1,3,1,1,1
+1,4,36,2,1,4686,1,3,2,3,1,2,4,32,3,3,1,4,1,2,1
+1,2,9,2,3,790,3,3,4,2,1,3,1,66,3,2,1,2,1,1,1
+0,2,12,2,3,766,3,3,4,3,1,3,1,66,3,2,1,2,1,1,1
+1,1,20,2,2,2212,5,4,4,3,1,4,3,39,3,2,1,3,1,2,1
+1,2,10,2,0,7308,1,1,2,3,1,4,4,70,1,3,1,4,1,2,1
+1,2,24,4,6,5743,1,2,2,2,1,4,4,24,3,3,2,3,1,2,1
+1,1,14,2,0,3973,1,1,1,3,1,4,4,22,3,3,1,3,1,1,1
+1,2,60,3,3,7418,5,3,1,3,1,1,1,27,3,2,1,2,1,1,1
+1,2,20,3,10,2629,1,3,2,3,1,3,3,29,1,2,2,3,1,2,1
+1,2,18,2,9,1941,4,3,4,3,1,2,2,35,3,2,1,2,1,2,1
+1,2,24,3,2,2333,5,2,4,3,1,2,2,29,1,2,1,2,1,1,1
+1,4,12,2,1,2445,5,2,2,4,1,4,3,26,3,1,1,3,1,2,1
+1,2,20,2,1,6468,5,1,1,1,1,4,1,60,3,2,1,4,1,2,1
+1,2,18,4,2,7374,1,1,4,3,1,4,2,40,2,2,2,4,1,2,1
+1,4,15,2,1,3812,2,2,1,2,1,4,3,23,3,2,1,3,1,2,1
+0,1,28,2,0,4006,1,3,3,3,1,2,3,45,3,2,1,2,1,1,1
+1,2,12,2,0,7472,5,1,1,2,1,2,1,24,3,1,1,1,1,1,1
+1,3,12,2,2,1424,5,5,3,2,1,4,1,55,3,2,1,4,1,2,1
+1,2,12,2,1,2028,5,3,4,3,1,2,3,30,3,2,1,3,1,1,1
+1,4,15,2,0,5324,3,5,1,2,1,4,4,35,3,3,1,3,1,1,1
+1,2,36,2,3,2323,1,4,4,3,1,4,3,24,3,1,1,3,1,1,1
+1,4,12,2,6,1393,1,5,4,3,1,4,2,47,1,2,3,3,2,2,1
+1,4,18,2,2,1984,1,3,4,3,1,4,4,47,1,3,2,3,1,1,1
+1,4,24,2,3,999,5,5,4,3,1,2,3,25,3,2,2,3,1,1,1
+1,4,36,2,9,7409,5,5,3,3,1,2,2,37,3,2,2,3,1,1,1
+1,4,15,2,2,2186,5,4,1,2,1,4,1,33,1,1,1,2,1,1,1
+1,3,36,2,3,4473,1,5,4,3,1,2,3,31,3,2,1,3,1,1,1
+1,4,24,2,8,937,1,2,4,4,1,3,3,27,3,2,2,2,1,1,1
+1,4,18,2,2,3422,1,5,4,3,1,4,2,47,1,2,3,3,2,2,1
+1,4,24,2,3,3105,5,2,4,3,1,2,3,25,3,2,2,3,1,1,1
+1,4,12,4,6,2748,1,5,2,2,1,4,4,57,1,3,3,2,1,1,1
+1,2,18,2,5,3872,1,1,2,2,1,4,3,67,3,2,1,3,1,2,1
+1,4,27,2,5,5190,5,5,4,3,1,4,2,48,3,2,4,3,2,2,1
+1,2,18,2,2,3001,1,4,2,2,1,4,1,40,3,1,1,3,1,1,1
+1,4,24,3,9,3863,1,3,1,3,1,2,4,32,3,3,1,3,1,1,1
+1,4,12,4,2,5801,5,5,2,3,1,4,2,49,3,1,1,3,1,2,2
+1,4,12,4,2,1592,4,4,3,2,1,2,2,35,3,2,1,3,1,1,1
+1,4,12,4,9,1185,1,3,3,2,1,2,1,27,3,2,2,3,1,1,1
+1,4,18,4,2,3780,1,2,3,1,1,2,3,35,3,2,2,4,1,2,1
+1,2,18,4,2,3612,1,5,3,2,1,4,2,37,3,2,1,3,1,2,2
+1,4,12,2,9,1076,1,3,2,4,1,2,1,26,3,2,1,3,1,2,1
+1,4,12,2,0,3527,5,2,2,3,1,3,2,45,3,2,1,4,2,2,1
+1,4,18,2,3,2051,1,2,4,3,1,1,1,33,3,2,1,3,1,1,1
+1,4,12,4,2,3331,1,5,2,3,1,4,2,42,2,2,1,3,1,1,1
+1,1,18,0,9,3104,1,4,3,3,1,1,2,31,1,2,1,3,1,2,1
+1,4,24,4,3,2611,1,5,4,4,2,3,1,46,3,2,2,3,1,1,1
+1,1,12,4,1,1409,1,5,4,3,1,3,1,54,3,2,1,3,1,1,1
+1,4,24,2,3,1311,2,4,4,4,1,3,2,26,3,2,1,3,1,2,1
+1,4,6,2,3,2108,1,4,2,4,1,2,1,29,3,1,1,3,1,1,1
+1,4,24,4,1,4042,5,4,3,3,1,4,2,43,3,2,2,3,1,2,1
+1,4,12,4,0,926,1,1,1,2,1,2,2,38,3,2,1,1,1,1,1
+1,1,12,2,3,1680,3,5,3,4,1,1,1,35,3,2,1,3,1,1,1
+1,4,24,2,0,1249,1,2,4,4,1,2,1,28,3,2,1,3,1,1,1
+1,4,24,4,0,2463,2,4,4,4,1,3,2,27,3,2,2,3,1,2,1
+1,4,6,2,3,1595,1,4,3,3,1,2,2,51,3,2,1,3,2,1,1
+1,4,24,4,5,2058,1,3,4,1,1,2,1,33,3,2,2,3,1,2,1
+1,4,24,2,1,7814,1,4,3,3,1,3,3,38,3,2,1,4,1,2,1
+1,4,6,4,3,1740,1,5,2,4,1,2,1,30,3,1,2,3,1,1,1
+1,4,12,4,3,1240,5,5,4,2,1,2,1,38,3,2,2,3,1,2,1
+1,4,24,4,1,6842,5,3,2,3,1,4,2,46,3,2,2,4,2,2,1
+1,4,24,4,2,5150,1,5,4,3,1,4,3,33,3,2,1,3,1,2,1
+1,1,6,2,0,1203,2,5,3,3,1,2,2,43,3,2,1,3,1,2,1
+1,4,6,4,0,2080,3,3,1,4,1,2,3,24,3,2,1,3,1,1,1
+1,4,6,2,6,1538,1,2,1,2,1,2,4,56,3,2,1,3,1,1,1
+1,2,24,4,0,3878,2,2,4,1,1,2,3,37,3,2,1,3,1,2,1
+1,2,30,2,2,3832,1,2,2,4,1,1,2,22,3,2,1,3,1,1,1
+1,4,15,2,0,3186,4,4,2,2,1,3,3,20,3,1,1,3,1,1,1
+1,2,24,2,3,2896,2,2,2,3,1,1,3,29,3,2,1,3,1,1,1
+1,2,24,3,9,6967,2,4,4,3,1,4,3,36,3,1,1,4,1,2,1
+0,4,36,2,6,1819,1,3,4,3,1,4,4,37,2,3,1,3,1,2,1
+0,4,24,2,3,5943,5,2,1,2,1,1,3,44,3,2,2,3,1,2,1
+0,4,36,4,2,7127,1,2,2,2,1,4,2,23,3,1,2,3,1,2,1
+0,4,36,2,2,3349,1,3,4,2,1,2,3,28,3,2,1,4,1,2,1
+0,4,36,2,2,10974,1,1,4,2,1,2,3,26,3,2,2,4,1,2,1
+1,4,6,2,3,518,1,3,3,2,1,1,1,29,3,2,1,3,1,1,1
+1,4,18,2,3,1126,5,2,4,2,1,2,1,21,3,1,1,3,1,2,1
+1,2,12,4,1,1860,1,1,4,3,1,2,3,34,3,2,2,4,1,2,1
+1,4,36,4,3,9566,1,3,2,2,1,2,3,31,2,2,2,3,1,1,1
+1,1,12,2,3,701,1,3,4,4,1,2,1,40,3,2,1,2,1,1,1
+1,2,12,2,3,2930,1,4,2,2,1,1,1,27,3,2,1,3,1,1,1
+1,4,18,2,3,1505,1,3,4,3,1,2,4,32,3,3,1,4,1,2,1
+1,4,18,4,3,2238,1,3,2,2,1,1,3,25,3,2,2,3,1,1,1
+1,4,4,4,3,1503,1,4,2,3,1,1,1,42,3,2,2,2,2,1,1
+1,4,24,4,1,2197,5,4,4,3,1,4,3,43,3,2,2,3,2,2,1
+1,3,12,2,3,1881,1,3,2,2,1,2,3,44,3,1,1,2,1,2,1
+1,1,18,4,3,1880,1,4,4,4,1,1,2,32,3,2,2,4,1,2,1
+1,1,18,2,3,2389,1,2,4,2,1,1,3,27,2,2,1,3,1,1,1
+1,2,24,2,3,1967,1,5,4,2,1,4,3,20,3,2,1,3,1,2,1
+1,4,4,4,0,3380,1,4,1,2,1,1,1,37,3,2,1,3,2,1,1
+1,4,4,4,0,1455,1,4,2,3,1,1,1,42,3,2,3,2,2,1,1
+1,4,7,4,3,730,5,5,4,3,1,2,2,46,3,1,2,2,1,2,1
+1,2,18,0,2,3244,1,3,1,2,1,4,3,33,1,2,2,3,1,2,1
