






"""simple docstring"""







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
				from ...tokenization_utils_base import TextInput
from ...utils import logging


_lowerCAmelCase            =	logging.get_logger(__name__)

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

_lowerCAmelCase            =	{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase            =	{
    't5-small': 5_12,
    't5-base': 5_12,
    't5-large': 5_12,
    't5-3b': 5_12,
    't5-11b': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : List[str]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Dict												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Any												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Any												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :Tuple			, __magic_name__  :Optional[int]			, __magic_name__  :List[Any]="</s>"			, __magic_name__  :List[str]="<unk>"			, __magic_name__  :Any="<pad>"			, __magic_name__  :Any=100			, __magic_name__  :Tuple=None			, __magic_name__  :Optional[Dict[str, Any]] = None			, __magic_name__  :Tuple=True			, **__magic_name__  :str			, )      ->None:
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
														lowercase    :      int       = [f"""<extra_id_{i}>""" for i in range(__magic_name__				)]
										elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra_id special tokens
														lowercase    :      Any       = len(set(filter(lambda __magic_name__				: bool("""extra_id""" in str(__magic_name__				)				)			, __magic_name__				)				)				)
														if extra_tokens != extra_ids:
																		raise ValueError(
																		    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																		    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																		    """ tokens"""				)
										if legacy:
														logger.warning_once(
														    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
														    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""				)

										lowercase    :      Any       = legacy
										lowercase    :      int       = {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=__magic_name__			, unk_token=__magic_name__			, pad_token=__magic_name__			, extra_ids=__magic_name__			, additional_special_tokens=__magic_name__			, sp_model_kwargs=self.sp_model_kwargs			, legacy=__magic_name__			, **__magic_name__			, )

										lowercase    :      Optional[int]       = vocab_file
										lowercase    :      Union[str, Any]       = extra_ids

										lowercase    :      List[str]       = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(__magic_name__				)






						@staticmethod
						def      __snake_case      (		__magic_name__  :List[Any]			, __magic_name__  :str			, __magic_name__  :Optional[int]				)      ->Optional[Any]:
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														lowercase    :      Optional[int]       = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																		return init_max_model_length
														elif init_max_model_length is None:
																		warnings.warn(
																		    """This tokenizer was incorrectly instantiated with a model max length of"""
																		    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																		    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																		    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																		    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																		    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																		    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																		    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																		    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""			, __magic_name__			, )

										return max_model_length






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										return self.sp_model.get_piece_size() + self._extra_ids






						def      __snake_case      (		self  :Tuple				)      ->Dict:
										lowercase    :      List[str]       = {self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None			, __magic_name__  :bool = False				)      ->List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__magic_name__			, token_ids_a=__magic_name__			, already_has_special_tokens=__magic_name__				)

										# normal case: some special tokens
										if token_ids_a is None:
														return ([0] * len(__magic_name__				)) + [1]
										return ([0] * len(__magic_name__				)) + [1] + ([0] * len(__magic_name__				)) + [1]






						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										return list(
										    set(filter(lambda __magic_name__				: bool(re.search(r"""<extra_id_\d+>"""			, __magic_name__				)				) is not None			, self.additional_special_tokens				)				)				)






						def      __snake_case      (		self  :Optional[int]				)      ->Tuple:
										return [self._convert_token_to_id(__magic_name__				) for token in self.get_sentinel_tokens()]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[int]				)      ->List[int]:
										if len(__magic_name__				) > 0 and token_ids[-1] == self.eos_token_id:
														warnings.warn(
														    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
														    """ eos tokens being added."""				)
														return token_ids
										else:
														return token_ids + [self.eos_token_id]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Dict       = [self.eos_token_id]

										if token_ids_a is None:
														return len(token_ids_a + eos				) * [0]
										return len(token_ids_a + eos + token_ids_a + eos				) * [0]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[Any]       = self._add_eos_if_not_present(__magic_name__				)
										if token_ids_a is None:
														return token_ids_a
										else:
														lowercase    :      Union[str, Any]       = self._add_eos_if_not_present(__magic_name__				)
														return token_ids_a + token_ids_a






						def __getstate__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Any       = self.__dict__.copy()
										lowercase    :      Optional[Any]       = None
										return state






						def __setstate__(		self  :Dict			, __magic_name__  :Optional[int]				)      ->Tuple:
										lowercase    :      Union[str, Any]       = d

