




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


__A								=       logging.get_logger(__name__)

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

__A								=       {
    '''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'''
        ),
    }
}

__A								=       {
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}







class        __lowerCAmelCase					(			lowerCamelCase_					):

			"""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__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else bos_token
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else eos_token
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else unk_token
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else pad_token
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else cls_token
							__lowerCamelCase									=       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
							__lowerCamelCase									=       AddedToken(snake_case__     ,		lstrip=snake_case__     ,		rstrip=snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else mask_token

							__lowerCamelCase									=       {} if sp_model_kwargs is None else sp_model_kwargs

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

							__lowerCamelCase									=       vocab_file

							__lowerCamelCase									=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(snake_case__				)


			@property
			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							return self.sp_model.get_piece_size()


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       {self.convert_ids_to_tokens(snake_case__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab


			def __getstate__( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       self.__dict__.copy()
							__lowerCamelCase									=       None
							return state


			def __setstate__( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       d

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							return self.sp_model.encode(snake_case__     ,		out_type=snake_case__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							return self.sp_model.piece_to_id(snake_case__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       self.sp_model.IdToPiece(snake_case__				)
							return token


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       []
							__lowerCamelCase									=       ''
							__lowerCamelCase									=       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(snake_case__				) + token
															__lowerCamelCase									=       True
															__lowerCamelCase									=       []
											else:
															current_sub_tokens.append(snake_case__				)
															__lowerCamelCase									=       False
							out_string += self.sp_model.decode(snake_case__				)
							return out_string.strip()


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		**lowerCamelCase__     ,		)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       kwargs.pop('use_source_tokenizer'     ,		snake_case__				)

							__lowerCamelCase									=       self.convert_ids_to_tokens(snake_case__     ,		skip_special_tokens=snake_case__				)

							# 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									=       []
							__lowerCamelCase									=       []
							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(snake_case__				)				)
																			__lowerCamelCase									=       []
															sub_texts.append(snake_case__				)
											else:
															current_sub_text.append(snake_case__				)
							if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(snake_case__				)				)

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Dict:



							'''simple docstring'''
							if not os.path.isdir(snake_case__				):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
							__lowerCamelCase									=       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__				) and os.path.isfile(self.vocab_file				):
											copyfile(self.vocab_file     ,		snake_case__				)
							elif not os.path.isfile(self.vocab_file				):
											with open(snake_case__     ,		'wb'				) as fi:
															__lowerCamelCase									=       self.sp_model.serialized_model_proto()
															fi.write(snake_case__				)

							return (out_vocab_file,)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> List[Any]:



							'''simple docstring'''
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__lowerCamelCase									=       [self.cls_token_id]
							__lowerCamelCase									=       [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = False				)       -> Optional[Any]:



							'''simple docstring'''
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case__     ,		token_ids_a=snake_case__     ,		already_has_special_tokens=snake_case__				)

							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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       [self.sep_token_id]
							__lowerCamelCase									=       [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 os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    'files'  ,							[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tmp_path_factory.mktemp('dset_infos_dir'  )
			if "full:README.md" in files:
						with open(dataset_infos_dir / 'README.md'  ,							'w'  ) as f:
									f.write('---\ndataset_info:\n  dataset_size: 42\n---'  )
			if "empty:README.md" in files:
						with open(dataset_infos_dir / 'README.md'  ,							'w'  ) as f:
									f.write(''  )
    # we want to support dataset_infos.json for backward compatibility
			if "dataset_infos.json" in files:
						with open(dataset_infos_dir / 'dataset_infos.json'  ,							'w'  ) as f:
									f.write('{"default": {"dataset_size": 42}}'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DatasetInfosDict.from_directory(lowerCamelCase_  )
			assert dataset_infos
			assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    'dataset_info'  ,							[
        DatasetInfo(),
        DatasetInfo(
            description='foo'  ,							features=Features({'a': Value('int32'  )}  )  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train'}]  ,							download_size=42  ,							),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     DatasetInfo  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      str(lowerCamelCase_  )
			dataset_info.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      DatasetInfo.from_directory(lowerCamelCase_  )
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(lowerCamelCase_  ,							'dataset_info.json'  )  )





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      DatasetInfo(
			    description='foo'  ,							citation='bar'  ,							homepage='https://foo.bar'  ,							license='CC0'  ,							features=Features({'a': Value('int32'  )}  )  ,							post_processed={}  ,							supervised_keys=()  ,							task_templates=[]  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train', 'num_examples': 42}]  ,							download_checksums={}  ,							download_size=13_37  ,							post_processing_size=4_42  ,							dataset_size=12_34  ,							size_in_bytes=13_37 + 4_42 + 12_34  ,							)
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      dataset_info._to_yaml_dict()
			assert sorted(lowerCamelCase_  ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML  )
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						assert key in dataset_info_yaml_dict
						assert isinstance(dataset_info_yaml_dict[key]  ,							(list, dict, int, str)  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      yaml.safe_dump(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      yaml.safe_load(lowerCamelCase_  )
			assert dataset_info_yaml_dict == reloaded





def        __UpperCAmelCase						(							)       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      DatasetInfo()
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dataset_info._to_yaml_dict()
			assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'  ,							[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}  ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}  ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'  ,							features=Features({'a': Value('int32'  )}  )  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train'}]  ,							download_size=42  ,							)
            }  ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42  ),
                'v2': DatasetInfo(dataset_size=13_37  ),
            }  ),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     DatasetInfosDict  )       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      str(lowerCamelCase_  )
			dataset_infos_dict.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DatasetInfosDict.from_directory(lowerCamelCase_  )

			# the config_name of the dataset_infos_dict take over the attribute
			for config_name, dataset_info in dataset_infos_dict.items():
						SCREAMING_SNAKE_CASE_   : Any					  =      config_name
						# the yaml representation doesn't include fields like description or citation
						# so we just test that we can recover what we can from the yaml
						SCREAMING_SNAKE_CASE_   : Any					  =      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()  )
			assert dataset_infos_dict == reloaded

			if dataset_infos_dict:
						assert os.path.exists(os.path.join(lowerCamelCase_  ,							'README.md'  )  )