+0,2,9,2,3,1670,1,2,4,2,1,2,3,22,3,2,1,3,1,2,1
+1,2,48,2,3,3979,5,4,4,3,1,1,3,41,3,2,2,3,2,2,1
+0,2,12,2,2,1922,1,3,4,3,1,2,2,37,3,2,1,2,1,1,1
+1,2,18,4,2,1295,1,2,4,2,1,1,2,27,3,2,2,3,1,1,1
+1,4,4,4,3,1544,1,4,2,3,1,1,1,42,3,2,3,2,2,1,1
+1,2,8,2,9,907,1,2,3,4,1,2,1,26,3,2,1,3,1,2,1
+1,2,30,2,3,1715,5,3,4,2,1,1,3,26,3,2,1,3,1,1,1
+1,3,10,4,3,1347,5,4,4,3,1,2,2,27,3,2,2,3,1,2,1
+1,2,12,2,0,1007,4,3,4,4,1,1,1,22,3,2,1,3,1,1,1
+1,4,12,4,2,1402,3,4,3,2,1,4,3,37,3,1,1,3,1,2,1
+1,2,12,2,0,2002,1,4,3,3,1,4,2,30,3,1,1,3,2,2,1
+1,4,12,4,6,2096,1,4,2,3,1,3,1,49,3,2,1,2,2,1,1
+1,4,12,2,0,1101,1,3,3,4,1,2,1,27,3,2,2,3,1,2,1
+1,4,10,2,8,894,5,4,4,2,1,3,2,40,3,2,1,3,1,2,1
+1,2,11,2,2,1577,4,2,4,2,1,1,1,20,3,2,1,3,1,1,1
+1,4,33,3,9,2764,1,3,2,2,1,2,3,26,3,2,2,3,1,2,1
+1,2,48,0,0,8358,3,2,1,2,1,1,3,30,3,2,2,3,1,1,1
+1,3,12,2,2,1474,1,2,4,2,1,1,2,33,1,2,1,4,1,2,1
+1,4,24,2,1,5433,5,1,2,2,1,4,2,26,3,1,1,4,1,2,1
+1,2,14,2,9,1410,3,5,1,4,1,2,1,35,3,2,1,3,1,2,1
+1,4,20,4,0,3485,5,2,2,1,1,4,1,44,3,2,2,3,1,2,1
+1,4,18,4,1,3850,1,4,3,3,1,1,3,27,3,2,2,3,1,1,1
+0,2,60,2,0,7408,2,2,4,2,1,2,2,24,3,2,1,4,1,1,1
+1,3,24,2,3,1377,2,5,4,2,1,2,4,47,3,3,1,3,1,2,1
+1,4,30,3,9,4272,2,3,2,3,1,2,2,26,3,2,2,2,1,1,1
+1,2,24,3,3,1553,2,4,3,2,1,2,2,23,3,1,2,3,1,2,1
+1,2,36,3,9,9857,2,4,1,3,1,3,2,31,3,2,2,2,2,2,1
+1,4,6,4,0,362,2,3,4,2,1,4,3,52,3,2,2,2,1,1,1
+1,4,12,4,2,1935,1,5,4,3,1,4,1,43,3,2,3,3,1,2,1
+1,4,48,2,3,10222,5,4,4,3,1,3,3,37,2,2,1,3,1,2,1
+1,3,12,2,0,1330,1,2,4,3,1,1,1,26,3,2,1,3,1,1,1
+1,4,36,2,6,9055,5,3,2,3,1,4,4,35,3,3,1,2,2,2,1
+1,2,26,2,1,7966,1,2,2,3,1,3,3,30,3,2,2,3,1,1,1
+1,2,30,1,2,3496,4,3,4,3,1,2,3,34,2,2,1,3,2,2,1
+1,2,36,2,1,6948,1,3,2,3,1,2,3,35,3,1,1,4,1,2,1
+1,2,48,0,9,12204,5,3,2,3,1,2,3,48,1,2,1,4,1,2,1
+0,1,36,2,2,3446,1,5,4,3,1,2,3,42,3,2,1,3,2,1,1
+0,1,12,2,6,684,1,3,4,3,1,4,3,40,3,1,1,2,2,1,1
+0,1,33,4,2,4281,3,3,1,2,1,4,3,23,3,2,2,3,1,1,1
+0,1,42,2,3,7174,5,4,4,2,1,3,3,30,3,2,1,4,1,2,1
+0,1,24,1,3,1546,1,4,4,3,3,4,3,24,1,1,1,2,1,1,1
+0,1,24,2,2,2359,2,1,1,1,1,1,2,33,3,2,1,3,1,1,1
+0,4,24,2,3,3621,2,5,2,3,1,4,3,31,3,2,2,3,1,1,1
+0,1,12,2,4,741,2,1,4,2,1,3,2,22,3,2,1,3,1,1,1
+0,1,12,2,2,7865,1,5,4,3,1,4,4,53,3,3,1,4,1,2,1
+1,1,24,2,1,2910,1,4,2,3,1,1,4,34,3,3,1,4,1,2,1
+1,1,18,4,0,5302,1,5,2,3,1,4,4,36,3,3,3,4,1,2,1
+1,1,36,2,2,3620,1,3,1,3,3,2,2,37,3,2,1,3,2,1,1
+1,1,18,2,3,3509,1,4,4,2,3,1,1,25,3,2,1,3,1,1,1
+1,2,12,2,2,3017,1,2,3,2,1,1,1,34,3,1,1,4,1,1,1
+1,1,12,2,2,1657,1,3,2,3,1,2,1,27,3,2,1,3,1,1,1
+1,1,8,4,10,1164,1,5,3,3,1,4,4,51,1,3,2,4,2,2,1
+0,1,36,4,2,6229,1,2,4,2,2,4,4,23,3,1,2,2,1,2,1
+0,1,24,1,0,1193,1,1,1,2,2,4,4,29,3,1,2,1,1,1,1
+1,1,30,0,2,4583,1,3,2,1,3,2,1,32,3,2,2,3,1,1,1
+1,1,36,4,2,5371,1,3,3,3,3,2,2,28,3,2,2,3,1,1,1
+1,1,12,2,2,708,1,3,2,3,3,3,2,38,3,2,1,2,2,1,1
+1,1,21,4,0,571,1,5,4,3,1,4,1,65,3,2,2,3,1,1,1
+1,1,30,2,3,2522,1,5,1,3,3,3,2,39,3,2,1,3,2,1,1
+0,1,36,2,2,5179,1,4,4,3,1,2,2,29,3,2,1,3,1,1,1
+0,1,36,2,1,8229,1,3,2,3,1,2,2,26,3,2,1,3,2,1,1
+1,4,24,4,2,2028,1,4,2,3,1,2,2,30,3,2,2,2,1,1,1
+1,1,6,2,0,1374,5,1,4,2,1,3,2,75,3,2,1,4,1,2,1
+1,1,12,2,2,1289,1,3,4,3,3,1,2,21,3,2,1,2,1,1,1
+0,1,36,2,2,2712,1,5,2,3,1,2,2,41,1,2,1,3,2,1,1
+1,1,15,4,2,975,1,3,2,1,1,3,2,25,3,2,2,3,1,1,1
+1,2,6,3,2,1050,1,1,4,3,1,1,2,35,2,2,2,4,1,2,2
+1,1,6,4,0,609,1,4,4,2,1,3,2,37,3,2,2,3,1,1,1
+1,1,48,2,1,4788,1,4,4,3,1,3,2,26,3,2,1,3,2,1,1
+1,2,24,2,2,3069,2,5,4,3,1,4,4,30,3,3,1,3,1,1,1
+0,2,12,2,0,836,2,2,4,2,1,2,2,23,1,2,1,2,1,1,1
+1,1,12,2,2,2577,1,3,2,1,1,1,3,42,3,2,1,3,1,1,1
+1,1,12,2,2,1620,1,3,2,2,2,3,2,30,3,2,1,3,1,1,1
+1,1,15,2,2,1845,1,2,4,2,3,1,2,46,3,1,1,3,1,1,1
+1,1,24,2,1,6579,1,1,4,3,1,2,4,29,3,3,1,4,1,2,1
+1,1,12,2,0,1893,1,3,4,2,3,4,2,29,3,2,1,3,1,2,1
+1,1,30,4,1,10623,1,5,3,3,1,4,4,38,3,3,3,4,2,2,1
+1,1,18,2,0,2249,2,4,4,3,1,3,3,30,3,2,1,4,2,2,1
+0,1,30,2,2,3108,1,2,2,1,1,4,2,31,3,2,1,2,1,1,1
+1,2,12,4,0,958,1,4,2,3,1,3,1,47,3,2,2,2,2,1,1
+1,4,24,2,1,9277,5,3,2,1,1,4,4,48,3,3,1,3,1,2,1
+1,4,24,4,10,6314,1,1,4,3,2,2,4,27,1,2,2,4,1,2,1
+1,1,12,4,1,1526,1,5,4,3,1,4,4,66,3,3,2,4,1,1,1
+1,1,12,2,2,3590,1,3,2,3,2,2,2,29,3,2,1,2,2,1,1
+1,1,24,4,1,6615,1,1,2,3,1,4,4,75,3,3,2,4,1,2,1
+1,1,6,4,2,1872,1,1,4,3,1,4,4,36,3,3,3,4,1,2,1
+1,4,12,2,0,2859,5,1,4,3,1,4,4,38,3,2,1,4,1,2,1
+1,4,18,4,3,1582,4,5,4,3,1,4,3,46,3,2,2,3,1,1,1
+1,4,6,2,8,1238,5,1,4,3,1,4,2,36,3,2,1,4,2,2,1
+1,1,12,2,2,2578,1,1,3,2,1,4,4,55,3,3,1,4,1,1,1
+1,1,15,4,2,1433,1,3,4,2,1,3,2,25,3,1,2,3,1,1,1
+1,1,42,2,2,7882,1,4,2,3,3,4,2,45,3,3,1,3,2,1,1
