


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


__UpperCamelCase    		=       logging.get_logger(__name__)

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

__UpperCamelCase    		=       {
    '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'
        ),
    }
}

__UpperCamelCase    		=       {
    '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"""






			_UpperCamelCase		:					Tuple    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					Any    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					Optional[Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:					Any    =		['input_ids', 'attention_mask']
			_UpperCamelCase		:					List[int]    =		[]




			def __init__(							self      ,						snake_case      ,						snake_case="<unk>"      ,						snake_case="<s>"      ,						snake_case="</s>"      ,						snake_case="<pad>"      ,						snake_case="[SEP]"      ,						snake_case="[MASK]"      ,						snake_case="[CLS]"      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else bos_token
						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else eos_token
						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else unk_token
						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else pad_token
						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else cls_token
						UpperCamelCase__					=       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
						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else mask_token

						UpperCamelCase__					=       {} 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      ,						)

						UpperCamelCase__					=       vocab_file

						UpperCamelCase__					=       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(snake_case     )




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.sp_model.get_piece_size()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {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     ):

						'''simple docstring'''


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




			def __setstate__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       d

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

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




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.sp_model.piece_to_id(snake_case     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.sp_model.IdToPiece(snake_case     )
						return token




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       []
						UpperCamelCase__					=       ""
						UpperCamelCase__					=       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
												UpperCamelCase__					=       True
												UpperCamelCase__					=       []
									else:
												current_sub_tokens.append(snake_case     )
												UpperCamelCase__					=       False
						out_string += self.sp_model.decode(snake_case     )
						return out_string.strip()




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

						'''simple docstring'''


						UpperCamelCase__					=       kwargs.pop("use_source_tokenizer"      ,						snake_case     )

						UpperCamelCase__					=       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
						UpperCamelCase__					=       []
						UpperCamelCase__					=       []
						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     )     )
															UpperCamelCase__					=       []
												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:
									UpperCamelCase__					=       re.sub(R" (\[(MASK|SEP)\])"      ,						R"\1"      ,						" ".join(snake_case     )     )
						else:
									UpperCamelCase__					=       "".join(snake_case     )

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




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

						'''simple docstring'''


						if not os.path.isdir(snake_case     ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
									return
						UpperCamelCase__					=       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:
												UpperCamelCase__					=       self.sp_model.serialized_model_proto()
												fi.write(snake_case     )

						return (out_vocab_file,)




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

						'''simple docstring'''


						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						UpperCamelCase__					=       [self.cls_token_id]
						UpperCamelCase__					=       [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep




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

						'''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 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [self.sep_token_id]
						UpperCamelCase__					=       [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]






from typing import TYPE_CHECKING

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


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

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


