




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__":
       lowerCamelCase	:	Tuple				=   pd.read_csv('sample_data.csv', header=None)
       lowerCamelCase	:	str				=   df.shape[:1][0]
       # If you're using some other dataset input the target column
       lowerCamelCase	:	Optional[int]				=   df.iloc[:, 1:2]
       lowerCamelCase	:	Optional[Any]				=   actual_data.values.reshape(len_data, 1)
       lowerCamelCase	:	Union[str, Any]				=   MinMaxScaler().fit_transform(actual_data)
       lowerCamelCase	:	Dict				=   1_0
       lowerCamelCase	:	Optional[int]				=   5
       lowerCamelCase	:	int				=   2_0
       lowerCamelCase	:	List[Any]				=   len_data - periods * look_back
       lowerCamelCase	:	Any				=   actual_data[:division]
       lowerCamelCase	:	List[Any]				=   actual_data[division - look_back :]
       lowerCamelCase	,       lowerCamelCase	:	Dict				=   [], []
       lowerCamelCase	,       lowerCamelCase	:	Any				=   [], []

       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])
       lowerCamelCase	:	Optional[int]				=   np.array(train_x)
       lowerCamelCase	:	str				=   np.array(test_x)
       lowerCamelCase	:	Tuple				=   np.array([list(i.ravel()) for i in train_y])
       lowerCamelCase	:	Any				=   np.array([list(i.ravel()) for i in test_y])

       lowerCamelCase	:	List[str]				=   Sequential()
       model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
       model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
       model.add(Dense(forward_days))
       model.compile(loss='mean_squared_error', optimizer='adam')
       lowerCamelCase	:	Union[str, Any]				=   model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       lowerCamelCase	:	List[str]				=   model.predict(x_test)











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