+1,1,24,2,2,4169,1,3,4,3,1,4,2,28,3,2,1,3,1,1,1
+1,1,36,2,2,3959,1,1,4,3,1,3,2,30,3,2,1,4,1,2,1
+1,1,36,2,0,3249,1,4,2,3,1,4,4,39,1,3,1,4,2,2,1
+1,1,24,2,2,3149,1,2,4,3,1,1,4,22,1,3,1,3,1,1,1
+0,1,12,4,2,2246,1,5,3,3,1,3,2,60,3,2,2,3,1,1,1
+1,1,13,4,9,1797,1,2,3,3,1,1,2,28,1,2,2,2,1,1,1
+1,1,20,4,2,4272,1,5,1,2,1,4,2,24,3,2,2,3,1,1,1
+1,1,24,4,1,2957,1,5,4,3,1,4,2,63,3,2,2,3,1,2,1
+1,1,36,4,2,2348,1,3,3,4,1,2,2,46,3,2,2,3,1,2,1
+1,3,42,0,9,6289,1,2,2,1,1,1,2,33,3,2,2,3,1,1,1
+1,1,24,4,1,6419,1,5,2,2,1,4,4,44,3,3,2,4,2,2,1
+0,1,48,4,1,6143,1,5,4,2,1,4,4,58,2,3,2,2,1,1,1
+1,4,24,4,6,1597,1,5,4,3,1,4,4,54,3,3,2,3,2,1,1
+1,1,36,2,10,15857,1,1,2,1,2,3,3,43,3,2,1,4,1,1,1
+1,4,24,4,3,2223,2,5,4,3,1,4,2,52,1,2,2,3,1,1,1
+1,4,48,3,3,7238,5,5,3,3,1,3,3,32,1,2,2,3,2,1,1
+1,2,30,3,9,2503,2,5,4,3,1,2,2,41,2,2,2,3,1,1,1
+1,2,18,2,9,2622,2,3,4,3,1,4,3,34,3,2,1,3,1,1,1
+1,2,24,2,2,4351,5,3,1,2,1,4,2,48,3,2,1,2,1,2,1
+1,2,6,2,3,368,5,5,4,3,1,4,2,38,3,2,1,3,1,1,1
+1,2,12,2,8,754,5,5,4,3,1,4,2,38,3,2,2,3,1,1,1
+1,4,24,4,3,2424,5,5,4,3,1,4,2,53,3,2,2,3,1,1,1
+1,2,48,3,9,6681,5,3,4,3,1,4,4,38,3,3,1,3,2,2,1
+1,2,18,3,9,2427,5,5,4,3,1,2,2,42,3,2,2,3,1,1,1
+0,2,24,4,3,1216,2,2,4,3,1,4,4,38,1,2,2,3,2,1,1
+1,2,6,2,3,753,1,3,2,2,3,3,1,64,3,2,1,3,1,1,1
+1,2,7,2,3,2576,1,3,2,3,3,2,1,35,3,2,1,3,1,1,2
+1,2,6,2,3,590,1,2,3,4,1,3,1,26,3,2,1,2,1,1,2
+1,2,8,2,3,1414,1,3,4,3,3,2,1,33,3,2,1,3,1,1,2
+1,2,12,2,3,1103,1,4,4,3,3,3,1,29,3,2,2,3,1,1,1
+1,2,12,3,3,585,1,3,4,4,2,4,1,20,3,1,2,3,1,1,1
+1,2,6,2,3,1068,1,5,4,3,1,4,3,28,3,2,1,3,2,1,1
+1,1,8,4,0,713,1,5,4,3,1,4,1,47,3,2,2,2,1,1,1
+1,2,12,2,3,1092,1,3,4,2,3,4,1,49,3,2,2,3,1,2,1
+1,2,7,2,3,2329,1,2,1,2,3,1,1,45,3,2,1,3,1,1,1
+1,2,13,4,3,882,1,2,4,3,3,4,1,23,3,2,2,3,1,1,1
+1,2,18,2,3,866,1,3,4,4,3,2,1,25,3,2,1,2,1,1,1
+1,2,7,2,3,2415,1,3,3,3,3,2,1,34,3,2,1,3,1,1,1
+1,2,13,2,3,2101,1,2,2,2,3,4,2,23,3,2,1,2,1,1,1
+1,2,18,2,3,1301,1,5,4,4,3,2,1,32,3,2,1,2,1,1,1
+1,2,18,2,3,1113,1,3,4,2,3,4,1,26,3,2,1,2,2,1,1
+1,2,8,2,3,760,1,4,4,2,3,2,1,44,3,2,1,2,1,1,1
+1,2,12,2,3,625,1,2,4,4,3,1,1,26,1,2,1,2,1,1,1
+1,3,6,4,0,1323,2,5,2,1,1,4,3,28,3,2,2,3,2,2,1
+1,1,9,4,3,1138,1,3,4,3,1,4,1,25,3,2,2,2,1,1,1
+1,2,18,4,3,1795,1,5,3,2,3,4,1,48,1,1,2,2,1,2,1
+1,2,15,4,3,2728,5,4,4,3,3,2,1,35,1,2,3,3,1,2,1
+1,2,6,2,3,484,1,4,3,4,3,3,1,28,1,2,1,2,1,1,1
+1,2,10,1,3,1048,1,3,4,3,1,4,1,23,2,2,1,2,1,1,1
+1,2,12,2,3,1155,1,5,3,4,3,3,1,40,1,2,2,2,1,1,1
+1,2,20,3,1,7057,5,4,3,3,1,4,2,36,1,1,2,4,2,2,1
+1,2,15,4,3,1537,5,5,4,3,3,4,1,50,3,2,2,3,1,2,1
+1,1,12,2,3,2214,1,3,4,3,1,3,2,24,3,2,1,2,1,1,1
+1,4,24,4,2,1585,1,4,4,3,1,3,2,40,3,2,2,3,1,1,1
+1,2,10,2,2,1521,1,3,4,1,1,2,3,31,3,2,1,2,1,1,1
+1,2,36,1,4,3990,5,2,3,2,1,2,4,29,1,2,1,1,1,1,1
+1,3,18,2,2,3049,1,2,1,2,1,1,2,45,2,2,1,2,1,1,1
+0,1,24,2,3,1282,2,3,4,2,1,2,3,32,3,2,1,2,1,1,1
+1,4,60,2,3,10144,2,4,2,2,1,4,1,21,3,2,1,3,1,2,1
+1,1,12,2,0,1168,1,3,4,4,1,3,1,27,3,2,1,2,1,1,1
+1,2,6,2,5,454,1,2,3,4,1,1,2,22,3,2,1,2,1,1,1
+1,4,15,3,1,3594,1,2,1,2,1,2,2,46,3,2,2,2,1,1,1
+1,4,12,2,2,1768,1,3,3,3,1,2,1,24,3,1,1,2,1,1,1
+1,4,60,3,3,15653,1,4,2,3,1,4,3,21,3,2,2,3,1,2,1
+1,3,12,3,0,2247,1,3,2,2,1,2,3,36,2,2,2,3,1,2,1
+1,4,24,2,3,1413,1,3,4,4,1,2,2,28,3,2,1,3,1,1,1
+1,2,45,4,1,4576,2,1,3,3,1,4,3,27,3,2,1,3,1,1,1
+1,1,24,4,3,1231,4,5,4,2,1,4,2,57,3,1,2,4,1,2,1
+0,1,36,2,1,8335,5,5,3,3,1,4,4,47,3,3,1,3,1,1,1
+1,2,36,4,1,5800,1,3,3,3,1,4,3,34,3,2,2,3,1,2,1
+1,1,18,3,6,8471,5,3,1,2,1,2,3,23,3,1,2,3,1,2,1
+1,1,30,2,2,3622,4,5,4,2,1,4,2,57,3,1,2,3,1,2,1
+1,1,6,4,3,1169,5,5,4,3,1,4,1,67,3,2,2,3,1,2,1
+1,4,15,2,4,1262,3,4,4,3,1,3,2,36,3,2,2,3,1,2,1
+1,3,24,4,2,3617,5,5,4,3,2,4,4,20,3,1,2,3,1,1,1
+1,2,30,4,0,2181,5,5,4,3,1,4,1,36,3,2,2,3,1,1,1
+0,1,48,1,9,7685,1,4,2,2,3,4,3,37,3,1,1,3,1,1,1
+1,4,48,4,6,6110,1,3,1,3,1,3,4,31,1,3,1,3,1,2,1
+1,4,24,2,0,3757,1,5,4,2,2,4,4,62,3,3,1,3,1,2,1
+1,1,42,4,5,3394,1,1,4,3,2,4,3,65,3,2,2,1,1,1,1
+1,4,36,4,9,6304,5,5,4,3,1,4,1,36,3,2,2,3,1,1,1
+1,4,9,4,6,1244,5,5,4,2,1,4,2,41,3,1,2,2,1,1,1
+1,4,6,2,0,3518,1,3,2,3,3,3,2,26,3,1,1,3,1,1,1
+1,4,36,0,5,2613,1,3,4,3,1,2,3,27,3,2,2,3,1,1,1
+1,1,48,2,6,7476,1,4,4,3,1,1,4,50,3,3,1,4,1,2,1
+0,4,24,2,9,4591,4,3,2,3,1,3,2,54,3,2,3,4,1,2,1
+0,2,18,2,2,1924,5,2,4,2,1,3,1,27,3,1,1,3,1,1,1
+0,2,72,2,3,5595,2,3,2,4,1,2,3,24,3,2,1,3,1,1,1
+0,2,48,3,6,6224,1,5,4,3,1,4,4,50,3,3,1,3,1,1,1
+1,3,15,2,6,1905,1,5,4,3,1,4,3,40,3,1,1,4,1,2,1
+1,4,21,3,1,2993,1,3,3,3,1,2,1,28,2,2,2,2,1,1,1
+1,4,36,3,1,8947,5,4,3,3,1,2,3,31,2,2,1,4,2,2,1
