
"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__ :       List[str]				= logging.get_logger(__name__)

A__ :       str				= {"""vocab_file""": """sentencepiece.bpe.model"""}

A__ :       Dict				= {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

A__ :       Tuple				= {
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

A__ :       Optional[int]				= """▁"""


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		VOCAB_FILES_NAMES
       UpperCamelCase_ =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_ =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_ =		['input_ids', 'attention_mask']
       def __init__(  self			,					A_			,					A_="<s>"			,					A_="</s>"			,					A_="</s>"			,					A_="<s>"			,					A_="<unk>"			,					A_="<pad>"			,					A_="<mask>"			,					A_ = None			,					**A_			,					)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  AddedToken(__lowerCAmelCase			,					lstrip=__lowerCAmelCase			,					rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase			,					__lowerCAmelCase							) else mask_token

         _lowercase:      Tuple   			=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__lowerCAmelCase			,					eos_token=__lowerCAmelCase			,					unk_token=__lowerCAmelCase			,					sep_token=__lowerCAmelCase			,					cls_token=__lowerCAmelCase			,					pad_token=__lowerCAmelCase			,					mask_token=__lowerCAmelCase			,					sp_model_kwargs=self.sp_model_kwargs			,					**__lowerCAmelCase			,					)

         _lowercase:      Tuple   			=  vocab_file
         _lowercase:      Union[str, Any]   			=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(__lowerCAmelCase							)							)

         _lowercase:      List[Any]   			=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         _lowercase:      Optional[int]   			=  len(self.sp_model							) - 1
         _lowercase:      int   			=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Tuple:





         """simple docstring"""




         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowercase:      Any   			=  [self.cls_token_id]
         _lowercase:      Dict   			=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = False							)  ->						Optional[int]:





         """simple docstring"""




         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowerCAmelCase			,					token_ids_a=__lowerCAmelCase			,					already_has_special_tokens=__lowerCAmelCase							)

         if token_ids_a is None:
           return [1] + ([0] * len(__lowerCAmelCase							)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase							)) + [1, 1] + ([0] * len(__lowerCAmelCase							)) + [1]
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						int:





         """simple docstring"""




         _lowercase:      str   			=  [self.sep_token_id]
         _lowercase:      List[Any]   			=  [self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]
       @property
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return len(self.sp_model							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Dict   			=  {self.convert_ids_to_tokens(__lowerCAmelCase							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         return self.sp_model.encode(__lowerCAmelCase			,					out_type=__lowerCAmelCase							)
       def 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         _lowercase:      Optional[int]   			=  self.sp_model.PieceToId(__lowerCAmelCase							)

         return spm_id if spm_id else self.unk_token_id
       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(__lowerCAmelCase							)
       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Tuple   			=  []
         _lowercase:      Union[str, Any]   			=  ''''''
         _lowercase:      Optional[Any]   			=  False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(__lowerCAmelCase							) + token
             _lowercase:      int   			=  True
             _lowercase:      Union[str, Any]   			=  []
           else:
             current_sub_tokens.append(__lowerCAmelCase							)
             _lowercase:      List[Any]   			=  False
         out_string += self.sp_model.decode(__lowerCAmelCase							)
         return out_string.strip()
       def __getstate__(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.__dict__.copy()
         _lowercase:      str   			=  None
         return state
       def __setstate__(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  d

         # for backward compatibility
         if not hasattr(self			,					'''sp_model_kwargs'''							):
           _lowercase:      Optional[Any]   			=  {}

         _lowercase:      Any   			=  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(self.vocab_file							)



       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						Optional[Any]:





         """simple docstring"""




         if not os.path.isdir(__lowerCAmelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         _lowercase:      Dict   			=  os.path.join(
             __lowerCAmelCase			,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCAmelCase							) and os.path.isfile(self.vocab_file							):
           copyfile(self.vocab_file			,					__lowerCAmelCase							)
         elif not os.path.isfile(self.vocab_file							):
           with open(__lowerCAmelCase			,					'''wb'''							) as fi:
             _lowercase:      List[Any]   			=  self.sp_model.serialized_model_proto()
             fi.write(__lowerCAmelCase							)

         return (out_vocab_file,)

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

       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       :			int   	=np.array(train_x)
       __lowerCAmelCase       :			List[str]   	=np.array(test_x)
       __lowerCAmelCase       :			Dict   	=np.array([list(i.ravel()) for i in train_y])
       __lowerCAmelCase       :			str   	=np.array([list(i.ravel()) for i in test_y])

       __lowerCAmelCase       :			Optional[Any]   	=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       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



