
"""simple docstring"""






import os
import re
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


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				{'''vocab_file''': '''spiece.model'''}

_lowercase							=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

_lowercase							=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			List[Any]          =   ["""input_ids""", """attention_mask"""]
							_lowerCamelCase					:			List[int]          =   []
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="[SEP]"  ,    _lowerCamelCase="[MASK]"  ,    _lowerCamelCase="[CLS]"  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							None:




										'''simple docstring'''
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else bos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else eos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else unk_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else pad_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else cls_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else mask_token

										__lowercase       =       {} if sp_model_kwargs is None else sp_model_kwargs

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

										__lowercase       =       vocab_file

										__lowercase       =       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(_lowerCamelCase       )
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										return self.sp_model.get_piece_size()
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										__lowercase       =       {self.convert_ids_to_tokens(_lowerCamelCase       ): i for i in range(self.vocab_size       )}
										vocab.update(self.added_tokens_encoder       )
										return vocab
							def __getstate__(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       self.__dict__.copy()
										__lowercase       =       None
										return state
							def __setstate__(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       d

										# for backward compatibility
										if not hasattr(self  ,    "sp_model_kwargs"       ):
													__lowercase       =       {}

										__lowercase       =       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(self.vocab_file       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										return self.sp_model.encode(_lowerCamelCase  ,    out_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Any:




										'''simple docstring'''
										return self.sp_model.piece_to_id(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       self.sp_model.IdToPiece(_lowerCamelCase       )
										return token
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       []
										__lowercase       =       ""
										__lowercase       =       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       =       True
																__lowercase       =       []
													else:
																current_sub_tokens.append(_lowerCamelCase       )
																__lowercase       =       False
										out_string += self.sp_model.decode(_lowerCamelCase       )
										return out_string.strip()
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    _lowerCamelCase = True  ,    **_lowerCamelCase  ,    )			->							str:




										'''simple docstring'''
										__lowercase       =       kwargs.pop("use_source_tokenizer"  ,    _lowerCamelCase       )

										__lowercase       =       self.convert_ids_to_tokens(_lowerCamelCase  ,    skip_special_tokens=_lowerCamelCase       )

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										__lowercase       =       []
										__lowercase       =       []
										for token in filtered_tokens:
													if skip_special_tokens and token in self.all_special_ids:
																continue
													if token in self.added_tokens_encoder:
																if current_sub_text:
																			sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase       )       )
																			__lowercase       =       []
																sub_texts.append(_lowerCamelCase       )
													else:
																current_sub_text.append(_lowerCamelCase       )
										if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase       )       )

										# Mimic the behavior of the Rust tokenizer:
										# No space before [MASK] and [SEP]
										if spaces_between_special_tokens:
													__lowercase       =       re.sub(R" (\[(MASK|SEP)\])"  ,    R"\1"  ,    " ".join(_lowerCamelCase       )       )
										else:
													__lowercase       =       "".join(_lowerCamelCase       )

										__lowercase       =       (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
													__lowercase       =       self.clean_up_tokenization(_lowerCamelCase       )
													return clean_text
										else:
													return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       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       =       self.sp_model.serialized_model_proto()
																fi.write(_lowerCamelCase       )

										return (out_vocab_file,)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]
										__lowercase       =       [self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[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] + ([0] * len(_lowerCamelCase       )) + [1]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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       ) * [0] + len(token_ids_a + sep       ) * [1]







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

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

					_lowercase							=				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''')
					_lowercase							=				model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					_lowercase							=				model.predict(x_test)





