


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

import sentencepiece as spm

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


lowerCAmelCase__  :Union[str, Any]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__  :Dict             =							{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__  :List[Any]             =							{'''bert_for_seq_generation''': 5_1_2}

class 		__a				(    UpperCAmelCase      ):
		_a     :  str           =     VOCAB_FILES_NAMES
		_a     :  Tuple           =     PRETRAINED_VOCAB_FILES_MAP
		_a     :  str           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     :  List[int]           =     []
		_a     :  Optional[int]           =     ['input_ids', 'attention_mask']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE="<s>"      ,   _SCREAMING_SNAKE_CASE="</s>"      ,   _SCREAMING_SNAKE_CASE="<unk>"      ,   _SCREAMING_SNAKE_CASE="<pad>"      ,   _SCREAMING_SNAKE_CASE="<::::>"      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									_UpperCAmelCase					=      {} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=_SCREAMING_SNAKE_CASE      ,   eos_token=_SCREAMING_SNAKE_CASE      ,   unk_token=_SCREAMING_SNAKE_CASE      ,   pad_token=_SCREAMING_SNAKE_CASE      ,   sep_token=_SCREAMING_SNAKE_CASE      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **_SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      vocab_file

									_UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(_SCREAMING_SNAKE_CASE						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									return self.sp_model.get_piece_size()
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
									vocab.update(self.added_tokens_encoder						)
									return vocab
		def __getstate__(      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.__dict__.copy()
									_UpperCAmelCase					=      None
									return state
		def __setstate__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      d

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

									_UpperCAmelCase					=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(self.vocab_file						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,   out_type=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE						)
									return token
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      ''
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						) + token
																							_UpperCAmelCase					=      []
																else:
																							current_sub_tokens.append(_SCREAMING_SNAKE_CASE						)
									out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						)
									return out_string.strip()






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	Tuple[str]:




									"""simple docstring"""





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

									if os.path.abspath(self.vocab_file						) != os.path.abspath(_SCREAMING_SNAKE_CASE						) and os.path.isfile(self.vocab_file						):
																copyfile(self.vocab_file      ,   _SCREAMING_SNAKE_CASE						)
									elif not os.path.isfile(self.vocab_file						):
																with open(_SCREAMING_SNAKE_CASE      ,   'wb'						) as fi:
																							_UpperCAmelCase					=      self.sp_model.serialized_model_proto()
																							fi.write(_SCREAMING_SNAKE_CASE						)

									return (out_vocab_file,)




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
