



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


__snake_case				:		Dict										=logging.get_logger(__name__)

__snake_case				:		List[Any]										={'vocab_file': 'spiece.model'}

__snake_case				:		int										={
    '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'
        ),
    }
}

__snake_case				:		Dict										={
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}



class        lowerCamelCase__			(							_UpperCamelCase):




		'''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		,__lowerCamelCase		,__lowerCamelCase="<unk>"		,__lowerCamelCase="<s>"		,__lowerCamelCase="</s>"		,__lowerCamelCase="<pad>"		,__lowerCamelCase="[SEP]"		,__lowerCamelCase="[MASK]"		,__lowerCamelCase="[CLS]"		,__lowerCamelCase = None		,**__lowerCamelCase		,)     ->			None:

						"""simple docstring"""






						lowerCAmelCase__     :				Tuple												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else bos_token
						lowerCAmelCase__     :				Tuple												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else eos_token
						lowerCAmelCase__     :				Optional[Any]												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else unk_token
						lowerCAmelCase__     :				Optional[Any]												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else pad_token
						lowerCAmelCase__     :				Any												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else cls_token
						lowerCAmelCase__     :				int												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCAmelCase__     :				List[Any]												=     AddedToken(lowerCAmelCase_		,lstrip=lowerCAmelCase_		,rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_		,lowerCAmelCase_ ) else mask_token

						lowerCAmelCase__     :				Optional[int]												=     {} if sp_model_kwargs is None else sp_model_kwargs

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

						lowerCAmelCase__     :				Optional[Any]												=     vocab_file

						lowerCAmelCase__     :				Optional[Any]												=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(lowerCAmelCase_ )



		@property
		def 		lowerCAmelCase__      (self )     ->			Optional[Any]:

						"""simple docstring"""






						return self.sp_model.get_piece_size()



		def 		lowerCAmelCase__      (self )     ->			Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__     :				int												=     {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 )     ->			List[str]:

						"""simple docstring"""






						lowerCAmelCase__     :				Any												=     self.__dict__.copy()
						lowerCAmelCase__     :				str												=     None
						return state



		def __setstate__(self		,__lowerCamelCase )     ->			Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__     :				Union[str, Any]												=     d

						# for backward compatibility
						if not hasattr(self		,'''sp_model_kwargs''' ):
										lowerCAmelCase__     :				Optional[Any]												=     {}

						lowerCAmelCase__     :				str												=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(self.vocab_file )



		def 		lowerCAmelCase__      (self		,__lowerCamelCase )     ->			List[str]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__      (self		,__lowerCamelCase )     ->			int:

						"""simple docstring"""






						return self.sp_model.piece_to_id(lowerCAmelCase_ )



		def 		lowerCAmelCase__      (self		,__lowerCamelCase )     ->			Any:

						"""simple docstring"""






						lowerCAmelCase__     :				Tuple												=     self.sp_model.IdToPiece(lowerCAmelCase_ )
						return token



		def 		lowerCAmelCase__      (self		,__lowerCamelCase )     ->			Dict:

						"""simple docstring"""






						lowerCAmelCase__     :				List[Any]												=     []
						lowerCAmelCase__     :				Union[str, Any]												=     ''''''
						lowerCAmelCase__     :				Optional[int]												=     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(lowerCAmelCase_ ) + token
														lowerCAmelCase__     :				Optional[int]												=     True
														lowerCAmelCase__     :				List[Any]												=     []
										else:
														current_sub_tokens.append(lowerCAmelCase_ )
														lowerCAmelCase__     :				List[str]												=     False
						out_string += self.sp_model.decode(lowerCAmelCase_ )
						return out_string.strip()



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase = False		,__lowerCamelCase = None		,__lowerCamelCase = True		,**__lowerCamelCase		,)     ->			str:

						"""simple docstring"""






						lowerCAmelCase__     :				int												=     kwargs.pop('''use_source_tokenizer'''		,lowerCAmelCase_ )

						lowerCAmelCase__     :				Union[str, Any]												=     self.convert_ids_to_tokens(lowerCAmelCase_		,skip_special_tokens=lowerCAmelCase_ )

						# 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
						lowerCAmelCase__     :				Union[str, Any]												=     []
						lowerCAmelCase__     :				Tuple												=     []
						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(lowerCAmelCase_ ) )
																		lowerCAmelCase__     :				Dict												=     []
														sub_texts.append(lowerCAmelCase_ )
										else:
														current_sub_text.append(lowerCAmelCase_ )
						if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
										lowerCAmelCase__     :				Dict												=     re.sub(R''' (\[(MASK|SEP)\])'''		,R'''\1'''		,''' '''.join(lowerCAmelCase_ ) )
						else:
										lowerCAmelCase__     :				List[str]												=     ''''''.join(lowerCAmelCase_ )

						lowerCAmelCase__     :				List[Any]												=     (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
										lowerCAmelCase__     :				Optional[int]												=     self.clean_up_tokenization(lowerCAmelCase_ )
										return clean_text
						else:
										return text



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase = None )     ->			Tuple[str]:

						"""simple docstring"""






						if not os.path.isdir(lowerCAmelCase_ ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
										return
						lowerCAmelCase__     :				List[Any]												=     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:
														lowerCAmelCase__     :				List[str]												=     self.sp_model.serialized_model_proto()
														fi.write(lowerCAmelCase_ )

						return (out_vocab_file,)



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase = None )     ->			List[int]:

						"""simple docstring"""






						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCAmelCase__     :				List[str]												=     [self.cls_token_id]
						lowerCAmelCase__     :				Optional[int]												=     [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = False )     ->			List[int]:

						"""simple docstring"""






						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=lowerCAmelCase_		,token_ids_a=lowerCAmelCase_		,already_has_special_tokens=lowerCAmelCase_ )

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



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase = None )     ->			List[int]:

						"""simple docstring"""






						lowerCAmelCase__     :				Dict												=     [self.sep_token_id]
						lowerCAmelCase__     :				List[Any]												=     [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]








import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs

@pytest.mark.parametrize(
    'kwargs, expected',	[
        ({'num_shards': 0, 'max_num_jobs': 1}, []),
        ({'num_shards': 10, 'max_num_jobs': 1}, [range(10	)]),
        ({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_,	i + 1	) for i in range(10	)]),
        ({'num_shards': 1, 'max_num_jobs': 10}, [range(1	)]),
        ({'num_shards': 10, 'max_num_jobs': 3}, [range(0,	4	), range(4,	7	), range(7,	10	)]),
        ({'num_shards': 3, 'max_num_jobs': 10}, [range(0,	1	), range(1,	2	), range(2,	3	)]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	_distribute_shards(**lowerCAmelCase_	)
   assert out == expected

@pytest.mark.parametrize(
    'gen_kwargs, max_num_jobs, expected',	[
        ({'foo': 0}, 10, [{'foo': 0}]),
        ({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
        ({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
        ({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
        ({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	_split_gen_kwargs(lowerCAmelCase_,	lowerCAmelCase_	)
   assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, expected',	[
        ({'foo': 0}, 1),
        ({'shards': [0]}, 1),
        ({'shards': [0, 1, 2, 3]}, 4),
        ({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
        ({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
        ({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
    ],	)
def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   if expected is RuntimeError:
      with pytest.raises(lowerCAmelCase_	):
         _number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
   else:
      __lowerCAmelCase								=	_number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
      assert out == expected