+1,1,24,2,2,4020,1,3,2,3,1,2,3,27,2,2,1,3,1,1,1
+1,2,18,2,1,2779,1,3,1,4,1,3,3,21,3,1,1,3,1,2,1
+1,4,21,2,0,2782,3,4,1,2,1,2,3,31,1,2,1,4,1,1,1
+1,4,12,2,0,1884,1,5,4,3,1,4,3,39,3,2,1,4,1,2,1
+1,4,36,4,1,11054,5,3,4,3,1,2,3,30,3,2,1,4,1,2,1
+1,2,60,3,3,9157,5,3,2,3,1,2,4,27,3,3,1,4,1,1,1
+1,2,42,1,1,9283,1,1,1,3,1,2,4,55,1,3,1,4,1,2,1
+1,4,60,2,0,6527,5,3,4,3,1,4,4,34,3,3,1,3,2,2,1
+1,4,15,4,1,3368,4,5,3,3,1,4,4,23,3,1,2,3,1,2,1
+1,1,15,2,0,2511,1,1,1,2,1,4,3,23,3,1,1,3,1,1,1
+1,1,36,2,1,5493,1,5,2,3,1,4,4,42,3,3,1,3,2,1,1
+1,4,6,2,4,1338,3,3,1,1,1,4,1,62,3,2,1,3,1,1,1
+1,2,9,2,3,1082,1,5,4,3,1,4,3,37,3,2,2,2,1,1,1
+1,4,18,4,3,1149,4,3,4,3,1,3,1,46,3,2,2,3,1,1,1
+1,2,15,2,5,1308,1,5,4,3,1,4,3,38,3,2,2,2,1,1,1
+1,2,20,0,1,6148,2,5,3,4,1,4,3,31,1,2,2,3,1,2,1
+1,4,12,2,2,1736,1,4,3,2,1,4,1,31,3,2,1,2,1,1,1
+1,4,12,2,3,3059,4,4,2,1,1,4,1,61,3,2,1,2,1,1,1
+0,1,24,2,2,2996,5,3,2,4,1,4,3,20,3,2,1,3,1,1,1
+1,4,30,4,1,7596,5,5,1,3,1,4,3,63,3,2,2,3,1,1,1
+1,4,30,2,1,4811,5,4,2,2,1,4,2,24,2,1,1,2,1,1,1
+1,4,6,2,2,1766,1,3,1,4,1,2,2,21,3,1,1,3,1,1,1
+1,2,24,2,1,2760,5,5,4,3,1,4,4,36,1,3,1,3,1,2,1
+1,4,24,4,5,5507,1,5,3,3,1,4,4,44,3,3,2,3,1,1,1
+1,2,9,2,6,1199,1,4,4,2,1,4,2,67,3,2,2,4,1,2,1
+1,3,24,2,2,2892,1,5,3,1,1,4,4,51,3,3,1,3,1,1,1
+1,2,36,3,0,2862,2,5,4,3,1,3,4,30,3,3,1,3,1,1,1
+0,1,9,2,0,654,1,3,4,3,1,3,3,28,3,2,1,2,1,1,1
+0,2,9,4,6,1136,4,5,4,3,1,3,4,32,3,3,2,3,2,1,1
+0,2,24,2,1,4113,3,2,3,2,1,4,3,28,3,1,1,3,1,1,1
+0,2,6,2,0,14555,5,1,1,3,1,2,2,23,3,2,1,1,1,2,1
+0,1,15,0,0,950,1,5,4,3,1,3,3,33,3,1,2,3,2,1,1
+0,1,24,4,0,1199,1,5,4,3,1,4,3,60,3,2,2,2,1,1,1
+0,1,12,0,0,1082,1,3,4,3,1,4,3,48,1,2,2,3,1,1,1
+0,2,30,2,0,2150,1,3,4,2,3,2,4,24,1,2,1,3,1,1,1
+0,2,36,4,0,2820,1,2,4,1,1,4,3,27,3,2,2,3,1,1,1
+0,2,48,2,3,3060,1,4,4,3,1,4,1,28,3,2,2,3,1,1,1
+0,1,18,2,3,2600,1,3,4,3,1,4,4,65,3,3,2,3,1,1,1
+0,4,21,0,0,5003,5,3,1,2,1,4,2,29,1,2,2,3,1,2,1
+0,2,60,2,6,6288,1,3,4,3,1,4,4,42,3,3,1,3,1,1,1
+0,4,24,3,0,2538,1,5,4,3,1,4,3,47,3,2,2,2,2,1,1
+0,4,9,2,3,1478,1,4,4,3,1,2,3,22,3,2,1,3,1,1,1
+0,2,39,4,3,4933,1,4,2,3,3,2,1,25,3,2,2,3,1,1,1
+0,4,18,4,0,1530,1,3,3,3,1,2,2,32,1,2,2,3,1,1,1
+0,2,9,1,0,1437,2,4,2,3,1,3,4,29,3,2,1,3,1,1,1
+0,1,15,2,4,1275,5,3,4,2,1,2,3,24,3,1,1,3,1,1,1
+0,1,24,2,3,1823,1,1,4,3,1,2,3,30,2,2,1,4,2,1,1
+0,1,9,2,0,1422,1,2,3,3,1,2,4,27,3,3,1,4,1,2,1
+0,1,18,2,4,1217,1,3,4,4,1,3,1,47,3,2,1,2,1,2,1
+0,1,36,2,0,9271,1,4,2,3,1,1,3,24,3,2,1,3,1,2,1
+0,1,36,3,9,2145,1,4,2,3,1,1,3,24,3,2,2,3,1,2,1
+0,1,36,2,0,1842,1,2,4,2,1,4,3,34,3,2,1,3,1,2,1
+0,2,18,3,2,4297,1,5,4,1,1,3,4,40,3,2,1,4,1,2,1
+0,1,6,4,2,3384,1,3,1,1,1,4,1,44,3,1,1,4,1,2,1
+0,2,18,4,3,1245,1,3,4,4,1,2,3,33,3,2,1,3,1,1,1
+0,4,15,2,6,4623,2,3,3,3,1,2,2,40,3,2,1,4,1,2,1
+0,2,30,4,2,8386,1,4,2,3,1,2,2,49,3,2,1,3,1,1,1
+0,1,24,3,3,1024,1,2,4,4,1,4,1,48,2,2,1,3,1,1,1
+0,2,36,2,0,14318,1,5,4,3,1,2,4,57,3,3,1,4,1,2,1
+0,2,6,1,6,433,4,2,4,2,1,2,2,24,1,1,1,3,2,1,1
+0,1,12,1,3,2149,1,3,4,1,1,1,4,29,3,3,1,3,1,1,1
+0,4,24,2,3,2397,3,5,3,3,1,2,3,35,1,2,2,3,1,2,1
+0,2,6,1,0,931,2,2,1,2,1,1,2,32,2,2,1,2,1,1,1
+0,2,15,3,5,1512,4,3,3,4,1,3,2,61,2,2,2,3,1,1,1
+0,2,24,0,9,4241,1,3,1,3,1,4,1,36,3,2,3,2,1,2,1
+0,2,24,4,2,4736,1,2,2,2,1,4,3,25,1,2,1,2,1,1,1
+0,2,15,0,0,1778,1,2,2,2,1,1,1,26,3,1,2,1,1,1,1
+0,3,15,2,3,2327,1,2,2,2,1,3,1,25,3,2,1,2,1,1,1
+0,1,24,1,2,6872,1,2,2,1,1,1,2,55,1,2,1,3,1,2,1
+0,1,12,2,6,795,1,2,4,2,1,4,2,53,3,2,1,3,1,1,1
+0,3,30,3,9,1908,1,5,4,3,1,4,1,66,3,2,1,4,1,2,1
+0,2,36,0,9,1953,1,5,4,3,1,4,4,61,3,3,1,4,1,2,1
+0,3,18,2,2,2864,1,3,2,3,1,1,1,34,3,2,1,2,2,1,1
+0,3,21,4,6,2319,1,2,2,1,1,1,3,33,3,1,1,3,1,1,1
+0,1,24,2,0,915,5,5,4,2,1,2,3,29,1,2,1,3,1,1,1
+0,3,24,2,0,947,1,4,4,3,1,3,4,38,1,3,1,3,2,1,1
+0,1,24,2,0,1381,5,3,4,2,1,2,2,35,3,2,1,3,1,1,1
+0,1,24,2,0,1285,5,4,4,2,1,4,4,32,3,1,1,3,1,1,1
+0,1,24,2,0,1371,5,3,4,2,1,4,1,25,3,1,1,3,1,1,1
+0,2,18,2,0,1042,5,3,4,2,1,2,2,33,3,2,1,3,1,1,1
+0,1,12,2,0,900,5,3,4,4,1,2,3,23,3,2,1,3,1,1,1
+0,1,24,2,0,1207,1,2,4,2,1,4,2,24,3,1,1,3,1,1,1
+0,2,18,0,0,2278,2,2,3,2,1,3,3,28,3,2,2,3,1,1,1
+0,1,60,3,9,6836,1,5,3,3,1,4,4,63,3,2,2,3,1,2,1
+0,1,24,2,2,3345,1,5,4,3,1,2,2,39,3,1,1,4,1,2,1
+0,1,6,1,6,1198,1,5,4,2,1,4,4,35,3,3,1,3,1,1,1
+0,2,48,2,9,15672,1,3,2,3,1,2,3,23,3,2,1,3,1,2,1
+0,1,60,2,9,7297,1,5,4,3,2,4,4,36,3,1,1,3,1,1,1
+0,4,18,2,5,1943,1,2,4,2,1,4,1,23,3,2,1,3,1,1,1
+0,1,18,2,3,3190,1,3,2,2,1,2,1,24,3,2,1,3,1,1,1
+0,2,9,1,1,5129,1,5,2,2,1,4,4,74,1,3,1,4,2,2,1
+0,4,18,3,2,1808,1,4,4,2,1,1,1,22,3,2,1,3,1,1,1