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

										lowercase    :      List[str]       = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(self.vocab_file				)






						def      __snake_case      (		self  :str			, __magic_name__  :"TextInput"			, **__magic_name__  :Dict				)      ->List[str]:
										# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
										# the beginning of the text
										if not self.legacy:
														lowercase    :      Dict       = SPIECE_UNDERLINE + text.replace(__magic_name__			, """ """				)
										return super().tokenize(__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Optional[Any]			, **__magic_name__  :str				)      ->List[str]:
										if not self.legacy:
														lowercase    :      Optional[int]       = text.startswith(__magic_name__				)
														if is_first:
																		lowercase    :      str       = text[1:]

										lowercase    :      Optional[int]       = self.sp_model.encode(__magic_name__			, out_type=__magic_name__				)

										if not self.legacy and not is_first and not text.startswith(""" """				) and tokens[0].startswith(__magic_name__				):
														lowercase    :      Optional[int]       = ([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
										return tokens






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :Tuple				)      ->Any:
										if token.startswith("""<extra_id_"""				):
														lowercase    :      str       = re.match(r"""<extra_id_(\d+)>"""			, __magic_name__				)
														lowercase    :      Union[str, Any]       = int(match.group(1				)				)
														return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(__magic_name__				)






						def      __snake_case      (		self  :str			, __magic_name__  :str				)      ->Optional[Any]:
										if index < self.sp_model.get_piece_size():
														lowercase    :      Any       = self.sp_model.IdToPiece(__magic_name__				)
										else:
														lowercase    :      Optional[Any]       = f"""<extra_id_{self.vocab_size - 1 - index}>"""
										return token






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Union[str, Any]				)      ->Any:
										lowercase    :      Optional[Any]       = []
										lowercase    :      List[str]       = """"""
										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(__magic_name__				) + token
																		lowercase    :      List[Any]       = True
																		lowercase    :      int       = []
														else:
																		current_sub_tokens.append(__magic_name__				)
																		lowercase    :      Optional[Any]       = False
										out_string += self.sp_model.decode(__magic_name__				)
										return out_string.strip()






						def      __snake_case      (		self  :List[str]			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      List[Any]       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
														copyfile(self.vocab_file			, __magic_name__				)
										elif not os.path.isfile(self.vocab_file				):
														with open(__magic_name__			, """wb"""				) as fi:
																		lowercase    :      int       = self.sp_model.serialized_model_proto()
																		fi.write(__magic_name__				)

										return (out_vocab_file,)








"""simple docstring"""







import math
import random


def      UpperCamelCase       ( _A  ,				_A = False )     ->       float:
				if deriv:
								return value * (1 - value)
				return 1 / (1 + math.exp(-value ))


# Initial Value
_lowerCAmelCase            =	0.02


def      UpperCamelCase       ( _A  ,				_A )     ->       float:
				lowercase    :      Any       = float(2 * (random.randint(1  ,				100 )) - 1 )

				for _ in range(_A ):
								# Forward propagation
								lowercase    :      Dict       = sigmoid_function(INITIAL_VALUE * weight )
								# How much did we miss?
								lowercase    :      Any       = (expected / 100) - layer_a
								# Error delta
								lowercase    :      Dict       = layer_1_error * sigmoid_function(_A  ,				_A )
								# Update weight
								weight += INITIAL_VALUE * layer_1_delta

				return layer_a * 100


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	int(input('Expected value: '))
				_lowerCAmelCase            =	int(input('Number of propagations: '))
				print(forward_propagation(expected, number_propagations))
