

"""simple docstring"""





import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
   _a							=							pd.read_csv('sample_data.csv', header=None)
   _a							=							df.shape[:1][0]
   # If you're using some other dataset input the target column
   _a							=							df.iloc[:, 1:2]
   _a							=							actual_data.values.reshape(len_data, 1)
   _a							=							MinMaxScaler().fit_transform(actual_data)
   _a							=							10
   _a							=							5
   _a							=							20
   _a							=							len_data - periods * look_back
   _a							=							actual_data[:division]
   _a							=							actual_data[division - look_back :]
   _a				,						_a							=							[], []
   _a				,						_a							=							[], []

   for i in range(0, len(train_data) - forward_days - look_back + 1):
      train_x.append(train_data[i : i + look_back])
      train_y.append(train_data[i + look_back : i + look_back + forward_days])
   for i in range(0, len(test_data) - forward_days - look_back + 1):
      test_x.append(test_data[i : i + look_back])
      test_y.append(test_data[i + look_back : i + look_back + forward_days])
   _a							=							np.array(train_x)
   _a							=							np.array(test_x)
   _a							=							np.array([list(i.ravel()) for i in train_y])
   _a							=							np.array([list(i.ravel()) for i in test_y])

   _a							=							Sequential()
   model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
   model.add(LSTM(64, input_shape=(1_28, 1)))
   model.add(Dense(forward_days))
   model.compile(loss='mean_squared_error', optimizer='adam')
   _a							=							model.fit(
       x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
   )
   _a							=							model.predict(x_test)







"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters




def   _A  (  )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   __lowercase								=						Dataset.from_dict(UpperCamelCase_)
   return dataset




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      int     ):
    __lowercase								=						get_dataset()
    __lowercase								=						make_duplicate_clusters(UpperCAmelCase__, 0.85     )
    self.assertEqual(len(duplicate_clusters[0]     ), 2     )



 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						get_dataset()
    __lowercase		,__lowercase								=						deduplicate_dataset(UpperCAmelCase__     )
    self.assertEqual(len(UpperCAmelCase__     ), 2     )
    print(UpperCAmelCase__     )
    self.assertEqual(duplicate_clusters[0][0]["copies"], 2     )
    self.assertEqual(duplicate_clusters[0][0]["is_extreme"], UpperCAmelCase__     )




