





"""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__":
   lowerCamelCase_     :						Tuple						=					pd.read_csv('sample_data.csv', header=None)
   lowerCamelCase_     :						int						=					df.shape[:1][0]
   # If you're using some other dataset input the target column
   lowerCamelCase_     :						Union[str, Any]						=					df.iloc[:, 1:2]
   lowerCamelCase_     :						Dict						=					actual_data.values.reshape(len_data, 1)
   lowerCamelCase_     :						int						=					MinMaxScaler().fit_transform(actual_data)
   lowerCamelCase_     :						Dict						=					10
   lowerCamelCase_     :						Tuple						=					5
   lowerCamelCase_     :						Any						=					20
   lowerCamelCase_     :						Dict						=					len_data - periods * look_back
   lowerCamelCase_     :						Optional[int]						=					actual_data[:division]
   lowerCamelCase_     :						Union[str, Any]						=					actual_data[division - look_back :]
   lowerCamelCase_					,					lowerCamelCase_     :						Dict						=					[], []
   lowerCamelCase_					,					lowerCamelCase_     :						Union[str, 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[Any]						=					np.array(train_x)
   lowerCamelCase_     :						Union[str, Any]						=					np.array(test_x)
   lowerCamelCase_     :						Dict						=					np.array([list(i.ravel()) for i in train_y])
   lowerCamelCase_     :						List[Any]						=					np.array([list(i.ravel()) for i in test_y])

   lowerCamelCase_     :						List[str]						=					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')
   lowerCamelCase_     :						Dict						=					model.fit(
       x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
   )
   lowerCamelCase_     :						Any						=					model.predict(x_test)






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )