from timeit import timeit



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if number < 0:
        raise ValueError('''the value of input must not be negative'''	)
    __UpperCamelCase     :str			  =		0
    while number:
        number &= number - 1
        result += 1
    return result



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if number < 0:
        raise ValueError('''the value of input must not be negative'''	)
    __UpperCamelCase     :Optional[Any]			  =		0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result



def lowerCamelCase  (   ):
    '''simple docstring'''







    def do_benchmark(SCREAMING_SNAKE_CASE	) -> None:
        __UpperCamelCase     :List[str]			  =		'''import __main__ as z'''
        print(f"""Benchmark when {number = }:"""	)
        print(f"""{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE	) = }"""	)
        __UpperCamelCase     :Optional[int]			  =		timeit('''z.get_set_bits_count_using_modulo_operator(25)'''  ,			setup=SCREAMING_SNAKE_CASE	)
        print(f"""timeit() runs in {timing} seconds"""	)
        print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE	) = }"""	)
        __UpperCamelCase     :Union[str, Any]			  =		timeit(
            '''z.get_set_bits_count_using_brian_kernighans_algorithm(25)'''  ,			setup=SCREAMING_SNAKE_CASE  ,			)
        print(f"""timeit() runs in {timing} seconds"""	)

    for number in (25, 37, 58, 0):
        do_benchmark(SCREAMING_SNAKE_CASE	)
        print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()







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


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

SCREAMING_SNAKE_CASE							:int							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				{
    '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'
        ),
    }
}

SCREAMING_SNAKE_CASE							:int							 =				{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]
	snake_case_					    =     []






	def __init__(       self					: Any							,A					: List[str]							,A					: str="<unk>"							,A					: int="<s>"							,A					: Union[str, Any]="</s>"							,A					: List[str]="<pad>"							,A					: int="[SEP]"							,A					: Optional[Any]="[MASK]"							,A					: Tuple="[CLS]"							,A					: Optional[Dict[str, Any]] = None							,**A					: Any							,):
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else bos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else eos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else unk_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else pad_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else cls_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else sep_token

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

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

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

					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return self.sp_model.get_piece_size()






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: str							,A					: Optional[Any] ):
					__A   			= d

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

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )






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






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Tuple ):
					return self.sp_model.piece_to_id(A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Tuple ):
					__A   			= self.sp_model.IdToPiece(A )
					return token






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: int ):
					__A   			= []
					__A   			= ""
					__A   			= 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(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[int]							,A					: bool = False							,A					: bool = None							,A					: bool = True							,**A					: Union[str, Any]							,):
					__A   			= kwargs.pop("use_source_tokenizer"							,A )

					__A   			= self.convert_ids_to_tokens(A							,skip_special_tokens=A )

					# 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
					__A   			= []
					__A   			= []
					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(A ) )
																	__A   			= []
													sub_texts.append(A )
									else:
													current_sub_text.append(A )
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A ) )

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

					__A   			= (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
									__A   			= self.clean_up_tokenization(A )
									return clean_text
					else:
									return text






	def 				UpperCamelCase_  (       self					: str							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None ):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]







	def 				UpperCamelCase_  (       self					: Any							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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]
