


'''simple docstring'''
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     ['image_processor', 'tokenizer']
      lowerCAmelCase__		     =     'LayoutLMv3ImageProcessor'
      lowerCAmelCase__		     =     ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')






      def __init__(					self				:    List[str]						,						_A				:    List[str]=None						,						_A				:    Dict=None						,						**_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.'''						,						_A						,						)
              UpperCAmelCase__			:		List[Any]													=				kwargs.pop('''feature_extractor''' )

          UpperCAmelCase__			:		Union[str, Any]													=				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.''' )
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.''' )

          super().__init__(_A						,						_A )






      def __call__(					self				:    List[Any]						,						_A				:    str						,						_A				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,						_A				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,						_A				:    Union[List[List[int]], List[List[List[int]]]] = None						,						_A				:    Optional[Union[List[int], List[List[int]]]] = None						,						_A				:    bool = True						,						_A				:    Union[bool, str, PaddingStrategy] = False						,						_A				:    Union[bool, str, TruncationStrategy] = None						,						_A				:    Optional[int] = None						,						_A				:    int = 0						,						_A				:    Optional[int] = None						,						_A				:    Optional[bool] = None						,						_A				:    Optional[bool] = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    bool = True						,						_A				:    Optional[Union[str, TensorType]] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.''' )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )

          # first, apply the image processor
          UpperCAmelCase__			:		int													=				self.image_processor(images=_A						,						return_tensors=_A )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(_A						,						_A ):
                  UpperCAmelCase__			:		str													=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__			:		Union[str, Any]													=				features['''words''']

          UpperCAmelCase__			:		List[str]													=				self.tokenizer(
              text=text if text is not None else features['''words''']						,						text_pair=text_pair if text_pair is not None else None						,						boxes=boxes if boxes is not None else features['''boxes''']						,						word_labels=_A						,						add_special_tokens=_A						,						padding=_A						,						truncation=_A						,						max_length=_A						,						stride=_A						,						pad_to_multiple_of=_A						,						return_token_type_ids=_A						,						return_attention_mask=_A						,						return_overflowing_tokens=_A						,						return_special_tokens_mask=_A						,						return_offsets_mapping=_A						,						return_length=_A						,						verbose=_A						,						return_tensors=_A						,						**_A						,						)

          # add pixel values
          UpperCAmelCase__			:		int													=				features.pop('''pixel_values''' )
          if return_overflowing_tokens is True:
              UpperCAmelCase__			:		Optional[Any]													=				self.get_overflowing_images(_A						,						encoded_inputs['''overflow_to_sample_mapping'''] )
          UpperCAmelCase__			:		str													=				images

          return encoded_inputs






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


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx] )

          if len(_A ) != len(_A ):
              raise ValueError(
                  '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                  f""" {len(_A )} and {len(_A )}""" )

          return images_with_overflow






      def lowercase_							(					self				:    List[str]						,						*_A				:    Dict						,						**_A				:    List[Any] ):


          '''simple docstring'''


          return self.tokenizer.batch_decode(*_A						,						**_A )






      def lowercase_							(					self				:    List[Any]						,						*_A				:    int						,						**_A				:    Tuple ):


          '''simple docstring'''


          return self.tokenizer.decode(*_A						,						**_A )






      @property
      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          return ["input_ids", "bbox", "attention_mask", "pixel_values"]






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


          '''simple docstring'''


          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,						_A						,						)
          return self.image_processor_class






      @property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,						_A						,						)
          return self.image_processor




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''DistilBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DistilBertForMaskedLM''',
      '''DistilBertForMultipleChoice''',
      '''DistilBertForQuestionAnswering''',
      '''DistilBertForSequenceClassification''',
      '''DistilBertForTokenClassification''',
      '''DistilBertModel''',
      '''DistilBertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFDistilBertForMaskedLM''',
      '''TFDistilBertForMultipleChoice''',
      '''TFDistilBertForQuestionAnswering''',
      '''TFDistilBertForSequenceClassification''',
      '''TFDistilBertForTokenClassification''',
      '''TFDistilBertMainLayer''',
      '''TFDistilBertModel''',
      '''TFDistilBertPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''FlaxDistilBertForMaskedLM''',
      '''FlaxDistilBertForMultipleChoice''',
      '''FlaxDistilBertForQuestionAnswering''',
      '''FlaxDistilBertForSequenceClassification''',
      '''FlaxDistilBertForTokenClassification''',
      '''FlaxDistilBertModel''',
      '''FlaxDistilBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

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