+0,3,10,2,0,1240,2,5,1,2,1,4,4,48,3,3,1,2,2,1,1
+0,1,12,2,0,759,1,4,4,3,1,2,1,26,3,2,1,3,1,1,1
+0,2,8,2,2,1237,1,3,3,2,1,4,1,24,3,2,1,3,1,1,1
+0,4,9,2,2,1980,1,2,2,2,2,2,3,19,3,1,2,3,1,1,1
+0,2,48,2,3,10961,4,4,1,3,2,2,4,27,1,2,2,3,1,2,1
+0,1,36,3,6,6887,1,3,4,3,1,3,2,29,2,2,1,3,1,2,1
+0,1,24,2,3,1938,1,2,4,1,1,3,2,32,3,2,1,3,1,1,1
+0,1,21,2,3,1835,1,3,3,2,1,2,1,25,3,2,2,3,1,2,1
+0,1,24,3,3,1659,1,2,4,2,1,2,3,29,3,1,1,2,1,2,1
+0,2,6,3,0,1209,1,1,4,3,1,4,2,47,3,2,1,4,1,2,1
+0,2,48,0,9,3844,2,4,4,3,1,4,4,34,3,3,1,2,2,1,1
+0,1,12,4,0,4843,1,5,3,3,2,4,2,43,3,1,2,3,1,2,1
+0,2,12,2,5,639,1,3,4,3,1,2,3,30,3,2,1,3,1,1,1
+0,2,48,2,3,5951,1,3,2,2,1,2,1,22,3,2,1,3,1,1,1
+0,2,36,0,3,3804,1,3,4,2,1,1,3,42,3,2,1,3,1,2,1
+0,4,36,3,3,4463,1,3,4,3,1,2,3,26,3,2,2,4,1,2,1
+0,4,36,3,9,7980,5,2,4,3,1,4,3,27,3,1,2,3,1,2,1
+0,3,36,2,3,4210,1,3,4,3,1,2,3,26,3,2,1,3,1,1,1
+0,4,6,2,2,4611,1,2,1,2,1,4,2,32,3,2,1,3,1,1,1
+0,2,24,2,1,11560,1,3,1,2,1,4,3,23,3,1,2,4,1,1,1
+0,4,18,0,9,4165,1,3,2,3,1,2,3,36,2,2,2,3,2,1,1
+0,2,24,2,2,4057,1,4,3,1,1,3,3,43,3,2,1,3,1,2,1
+0,4,18,1,0,6458,1,5,2,3,1,4,4,39,1,2,2,4,2,2,1
+0,4,12,2,0,1386,3,3,2,2,1,2,2,26,3,2,1,3,1,1,1
+0,1,36,2,6,1977,5,5,4,3,1,4,4,40,3,2,1,4,1,2,1
+0,2,18,4,2,1928,1,2,2,3,1,2,1,31,3,2,2,2,1,1,1
+0,4,12,2,2,1123,3,3,4,2,1,4,3,29,3,1,1,2,1,1,1
+0,2,24,2,10,11328,1,3,2,3,2,3,3,29,1,2,2,4,1,2,1
+0,2,24,4,10,11938,1,3,2,3,2,3,3,39,3,2,2,4,2,2,1
+0,2,27,4,3,2520,3,3,4,3,1,2,2,23,3,2,2,2,1,1,1
+0,2,30,3,3,1919,2,2,4,3,1,3,4,30,2,2,2,4,1,1,1
+0,2,60,1,10,14782,2,5,3,2,1,4,4,60,1,3,2,4,1,2,1
+0,2,36,2,3,2671,2,3,4,2,2,4,4,50,3,3,1,3,1,1,1
+0,2,36,2,6,12612,2,3,1,3,1,4,4,47,3,3,1,3,2,2,1
+0,2,45,2,3,3031,2,3,4,3,3,4,2,21,3,1,1,3,1,1,1
+0,1,12,1,3,626,1,3,4,2,1,4,1,24,1,2,1,2,1,1,1
+0,2,24,4,9,1935,1,5,4,1,1,4,1,31,3,2,2,3,1,2,1
+0,3,24,4,0,1344,5,4,4,3,1,2,1,37,1,2,2,2,2,1,1
+0,4,18,2,2,1533,1,2,4,4,2,1,2,43,3,2,1,2,2,1,1
+0,1,48,2,0,3931,1,4,4,3,1,4,4,46,3,3,1,3,2,1,1
+0,1,24,1,2,3349,3,2,4,3,1,4,4,30,3,3,1,3,2,2,1
+0,1,36,2,3,2302,1,3,4,1,1,4,3,31,3,1,1,3,1,1,1
+0,1,42,2,3,3965,1,2,4,3,1,3,3,34,3,2,1,3,1,1,1
+0,1,12,2,3,727,2,2,4,4,1,3,4,33,3,2,1,2,1,2,1
+0,4,48,2,9,3914,5,3,4,1,1,2,1,38,1,2,1,3,1,1,1
+0,1,48,2,9,4308,1,2,3,2,1,4,2,24,3,1,1,3,1,1,1
+0,2,12,2,3,1534,1,2,1,4,1,1,1,23,3,1,1,3,1,1,1
+0,4,18,4,0,2775,1,4,2,3,1,2,2,31,1,2,2,3,1,1,1
+0,1,40,4,6,5998,1,3,4,3,1,3,4,27,1,2,1,3,1,2,1
+0,3,15,4,3,1271,5,3,3,3,1,4,4,39,3,3,2,3,1,2,1
+0,2,12,2,0,1295,1,2,3,2,1,1,3,25,3,1,1,3,1,1,1
+0,2,36,2,1,9398,1,2,1,4,1,4,3,28,3,1,1,4,1,2,1
+0,2,12,2,2,951,2,2,4,2,1,4,3,27,1,1,4,3,1,1,1
+0,2,24,2,0,1355,1,2,3,2,1,4,3,25,3,2,1,2,1,2,1
+0,1,48,2,4,3051,1,3,3,3,1,4,3,54,3,2,1,3,1,1,1
+0,4,36,4,0,7855,1,3,4,2,1,2,1,25,2,2,2,3,1,2,1
+0,4,18,2,3,433,1,1,3,2,2,4,1,22,3,1,1,3,1,1,1
+0,4,36,3,9,9572,1,2,1,1,1,1,3,28,3,2,2,3,1,1,1
+0,2,24,1,6,1837,1,4,4,2,1,4,4,34,1,3,1,2,1,1,1
+0,2,30,4,0,4249,1,1,4,4,1,2,3,28,3,2,2,4,1,1,1
+0,2,30,4,0,5234,1,1,4,4,1,2,3,28,3,2,2,4,1,1,1
+0,1,48,2,3,6758,1,3,3,2,1,2,3,31,3,2,1,3,1,2,1
+0,1,9,2,3,1366,1,2,3,2,1,4,2,22,3,1,1,3,1,1,1
+0,1,24,1,10,1358,5,5,4,3,1,3,3,40,2,2,1,4,1,2,1
+0,1,18,2,2,2473,1,1,4,3,1,1,3,25,3,2,1,1,1,1,1
+0,3,9,0,3,1337,1,2,4,3,1,2,3,34,3,2,2,4,1,2,1
+0,1,48,2,0,7763,1,5,4,3,1,4,4,42,1,3,1,4,1,1,1
+0,2,15,1,0,1264,2,3,2,4,1,2,2,25,3,1,1,3,1,1,1
+0,2,15,2,0,2631,2,3,2,2,1,4,3,28,3,1,2,3,1,2,1
+0,2,48,2,0,6560,2,4,3,3,1,2,2,24,3,2,1,3,1,1,1
+0,1,24,2,0,3123,1,2,4,2,1,1,2,27,3,2,1,3,1,1,1
+0,1,36,4,6,8065,1,3,3,2,1,2,4,25,3,2,2,4,1,2,1
+0,1,24,2,3,2439,1,2,4,2,1,4,1,35,3,2,1,3,1,2,1
+0,2,36,2,2,9034,2,2,4,3,2,1,4,29,3,1,1,4,1,2,1
+0,2,60,2,0,14027,1,4,4,3,1,2,4,27,3,2,1,4,1,2,1
+0,1,36,4,1,9629,1,4,4,3,1,4,3,24,3,2,2,3,1,2,1
+0,2,12,2,3,1484,5,3,2,4,1,1,1,25,3,2,1,3,1,2,1
+0,1,18,2,2,1131,1,1,4,2,1,2,3,33,3,2,1,3,1,1,1
+0,2,24,3,2,2064,1,1,3,2,1,2,2,34,3,2,1,4,1,2,1
+0,2,18,2,1,12976,1,1,3,2,1,4,4,38,3,3,1,4,1,2,1
+0,4,21,3,9,2580,3,2,4,3,1,2,1,41,1,2,1,2,2,1,1
+0,4,27,2,0,2570,1,3,3,2,1,3,1,21,3,1,1,3,1,1,1
+0,2,27,2,9,3915,1,3,4,3,1,2,3,36,3,2,1,3,2,2,1
+0,4,10,2,0,1309,5,3,4,3,3,4,2,27,3,2,1,2,1,1,1
+0,1,24,2,0,4817,1,4,2,3,2,3,2,31,3,2,1,3,1,2,1
+0,1,12,2,0,2579,1,2,4,3,1,1,1,33,3,2,1,2,2,1,1
+0,2,36,3,0,2225,1,5,4,3,1,4,4,57,1,3,2,3,1,2,1
+0,1,18,2,2,4153,1,3,2,3,2,3,3,42,3,2,1,3,1,1,1
+0,1,18,0,2,3114,1,2,1,2,1,4,2,26,3,1,1,3,1,1,1
+0,1,18,4,2,2124,1,3,4,2,1,4,1,24,3,1,2,3,1,1,1
+0,1,18,1,2,1553,1,3,4,3,1,3,3,44,1,2,1,3,1,1,1
+0,1,30,2,2,2406,1,4,4,2,1,4,1,23,3,1,1,3,1,1,1
