






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

__a       :	Optional[int]             = {'vocab_file': 'spiece.model'}

__a       :	List[str]             = {
    '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       :	List[str]             = {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =['input_ids', 'attention_mask']
							_SCREAMING_SNAKE_CASE    =[]




							def __init__(   self:     List[str]				,		__A:     Tuple				,		__A:     int="<unk>"				,		__A:     List[str]="<s>"				,		__A:     Any="</s>"				,		__A:     Union[str, Any]="<pad>"				,		__A:     Optional[Any]="[SEP]"				,		__A:     int="[MASK]"				,		__A:     int="[CLS]"				,		__A:     Optional[Dict[str, Any]] = None				,		**__A:     int				,		):

												'''simple docstring'''

												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  lowercase		(   self:     Any    ):

												'''simple docstring'''

												return self.sp_model.get_piece_size()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												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:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.__dict__.copy()
												a__          =  None
												return state




							def __setstate__(   self:     Optional[Any]				,		__A:     Tuple    ):

												'''simple docstring'''

												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  lowercase		(   self:     Any				,		__A:     str    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		__A:     List[Any]    ):

												'''simple docstring'''

												return self.sp_model.piece_to_id(__A    )




							def  lowercase		(   self:     int				,		__A:     Dict    ):

												'''simple docstring'''

												a__          =  self.sp_model.IdToPiece(__A    )
												return token




							def  lowercase		(   self:     Optional[int]				,		__A:     int    ):

												'''simple docstring'''

												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  lowercase		(   self:     Tuple				,		__A:     List[int]				,		__A:     bool = False				,		__A:     bool = None				,		__A:     bool = True				,		**__A:     str				,		):

												'''simple docstring'''

												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  lowercase		(   self:     Any				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												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  lowercase		(   self:     int				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												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  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None				,		__A:     bool = False    ):

												'''simple docstring'''

												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  lowercase		(   self:     Tuple				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												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]










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	__a       :	List[Any]             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


