





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:
						snake_case_							=			None

snake_case_							=			logging.get_logger(__name__)
snake_case_							=			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_							=			{
    '''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'''
        ),
    },
}

snake_case_							=			{
    '''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,
}


snake_case_							=			'''▁'''
class        snake_case_		(			__lowerCamelCase):
		lowerCamelCase    :Optional[Any]						=     VOCAB_FILES_NAMES
		lowerCamelCase    :Union[str, Any]						=     PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Union[str, Any]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :Union[str, Any]						=     BigBirdTokenizer
		lowerCamelCase    :str						=     ["input_ids", "attention_mask"]
		lowerCamelCase    :Any						=     []







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

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

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

				lowerCamelCase   : int								=vocab_file
				lowerCamelCase   : Tuple								=False if not self.vocab_file else True







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   Dict:
				lowerCamelCase   : Optional[Any]								=[self.sep_token_id]
				lowerCamelCase   : int								=[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 			__lowercase   (		self			,     __lowercase			,     __lowercase = None			,     __lowercase = False   )				->   Any:

				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(UpperCAmelCase_   )) + [1]
				return [1] + ([0] * len(UpperCAmelCase_   )) + [1] + ([0] * len(UpperCAmelCase_   )) + [1]







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   int:
				lowerCamelCase   : Dict								=[self.sep_token_id]
				lowerCamelCase   : 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 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   Union[str, Any]:
				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(UpperCAmelCase_   ):
						logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
						return
				lowerCamelCase   : Optional[int]								=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_   ):
						copyfile(self.vocab_file			,     UpperCAmelCase_   )

				return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_							=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

				snake_case_							=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