+0,1,24,3,0,1333,1,1,4,3,1,2,1,43,3,3,2,3,2,1,1
+0,1,48,0,2,7119,1,3,3,3,1,4,4,53,3,3,2,3,2,1,1
+0,1,24,3,0,4870,1,3,3,3,1,4,4,53,3,3,2,3,2,1,1
+0,1,12,4,0,691,1,5,4,3,1,3,2,35,3,2,2,3,1,1,1
+0,1,42,3,3,4370,1,4,3,3,1,2,2,26,1,2,2,3,2,2,1
+0,1,36,1,2,2746,1,5,4,3,1,4,3,31,1,2,1,3,1,1,1
+0,1,24,0,2,4110,1,5,3,3,1,4,4,23,1,1,2,3,2,1,1
+0,1,18,2,2,2462,1,3,2,3,1,2,3,22,3,2,1,3,1,1,1
+0,1,12,2,2,1282,1,3,2,2,1,4,3,20,3,1,1,3,1,1,1
+0,2,12,0,2,2969,1,2,4,2,1,3,2,25,3,1,2,3,1,1,1
+0,1,48,0,1,4605,1,5,3,3,1,4,4,24,3,3,2,3,2,1,1
+0,1,48,4,1,6331,1,5,4,3,1,4,4,46,3,3,2,3,1,2,1
+0,1,24,1,2,3552,1,4,3,3,1,4,3,27,1,2,1,3,1,1,1
+0,1,12,1,0,697,1,2,4,3,1,2,3,46,1,2,2,3,1,2,1
+0,1,24,2,0,1442,1,4,4,2,1,4,3,23,3,1,2,3,1,1,1
+0,1,27,0,9,5293,1,1,2,3,1,4,2,50,3,2,2,3,1,2,1
+0,1,21,3,6,3414,1,2,2,3,1,1,2,26,2,2,2,3,1,1,1
+0,1,18,2,2,2039,1,3,1,2,1,4,1,20,3,1,1,3,1,1,1
+0,1,24,1,9,3161,1,3,4,3,1,2,2,31,1,1,1,3,1,2,1
+0,1,12,2,8,902,1,4,4,4,1,4,2,21,3,1,1,3,1,1,1
+0,1,48,2,1,10297,1,4,4,3,1,4,4,39,3,3,3,3,2,2,1
+0,2,48,0,9,14421,1,3,2,3,1,2,3,25,2,2,1,3,1,2,1
+0,2,18,4,0,1056,1,5,3,3,3,3,1,30,3,2,2,3,1,1,1
+0,1,12,2,0,1274,1,2,3,2,1,1,1,37,1,2,1,2,1,1,1
+0,2,12,2,0,1223,1,5,1,1,1,1,1,46,3,1,2,3,1,1,1
+0,1,12,2,0,1372,1,4,2,1,1,3,3,36,3,2,1,3,1,1,1
+0,1,16,4,0,2625,1,5,2,3,3,4,2,43,3,1,1,3,1,2,2
+0,1,20,4,0,2235,1,3,4,4,3,2,2,33,1,1,2,3,1,1,2
+0,2,9,2,2,959,1,3,1,2,1,2,3,29,1,2,1,3,1,1,1
+0,2,18,4,0,884,1,5,4,3,1,4,3,36,3,2,1,3,2,2,1
+0,2,24,2,0,1246,1,2,4,3,1,2,1,23,1,2,1,2,1,1,1
+0,2,36,3,0,8086,2,5,2,3,1,4,3,42,2,2,4,4,1,2,1
+0,4,48,4,0,10127,3,3,2,3,1,2,4,44,3,3,1,3,1,1,1
+0,2,12,2,0,888,1,5,4,3,1,4,3,41,1,2,1,2,2,1,1
+0,4,12,2,6,719,1,5,4,3,1,4,3,41,1,2,1,2,2,1,1
+0,2,36,2,0,12389,5,3,1,3,1,4,4,37,1,3,1,3,1,2,1
+0,1,12,2,3,709,1,5,4,3,1,4,1,57,3,2,1,2,1,1,1
+0,2,15,1,0,6850,2,1,1,3,1,2,2,34,2,2,1,4,2,2,1
+0,4,10,2,2,2210,1,3,2,3,1,2,1,25,3,1,1,2,1,1,1
+0,4,30,1,1,7485,5,1,4,2,1,1,1,53,1,2,1,4,1,2,1
+0,1,18,1,0,1442,1,4,4,3,1,4,4,32,1,3,2,2,2,1,1
+0,4,12,4,3,797,5,5,4,2,1,3,2,33,3,2,1,2,2,1,1
+0,2,45,4,3,4746,1,2,4,3,1,2,2,24,1,2,2,2,1,1,1
+0,3,12,4,0,939,3,4,4,4,1,2,1,28,3,2,3,3,1,2,1
+0,2,21,2,9,1188,1,5,2,2,1,4,2,39,3,2,1,3,2,1,1
+0,4,48,4,1,11590,2,3,2,2,1,4,3,24,3,1,2,2,1,1,1
+0,3,12,1,9,609,1,2,4,2,1,1,1,26,1,2,1,1,1,1,1
+0,1,18,4,5,1190,1,1,2,2,1,4,4,55,3,3,3,1,2,1,1
+0,2,21,2,9,2767,2,5,4,1,1,2,3,61,3,1,2,2,1,1,1
+0,2,30,2,2,3441,2,3,2,2,2,4,3,21,1,1,1,3,1,1,1
+0,2,30,0,9,4280,2,3,4,2,1,4,3,26,3,1,2,2,1,1,1
+0,2,24,2,3,3092,2,2,3,4,1,2,3,22,3,1,1,3,1,2,1
+0,4,18,2,0,6761,5,3,2,3,1,4,3,68,2,1,2,3,1,1,1
+0,2,12,2,3,1331,1,2,2,3,1,1,3,22,3,2,1,3,1,1,1
+0,2,54,0,9,15945,1,2,3,3,1,4,4,58,3,1,1,3,1,2,1
+0,1,24,2,2,3234,1,2,4,2,1,4,1,23,3,1,1,2,1,2,1
+0,2,15,2,3,802,1,5,4,3,1,3,3,37,3,2,1,3,2,1,1
+0,2,48,2,2,9960,1,2,1,2,1,2,3,26,1,2,1,3,1,2,1
+0,4,24,3,9,8648,1,2,2,3,1,2,3,27,1,2,2,3,1,2,1
+0,1,18,2,3,1345,1,3,4,4,1,3,1,26,3,2,1,3,1,1,1
+0,1,45,2,3,1845,1,3,4,3,1,4,4,23,3,3,1,3,1,2,1
+0,1,21,1,0,1647,5,3,4,3,1,2,2,40,1,2,2,2,2,1,1
+0,4,48,2,9,4844,1,1,3,3,1,2,3,33,3,1,1,4,1,2,1
+0,2,27,0,9,8318,1,5,2,2,1,4,4,42,2,3,2,4,1,2,1
+0,3,18,2,3,2100,1,3,4,3,2,2,1,37,3,2,1,3,1,1,1
+0,1,45,0,9,11816,1,5,2,3,1,4,3,29,3,1,2,3,1,1,1
+0,1,6,2,6,448,1,2,4,2,1,4,2,23,3,2,1,3,1,1,1
+0,1,30,2,5,11998,1,2,1,1,1,1,4,34,1,2,1,2,1,2,1
+0,2,48,0,10,18424,1,3,1,2,1,2,2,32,1,2,1,4,1,2,2
+0,1,6,2,0,14896,1,5,1,3,1,4,4,68,1,2,1,4,1,2,1
+0,2,12,2,2,2762,5,5,1,2,1,2,2,25,3,2,1,3,1,2,1
+0,1,12,2,1,3386,1,5,3,3,1,4,4,35,3,3,1,3,1,2,1
+0,2,24,2,3,2039,1,2,1,4,1,1,2,22,3,2,1,3,1,2,1
+0,4,18,3,9,2169,1,3,4,4,1,2,3,28,3,2,1,3,1,2,1
+0,2,48,4,2,5096,1,3,2,2,1,3,3,30,3,2,1,4,1,2,1
+0,1,18,2,3,1882,1,3,4,2,1,4,3,25,1,1,2,3,1,1,1
+0,1,48,2,3,6999,1,4,1,4,3,1,1,34,3,2,2,3,1,2,1
+0,4,12,4,9,2292,1,1,4,3,1,2,3,42,2,2,2,4,1,2,1
+0,1,14,2,0,8978,1,5,1,1,1,4,2,45,3,2,1,4,1,2,2
+0,1,12,2,3,674,2,4,4,4,1,1,2,20,3,2,1,3,1,1,1
+0,1,18,2,0,976,1,2,1,2,1,2,3,23,3,2,1,2,1,1,1
+0,2,24,2,0,2718,1,3,3,2,1,4,2,20,3,1,1,2,1,2,1
+0,1,18,2,6,750,1,1,4,2,1,1,1,27,3,2,1,1,1,1,1
+0,2,24,2,1,12579,1,5,4,2,1,2,4,44,3,3,1,4,1,2,1
+0,1,18,2,1,7511,5,5,1,3,1,4,2,51,3,3,1,3,2,2,1
+0,1,18,4,0,3966,1,5,1,2,1,4,1,33,1,1,3,3,1,2,1
+0,1,12,0,3,6199,1,3,4,3,1,2,2,28,3,1,2,3,1,2,1
+0,1,24,2,3,1987,1,3,2,3,1,4,1,21,3,1,1,2,2,1,1
+0,1,24,2,0,2303,1,5,4,3,2,1,1,45,3,2,1,3,1,1,1
+0,4,21,4,0,12680,5,5,4,3,1,4,4,30,3,3,1,4,1,2,1
+0,2,12,2,3,6468,5,1,2,3,1,1,4,52,3,2,1,4,1,2,1
+0,1,30,2,2,6350,5,5,4,3,1,4,2,31,3,2,1,3,1,1,1

