


"""simple docstring"""





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


snake_case__		:     Optional[int]    				=				'''▁'''

snake_case__		:     Any    				=				{'''vocab_file''': '''spiece.model'''}

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

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


snake_case__		:     List[Any]    				=				logging.get_logger(__name__)



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES

     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']






     def __init__(	self  :					int					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Optional[Any]="<pad>"					,				UpperCamelCase_  :					Any="</s>"					,				UpperCamelCase_  :					Tuple="<unk>"					,				UpperCamelCase_  :					List[Any]="<mask_2>"					,				UpperCamelCase_  :					str="<mask_1>"					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					Tuple=1_0_3					,				UpperCamelCase_  :					Optional[Dict[str, Any]] = None					,				**UpperCamelCase_  :					Optional[Any]					,				):
           lowerCAmelCase					:	int       =   offset
           if additional_special_tokens is not None:
                 if not isinstance(UpperCamelCase_					,				UpperCamelCase_				):
                       raise TypeError(
                           F'''additional_special_tokens should be of type {type(UpperCamelCase_				)}, but is'''
                           F''' {type(UpperCamelCase_				)}'''				)

                 lowerCAmelCase					:	Union[str, Any]       =   (
                     ([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(UpperCamelCase_				)					,				self.offset - 1				)
                 ]

                 if len(set(UpperCamelCase_				)				) != len(UpperCamelCase_				):
                       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}.'''				)
                 lowerCAmelCase					:	Tuple       =   additional_special_tokens_extended
           else:
                 lowerCAmelCase					:	str       =   [mask_token_sent] if mask_token_sent is not None else []
                 additional_special_tokens += [F'''<unk_{i}>''' for i in range(2					,				self.offset				)]

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

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

           # add special tokens to encoder dict
           lowerCAmelCase					:	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				)}				)

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






     @property
     def      lowerCamelCase__     (	self  :					str				):
           return len(self.sp_model				) + self.offset






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Any       =   {self.convert_ids_to_tokens(UpperCamelCase_				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab






     def __getstate__(	self  :					List[str]				):
           lowerCAmelCase					:	List[str]       =   self.__dict__.copy()
           lowerCAmelCase					:	Union[str, Any]       =   None
           return state






     def __setstate__(	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	Any       =   d

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

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






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					str				):
           return self.sp_model.encode(UpperCamelCase_					,				out_type=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					str				):
           if token in self.decoder:
                 return self.decoder[token]
           elif token in self.added_tokens_decoder:
                 return self.added_tokens_decoder[token]
           lowerCAmelCase					:	List[Any]       =   self.sp_model.piece_to_id(UpperCamelCase_				)
           return sp_id + self.offset






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					int				):
           if index in self.encoder:
                 return self.encoder[index]
           elif index in self.added_tokens_encoder:
                 return self.added_tokens_encoder[index]
           else:
                 lowerCAmelCase					:	Any       =   self.sp_model.IdToPiece(index - self.offset				)
           return token






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]				):
           lowerCAmelCase					:	List[str]       =   []
           lowerCAmelCase					:	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(UpperCamelCase_				) + token
                       lowerCAmelCase					:	Optional[int]       =   []
                 else:
                       current_sub_tokens.append(UpperCamelCase_				)
           out_string += self.sp_model.decode(UpperCamelCase_				)
           return out_string.strip()






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[str]=False				):
           return 1






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	int       =   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  :					Tuple					,				UpperCamelCase_  :					List					,				UpperCamelCase_  :					Optional[List] = None					,				UpperCamelCase_  :					bool = False				):
           if already_has_special_tokens:
                 return self._special_token_mask(UpperCamelCase_				)
           elif token_ids_a is None:
                 return self._special_token_mask(UpperCamelCase_				) + [1]
           else:
                 return self._special_token_mask(token_ids_a + token_ids_a				) + [1]






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					List[str]=None				):
           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  :					Any					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	str       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

           return (out_vocab_file,)






"""simple docstring"""





from __future__ import annotations


def      _snake_case						(						_snake_case				:			list[int] ,   _snake_case				:			int			):
      if len(_snake_case			) == 0:
            return False
      lowerCAmelCase					:	List[Any]       =   len(_snake_case			) // 2
      if a_list[midpoint] == item:
            return True
      if item < a_list[midpoint]:
            return binary_search(a_list[:midpoint] ,   _snake_case			)
      else:
            return binary_search(a_list[midpoint + 1 :] ,   _snake_case			)


if __name__ == "__main__":
 snake_case__		:     List[str]    				=				input('''Enter numbers separated by comma:\n''').strip()
 snake_case__		:     Optional[int]    				=				[int(item.strip()) for item in user_input.split(''',''')]
 snake_case__		:     Dict    				=				int(input('''Enter the number to be found in the list:\n''').strip())
 snake_case__		:     str    				=				'''''' if binary_search(sequence, target) else '''not '''
 print(f"""{target} was {not_str}found in {sequence}""")


