


'''simple docstring'''
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_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']
      lowerCAmelCase__		     =     []






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


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else bos_token
          UpperCAmelCase__			:		Any													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else eos_token
          UpperCAmelCase__			:		int													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else unk_token
          UpperCAmelCase__			:		str													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else pad_token
          UpperCAmelCase__			:		Optional[Any]													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else cls_token
          UpperCAmelCase__			:		str													=				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
          UpperCAmelCase__			:		int													=				AddedToken(_A						,						lstrip=_A						,						rstrip=_A ) if isinstance(_A						,						_A ) else mask_token

          UpperCAmelCase__			:		Dict													=				{} 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						,						)

          UpperCAmelCase__			:		Any													=				vocab_file

          UpperCAmelCase__			:		Any													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(_A )






      @property
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          return self.sp_model.get_piece_size()






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				{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[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.__dict__.copy()
          UpperCAmelCase__			:		List[Any]													=				None
          return state






      def __setstate__(					self				:    Dict						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				d

          # for backward compatibility
          if not hasattr(self						,						'''sp_model_kwargs''' ):
              UpperCAmelCase__			:		Optional[int]													=				{}

          UpperCAmelCase__			:		List[Any]													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.vocab_file )






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Tuple ):


          '''simple docstring'''


          return self.sp_model.piece_to_id(_A )






      def lowercase_							(					self				:    int						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.sp_model.IdToPiece(_A )
          return token






      def lowercase_							(					self				:    int						,						_A				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				[]
          UpperCAmelCase__			:		Dict													=				''''''
          UpperCAmelCase__			:		int													=				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
                  UpperCAmelCase__			:		Tuple													=				True
                  UpperCAmelCase__			:		str													=				[]
              else:
                  current_sub_tokens.append(_A )
                  UpperCAmelCase__			:		Optional[Any]													=				False
          out_string += self.sp_model.decode(_A )
          return out_string.strip()






      def lowercase_							(					self				:    int						,						_A				:    List[int]						,						_A				:    bool = False						,						_A				:    bool = None						,						_A				:    bool = True						,						**_A				:    int						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				kwargs.pop('''use_source_tokenizer'''						,						_A )

          UpperCAmelCase__			:		str													=				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
          UpperCAmelCase__			:		int													=				[]
          UpperCAmelCase__			:		int													=				[]
          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 ) )
                      UpperCAmelCase__			:		Optional[Any]													=				[]
                  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:
              UpperCAmelCase__			:		int													=				re.sub(R''' (\[(MASK|SEP)\])'''						,						R'''\1'''						,						''' '''.join(_A ) )
          else:
              UpperCAmelCase__			:		Dict													=				''''''.join(_A )

          UpperCAmelCase__			:		List[str]													=				(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
              UpperCAmelCase__			:		Tuple													=				self.clean_up_tokenization(_A )
              return clean_text
          else:
              return text






      def lowercase_							(					self				:    List[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
          UpperCAmelCase__			:		Optional[int]													=				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:
                  UpperCAmelCase__			:		Optional[int]													=				self.sp_model.serialized_model_proto()
                  fi.write(_A )

          return (out_vocab_file,)






      def lowercase_							(					self				:    Dict						,						_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]
          UpperCAmelCase__			:		List[Any]													=				[self.cls_token_id]
          UpperCAmelCase__			:		Optional[Any]													=				[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep






      def lowercase_							(					self				:    Tuple						,						_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				:    List[Any]						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				[self.sep_token_id]
          UpperCAmelCase__			:		Any													=				[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'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__    					=      {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''VivitImageProcessor''']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VivitModel''',
      '''VivitPreTrainedModel''',
      '''VivitForVideoClassification''',
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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