reports/metrics.json

@@ -0,0 +1,7 @@
+{
+  "accuracy": 0.71,
+  "precision": 0.7887,
+  "recall": 0.8,
+  "f1_score": 0.7943,
+  "roc_auc": 0.7533
+}

requirements.txt

@@ -0,0 +1,4 @@
+pandas==1.5.3
+plotly==5.14.1
+scikit-learn==1.1.2
+gradio==3.24.1

scripts/evaluate_model.py

@@ -0,0 +1,27 @@
+"""Quick report command for credit risk model metrics."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+METRICS_PATH = PROJECT_ROOT / "reports" / "metrics.json"
+
+
+def main() -> None:
+    """Print persisted metrics in a clean terminal format."""
+    if not METRICS_PATH.exists():
+        raise FileNotFoundError(
+            "Metrics file not found. Run `python scripts/train_model.py` first."
+        )
+
+    metrics = json.loads(METRICS_PATH.read_text(encoding="utf-8"))
+    print("Credit Risk Metrics")
+    for key, value in metrics.items():
+        print(f"- {key}: {value}")
+
+
+if __name__ == "__main__":
+    main()
+

scripts/train_model.py

@@ -0,0 +1,56 @@
+"""Train and persist the credit risk model and evaluation artifacts."""
+
+from __future__ import annotations
+
+import sys
+import pickle
+from pathlib import Path
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(PROJECT_ROOT / "src"))
+
+from credit_risk.config import DATA_PROCESSED_DIR, DATA_RAW_PATH, MODEL_DIR, REPORTS_DIR
+from credit_risk.features import build_training_frame
+from credit_risk.modeling import evaluate_model, save_metrics, save_model, train_model
+
+
+def main() -> None:
+    """Main training flow used by local runs and CI validations."""
+    raw_df = pd.read_csv(DATA_RAW_PATH)
+    features, target = build_training_frame(raw_df)
+
+    # Stratification preserves class balance between train and test sets.
+    x_train, x_test, y_train, y_test = train_test_split(
+        features,
+        target,
+        test_size=0.3,
+        random_state=42,
+        stratify=target,
+    )
+
+    model = train_model(x_train=x_train, y_train=y_train, random_state=42)
+    metrics, y_hat = evaluate_model(model=model, x_test=x_test, y_test=y_test)
+
+    DATA_PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
+    x_train.to_parquet(DATA_PROCESSED_DIR / "x_train.parquet", index=False)
+    x_test.to_parquet(DATA_PROCESSED_DIR / "x_test.parquet", index=False)
+    y_train.to_frame(name="target").to_parquet(DATA_PROCESSED_DIR / "y_train.parquet", index=False)
+    y_test.to_frame(name="target").to_parquet(DATA_PROCESSED_DIR / "y_test.parquet", index=False)
+    y_hat.to_frame(name="prediction").to_parquet(DATA_PROCESSED_DIR / "yhat.parquet", index=False)
+
+    save_model(model=model, model_path=MODEL_DIR / "model.joblib")
+    # Backward-compatible artifact used by the existing Gradio Space app.
+    with (MODEL_DIR / "model.pickle").open("wb") as file:
+        pickle.dump(model, file)
+    save_metrics(metrics=metrics, path=REPORTS_DIR / "metrics.json")
+
+    print("Training finished successfully.")
+    print(f"Saved model to: {MODEL_DIR / 'model.joblib'}")
+    print(f"Saved metrics to: {REPORTS_DIR / 'metrics.json'}")
+
+
+if __name__ == "__main__":
+    main()

src/credit_risk/__init__.py

@@ -0,0 +1,2 @@
+"""Core package for the credit risk classification project."""
+

src/credit_risk/app_support.py

@@ -0,0 +1,141 @@
+"""App-side helpers to load artifacts and build visual outputs."""
+
+from __future__ import annotations
+
+import json
+import pickle
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import joblib
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.metrics import confusion_matrix
+
+from credit_risk.config import MODEL_DIR, REPORTS_DIR, SELECTED_FEATURES
+
+
+@dataclass
+class AppArtifacts:
+    """Objects loaded once at startup to keep app latency low."""
+
+    model: Any
+    metrics: dict[str, float]
+    feature_importance_plot: go.Figure
+    confusion_matrix_plot: go.Figure
+
+
+def _load_model() -> Any:
+    """Load the most recent model artifact with backward-compatible fallback."""
+    joblib_path = MODEL_DIR / "model.joblib"
+    legacy_pickle_path = MODEL_DIR / "model.pickle"
+
+    if joblib_path.exists():
+        return joblib.load(joblib_path)
+
+    if legacy_pickle_path.exists():
+        with legacy_pickle_path.open("rb") as file:
+            return pickle.load(file)
+
+    raise FileNotFoundError(
+        "No model artifact found. Run `python scripts/train_model.py` first."
+    )
+
+
+def _load_metrics() -> dict[str, float]:
+    """Load cached metrics, or return an empty dict when not available."""
+    metrics_path = REPORTS_DIR / "metrics.json"
+    if not metrics_path.exists():
+        return {}
+    return json.loads(metrics_path.read_text(encoding="utf-8"))
+
+
+def _load_test_outputs() -> tuple[pd.Series | None, pd.Series | None]:
+    """Load y_test and yhat predictions used to generate confusion matrix."""
+    y_test_path = Path("data") / "processed" / "y_test.parquet"
+    y_hat_path = Path("data") / "processed" / "yhat.parquet"
+
+    if not y_test_path.exists() or not y_hat_path.exists():
+        return None, None
+
+    y_test = pd.read_parquet(y_test_path).squeeze()
+    y_hat = pd.read_parquet(y_hat_path).squeeze()
+    return y_test, y_hat
+
+
+def _build_feature_importance_plot(model: Any) -> go.Figure:
+    """Build a robust plot even when the estimator has no feature_importances_."""
+    if hasattr(model, "feature_importances_"):
+        importances = pd.Series(model.feature_importances_, index=SELECTED_FEATURES)
+        data = (
+            importances.sort_values(ascending=False)
+            .rename_axis("feature")
+            .reset_index(name="importance")
+        )
+        return px.bar(
+            data,
+            x="feature",
+            y="importance",
+            title="Feature Importance",
+            labels={"feature": "Feature", "importance": "Importance"},
+        )
+
+    return go.Figure(
+        layout={
+            "title": "Feature importance is not available for this model type.",
+            "xaxis_title": "Feature",
+            "yaxis_title": "Importance",
+        }
+    )
+
+
+def _build_confusion_matrix_plot(y_test: pd.Series | None, y_hat: pd.Series | None) -> go.Figure:
+    """Build confusion matrix from cached test predictions."""
+    if y_test is None or y_hat is None:
+        return go.Figure(
+            layout={
+                "title": "Confusion matrix not available yet. Run training script first.",
+                "xaxis_title": "Predicted",
+                "yaxis_title": "Actual",
+            }
+        )
+
+    matrix = confusion_matrix(y_test, y_hat)
+    return px.imshow(
+        matrix,
+        x=["Predicted 0", "Predicted 1"],
+        y=["Actual 0", "Actual 1"],
+        color_continuous_scale="Blues",
+        text_auto=True,
+        labels={"x": "Predicted", "y": "Actual", "color": "Count"},
+        title="Confusion Matrix",
+    )
+
+
+def format_metrics_markdown(metrics: dict[str, float]) -> str:
+    """Render metrics consistently in the UI."""
+    if not metrics:
+        return "Metrics not available. Run `python scripts/train_model.py` first."
+
+    lines = ["### Model Metrics"]
+    for key, value in metrics.items():
+        metric_name = key.replace("_", " ").title()
+        lines.append(f"- **{metric_name}:** {value:.4f}")
+    return "\n".join(lines)
+
+
+def load_artifacts() -> AppArtifacts:
+    """Entry point used by the app to pre-load model and visual assets once."""
+    model = _load_model()
+    metrics = _load_metrics()
+    y_test, y_hat = _load_test_outputs()
+
+    return AppArtifacts(
+        model=model,
+        metrics=metrics,
+        feature_importance_plot=_build_feature_importance_plot(model),
+        confusion_matrix_plot=_build_confusion_matrix_plot(y_test, y_hat),
+    )
+

