




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


a__		     =     '''▁'''

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

a__		     =     {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

a__		     =     {
    '''google/pegasus-xsum''': 512,
}


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Any      	=		VOCAB_FILES_NAMES

	__magic_name__   :       Tuple      	=		VOCAB_FILES_NAMES
	__magic_name__   :       Union[str, Any]      	=		PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       int      	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Optional[Any]      	=		['input_ids', 'attention_mask']






	def __init__(					self		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						int="<pad>"					,     lowerCAmelCase		:						Any="</s>"					,     lowerCAmelCase		:						Dict="<unk>"					,     lowerCAmelCase		:						Any="<mask_2>"					,     lowerCAmelCase		:						Any="<mask_1>"					,     lowerCAmelCase		:						Dict=None					,     lowerCAmelCase		:						Any=103					,     lowerCAmelCase		:						Optional[Dict[str, Any]] = None					,     **lowerCAmelCase		:						Any					,     )	->       None:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      offset
						if additional_special_tokens is not None:
											if not isinstance(lowerCAmelCase					,     lowerCAmelCase	):
																raise TypeError(
																    F'''additional_special_tokens should be of type {type(lowerCAmelCase	)}, but is'''
																    F''' {type(lowerCAmelCase	)}'''	)

											__UpperCamelCase					:	List[str]             =      (
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    F'''<unk_{i}>''' for i in range(len(lowerCAmelCase	)					,     self.offset - 1	)
											]

											if len(set(lowerCAmelCase	)	) != len(lowerCAmelCase	):
																raise ValueError(
																    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''	)
											__UpperCamelCase					:	str             =      additional_special_tokens_extended
						else:
											__UpperCamelCase					:	List[Any]             =      [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2					,     self.offset	)]

						__UpperCamelCase					:	str             =      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=lowerCAmelCase					,     unk_token=lowerCAmelCase					,     mask_token=lowerCAmelCase					,     pad_token=lowerCAmelCase					,     mask_token_sent=lowerCAmelCase					,     offset=lowerCAmelCase					,     additional_special_tokens=lowerCAmelCase					,     sp_model_kwargs=self.sp_model_kwargs					,     **lowerCAmelCase					,     )
						__UpperCamelCase					:	List[Any]             =      mask_token_sent
						__UpperCamelCase					:	Tuple             =      vocab_file
						__UpperCamelCase					:	Optional[Any]             =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(lowerCAmelCase	)

						# add special tokens to encoder dict
						__UpperCamelCase					:	Dict[int, str]             =      {
						    0: self.pad_token,
						    1: self.eos_token,
						}

						if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }	)

						if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1					,     self.offset - 1	)}	)

						__UpperCamelCase					:	Dict[str, int]             =      {v: k for k, v in self.encoder.items()}






	@property
	def 					lowerCamelCase__     (					self		:						str	)	->       int:







						"""simple docstring"""







						return len(self.sp_model	) + self.offset






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Dict[str, int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      {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		:						Dict	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self.__dict__.copy()
						__UpperCamelCase					:	List[str]             =      None
						return state






	def __setstate__(					self		:						List[Any]					,     lowerCAmelCase		:						List[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      d

						# for backward compatibility
						if not hasattr(self					,     """sp_model_kwargs"""	):
											__UpperCamelCase					:	Optional[int]             =      {}

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






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						return self.sp_model.encode(lowerCAmelCase					,     out_type=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						str	)	->       int:







						"""simple docstring"""







						if token in self.decoder:
											return self.decoder[token]
						elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
						__UpperCamelCase					:	List[Any]             =      self.sp_model.piece_to_id(lowerCAmelCase	)
						return sp_id + self.offset






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						int	)	->       str:







						"""simple docstring"""







						if index in self.encoder:
											return self.encoder[index]
						elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
						else:
											__UpperCamelCase					:	Any             =      self.sp_model.IdToPiece(index - self.offset	)
						return token






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Tuple	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      []
						__UpperCamelCase					:	Optional[int]             =      """"""
						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(lowerCAmelCase	) + token
																__UpperCamelCase					:	str             =      []
											else:
																current_sub_tokens.append(lowerCAmelCase	)
						out_string += self.sp_model.decode(lowerCAmelCase	)
						return out_string.strip()






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						Optional[Any]=False	)	->       str:







						"""simple docstring"""







						return 1






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      set(self.all_special_ids	)  # call it once instead of inside list comp
						all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

						return [1 if x in all_special_ids else 0 for x in seq]






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List					,     lowerCAmelCase		:						Optional[List] = None					,     lowerCAmelCase		:						bool = False	)	->       List[int]:







						"""simple docstring"""







						if already_has_special_tokens:
											return self._special_token_mask(lowerCAmelCase	)
						elif token_ids_a is None:
											return self._special_token_mask(lowerCAmelCase	) + [1]
						else:
											return self._special_token_mask(token_ids_a + token_ids_a	) + [1]






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Tuple=None	)	->       List[int]:







						"""simple docstring"""







						if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Optional[str] = None	)	->       Tuple[str]:







						"""simple docstring"""







						if not os.path.isdir(lowerCAmelCase	):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
											return
						__UpperCamelCase					:	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:
																__UpperCamelCase					:	Optional[Any]             =      self.sp_model.serialized_model_proto()
																fi.write(lowerCAmelCase	)

						return (out_vocab_file,)












def 				A__ (snake_case   :		float		,	snake_case   :		int				)     ->						float:
					if digit_amount > 0:
										return round(number - int(snake_case				)		,	snake_case				)
					return number - int(snake_case				)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))






