import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={'''vocab_file''': '''vocab.json'''}

__magic_name__								={
    '''vocab_file''': {
        '''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
    }
}

__magic_name__								={'''mgp-str''': 27}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	Tuple						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[str]						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="[GO]"   ,				SCREAMING_SNAKE_CASE_="[GO]"   ,				SCREAMING_SNAKE_CASE_="[s]"   ,				SCREAMING_SNAKE_CASE_="[GO]"   ,				**SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       super().__init__(
           unk_token=SCREAMING_SNAKE_CASE_   ,				bos_token=SCREAMING_SNAKE_CASE_   ,				eos_token=SCREAMING_SNAKE_CASE_   ,				pad_token=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)

       with open(SCREAMING_SNAKE_CASE_   ,				encoding='''utf-8'''	) as vocab_handle:
            UpperCamelCase__										=      json.load(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {v: k for k, v in self.vocab.items()}






  @property
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       return len(self.vocab	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       return dict(self.vocab   ,				**self.added_tokens_encoder	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      []
       for s in text:
            char_tokens.extend(SCREAMING_SNAKE_CASE_	)
       return char_tokens






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       return self.vocab.get(SCREAMING_SNAKE_CASE_   ,				self.vocab.get(self.unk_token	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       return self.decoder.get(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





       '''simple docstring'''





       if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE_	)	)
            return
       UpperCamelCase__										=      os.path.join(
           SCREAMING_SNAKE_CASE_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

       with open(SCREAMING_SNAKE_CASE_   ,				'''w'''   ,				encoding='''utf-8'''	) as f:
            f.write(json.dumps(self.vocab   ,				indent=2   ,				sort_keys=SCREAMING_SNAKE_CASE_   ,				ensure_ascii=SCREAMING_SNAKE_CASE_	) + '''\n'''	)

       return (vocab_file,)




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

     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])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