src/credit_risk/config.py

@@ -0,0 +1,142 @@
+"""Project-wide constants and feature configuration for credit risk."""
+
+from dataclasses import dataclass
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+DATA_RAW_PATH = PROJECT_ROOT / "data" / "raw" / "german_credit.csv"
+DATA_PROCESSED_DIR = PROJECT_ROOT / "data" / "processed"
+MODEL_DIR = PROJECT_ROOT / "model"
+REPORTS_DIR = PROJECT_ROOT / "reports"
+
+TARGET_COLUMN = "Creditability"
+NOT_SELECTED_LABEL = "Not selected"
+
+
+@dataclass(frozen=True)
+class FeatureOption:
+    """Single UI option and its one-hot encoded destination column."""
+
+    label: str
+    column: str
+
+
+@dataclass(frozen=True)
+class FeatureGroup:
+    """Feature group used in the app and preprocessing logic."""
+
+    name: str
+    source_column: str
+    options: tuple[FeatureOption, ...]
+
+    @property
+    def labels(self) -> list[str]:
+        return [option.label for option in self.options]
+
+    def column_from_label(self, label: str | None) -> str | None:
+        if label is None:
+            return None
+        for option in self.options:
+            if option.label == label:
+                return option.column
+        return None
+
+
+# This list defines both the app controls and the final model input schema.
+FEATURE_GROUPS: tuple[FeatureGroup, ...] = (
+    FeatureGroup(
+        name="Account Balance",
+        source_column="Account Balance",
+        options=(
+            FeatureOption("No account", "Account Balance_1"),
+            FeatureOption("No balance", "Account Balance_2"),
+            FeatureOption("Some balance", "Account Balance_3"),
+        ),
+    ),
+    FeatureGroup(
+        name="Payment Status of Previous Credit",
+        source_column="Payment Status of Previous Credit",
+        options=(
+            FeatureOption("Some problems", "Payment Status of Previous Credit_1"),
+            FeatureOption("No problems in this bank", "Payment Status of Previous Credit_3"),
+        ),
+    ),
+    FeatureGroup(
+        name="Purpose",
+        source_column="Purpose",
+        options=(
+            FeatureOption("New car", "Purpose_1"),
+            FeatureOption("Other", "Purpose_4"),
+        ),
+    ),
+    FeatureGroup(
+        name="Value Savings/Stocks",
+        source_column="Value Savings/Stocks",
+        options=(
+            FeatureOption("No savings", "Value Savings/Stocks_1"),
+            FeatureOption("DM between [100, 1000]", "Value Savings/Stocks_3"),
+            FeatureOption("DM >= 1000", "Value Savings/Stocks_5"),
+        ),
+    ),
+    FeatureGroup(
+        name="Length of Current Employment",
+        source_column="Length of current employment",
+        options=(
+            FeatureOption("Below 1 year (or unemployed)", "Length of current employment_1"),
+            FeatureOption("Between 4 and 7 years", "Length of current employment_4"),
+        ),
+    ),
+    FeatureGroup(
+        name="Instalment Per Cent",
+        source_column="Instalment per cent",
+        options=(FeatureOption("Smaller than 20%", "Instalment per cent_4"),),
+    ),
+    FeatureGroup(
+        name="Guarantors",
+        source_column="Guarantors",
+        options=(FeatureOption("No guarantors", "Guarantors_1"),),
+    ),
+    FeatureGroup(
+        name="Duration in Current Address",
+        source_column="Duration in Current address",
+        options=(
+            FeatureOption("Less than a year", "Duration in Current address_1"),
+            FeatureOption("Between 1 and 4 years", "Duration in Current address_2"),
+        ),
+    ),
+    FeatureGroup(
+        name="Most Valuable Available Asset",
+        source_column="Most valuable available asset",
+        options=(
+            FeatureOption("Not available / no assets", "Most valuable available asset_1"),
+            FeatureOption("Ownership of house or land", "Most valuable available asset_4"),
+        ),
+    ),
+    FeatureGroup(
+        name="Concurrent Credits",
+        source_column="Concurrent Credits",
+        options=(FeatureOption("No further running credits", "Concurrent Credits_3"),),
+    ),
+    FeatureGroup(
+        name="Type of Apartment",
+        source_column="Type of apartment",
+        options=(FeatureOption("Free apartment", "Type of apartment_1"),),
+    ),
+    FeatureGroup(
+        name="Number of Credits at this Bank",
+        source_column="No of Credits at this Bank",
+        options=(FeatureOption("One credit", "No of Credits at this Bank_1"),),
+    ),
+    FeatureGroup(
+        name="Occupation",
+        source_column="Occupation",
+        options=(FeatureOption("Unemployed or unskilled with no permanent", "Occupation_1"),),
+    ),
+)
+
+
+# Keep this explicit list to guarantee deterministic input order for training/inference.
+SELECTED_FEATURES: list[str] = [
+    option.column for group in FEATURE_GROUPS for option in group.options
+]
+

src/credit_risk/features.py

@@ -0,0 +1,53 @@
+"""Feature preparation utilities for training and app inference."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from credit_risk.config import FEATURE_GROUPS, SELECTED_FEATURES, TARGET_COLUMN
+
+
+def _validate_raw_columns(df: pd.DataFrame) -> None:
+    """Fail fast when required raw columns are missing."""
+    required_columns = {TARGET_COLUMN, *[group.source_column for group in FEATURE_GROUPS]}
+    missing = sorted(required_columns.difference(df.columns))
+    if missing:
+        raise ValueError(f"Missing required columns in raw dataset: {missing}")
+
+
+def build_training_frame(df: pd.DataFrame) -> tuple[pd.DataFrame, pd.Series]:
+    """
+    Build the model matrix from the raw CSV.
+
+    Notes:
+    - Uses one-hot encoding only on the columns represented in FEATURE_GROUPS.
+    - Reindexes to SELECTED_FEATURES so train/inference always match column order.
+    """
+    _validate_raw_columns(df)
+
+    categorical_columns = [group.source_column for group in FEATURE_GROUPS]
+    encoded = pd.get_dummies(df[categorical_columns], columns=categorical_columns, dtype="int64")
+
+    # Guarantee all model columns exist even if a category is absent in the current dataset.
+    feature_frame = encoded.reindex(columns=SELECTED_FEATURES, fill_value=0).astype("int64")
+    target = df[TARGET_COLUMN].astype("int64")
+    return feature_frame, target
+
+
+def build_inference_frame(selection_by_group: dict[str, str | None]) -> pd.DataFrame:
+    """
+    Convert app selections to a one-row DataFrame matching model schema.
+
+    The dict format is:
+      {"Account Balance": "No account", "Purpose": None, ...}
+    """
+    values = {column: 0 for column in SELECTED_FEATURES}
+
+    for group in FEATURE_GROUPS:
+        selected_label = selection_by_group.get(group.name)
+        selected_column = group.column_from_label(selected_label)
+        if selected_column is not None:
+            values[selected_column] = 1
+
+    return pd.DataFrame([values], columns=SELECTED_FEATURES, dtype="int64")
+

src/credit_risk/modeling.py

@@ -0,0 +1,55 @@
+"""Model training and evaluation helpers for credit risk classification."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import joblib
+import pandas as pd
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score
+from sklearn.tree import DecisionTreeClassifier
+
+
+def train_model(x_train: pd.DataFrame, y_train: pd.Series, random_state: int = 42) -> DecisionTreeClassifier:
+    """
+    Train a Decision Tree classifier.
+
+    A tree model keeps parity with the previous project behavior while
+    remaining simple to interpret in the app via feature importances.
+    """
+    model = DecisionTreeClassifier(max_depth=6, min_samples_leaf=12, random_state=random_state)
+    model.fit(x_train, y_train)
+    return model
+
+
+def evaluate_model(model: DecisionTreeClassifier, x_test: pd.DataFrame, y_test: pd.Series) -> tuple[dict[str, float], pd.Series]:
+    """Return classification metrics and test-set predictions."""
+    y_pred = pd.Series(model.predict(x_test), index=y_test.index, name="prediction")
+
+    metrics = {
+        "accuracy": round(float(accuracy_score(y_test, y_pred)), 4),
+        "precision": round(float(precision_score(y_test, y_pred, zero_division=0)), 4),
+        "recall": round(float(recall_score(y_test, y_pred, zero_division=0)), 4),
+        "f1_score": round(float(f1_score(y_test, y_pred, zero_division=0)), 4),
+    }
+
+    # ROC-AUC is only available when the model exposes class probabilities.
+    if hasattr(model, "predict_proba"):
+        y_score = model.predict_proba(x_test)[:, 1]
+        metrics["roc_auc"] = round(float(roc_auc_score(y_test, y_score)), 4)
+
+    return metrics, y_pred
+
+
+def save_model(model: DecisionTreeClassifier, model_path: Path) -> None:
+    """Persist model as a joblib artifact."""
+    model_path.parent.mkdir(parents=True, exist_ok=True)
+    joblib.dump(model, model_path)
+
+
+def save_metrics(metrics: dict[str, float], path: Path) -> None:
+    """Persist metrics in JSON for app visualization and reproducibility."""
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(metrics, indent=2), encoding="utf-8")
+