




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_big_bird import BigBirdTokenizer
else:
						UpperCamelCase__ :   List[Any]						=							None

UpperCamelCase__ :   Union[str, Any]						=							logging.get_logger(__name__)
UpperCamelCase__ :   Dict						=							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   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'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   List[Any]						=							{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}


UpperCamelCase__ :   Union[str, Any]						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Union[str, Any]						=     VOCAB_FILES_NAMES
				__a     :       Any						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       int						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       Any						=     BigBirdTokenizer
				__a     :       str						=     ["input_ids", "attention_mask"]
				__a     :       List[int]						=     []






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

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

							super().__init__(
							    snake_case__      ,tokenizer_file=snake_case__      ,bos_token=snake_case__      ,eos_token=snake_case__      ,unk_token=snake_case__      ,sep_token=snake_case__      ,pad_token=snake_case__      ,cls_token=snake_case__      ,mask_token=snake_case__      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Any					  =      [self.cls_token_id]
							if token_ids_a is None:
										return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = False ):

							if already_has_special_tokens:
										if token_ids_a is not None:
													raise ValueError(
													    'You should not supply a second sequence if the provided sequence of '
													    'ids is already formatted with special tokens for the model.' )
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : str					  =      [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]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
										    'tokenizer.' )

							if not os.path.isdir(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : str					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
										copyfile(self.vocab_file      ,snake_case__ )

							return (out_vocab_file,)









import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase__ :   List[Any]						=							pytest.mark.integration





@pytest.mark.parametrize('path'  ,							['paws', 'csv']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			inspect_dataset(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'  )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'  )
@pytest.mark.parametrize('path'  ,							['accuracy']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  Optional[Any]:







			"""simple docstring"""





			inspect_metric(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, config_name, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )
			assert info.config_name == config_name
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     int  )       ->  List[Any]:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, expected'  ,							[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_config_names(lowerCamelCase_  )
			assert expected in config_names





@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'  ,							[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_infos(lowerCamelCase_  )
			assert list(infos.keys()  ) == expected_configs
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Any					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits_in_first_config





@pytest.mark.parametrize(
    'path, expected_config, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_dataset_infos(lowerCamelCase_  )
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Tuple					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_split_names(lowerCamelCase_  ,							config_name=lowerCamelCase_  )



