






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       							=  {
    'auto_factory': ['get_values'],
    'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
    'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
    'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
    'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
    'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  [
      'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
      'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
      'MODEL_FOR_BACKBONE_MAPPING',
      'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
      'MODEL_FOR_CAUSAL_LM_MAPPING',
      'MODEL_FOR_CTC_MAPPING',
      'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
      'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
      'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
      'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
      'MODEL_FOR_MASKED_LM_MAPPING',
      'MODEL_FOR_MASK_GENERATION_MAPPING',
      'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'MODEL_FOR_OBJECT_DETECTION_MAPPING',
      'MODEL_FOR_PRETRAINING_MAPPING',
      'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
      'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
      'MODEL_FOR_TEXT_ENCODING_MAPPING',
      'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
      'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
      'MODEL_FOR_VISION_2_SEQ_MAPPING',
      'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
      'MODEL_MAPPING',
      'MODEL_WITH_LM_HEAD_MAPPING',
      'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
      'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
      'AutoModel',
      'AutoBackbone',
      'AutoModelForAudioClassification',
      'AutoModelForAudioFrameClassification',
      'AutoModelForAudioXVector',
      'AutoModelForCausalLM',
      'AutoModelForCTC',
      'AutoModelForDepthEstimation',
      'AutoModelForImageClassification',
      'AutoModelForImageSegmentation',
      'AutoModelForInstanceSegmentation',
      'AutoModelForMaskGeneration',
      'AutoModelForTextEncoding',
      'AutoModelForMaskedImageModeling',
      'AutoModelForMaskedLM',
      'AutoModelForMultipleChoice',
      'AutoModelForNextSentencePrediction',
      'AutoModelForObjectDetection',
      'AutoModelForPreTraining',
      'AutoModelForQuestionAnswering',
      'AutoModelForSemanticSegmentation',
      'AutoModelForSeq2SeqLM',
      'AutoModelForSequenceClassification',
      'AutoModelForSpeechSeq2Seq',
      'AutoModelForTableQuestionAnswering',
      'AutoModelForTokenClassification',
      'AutoModelForUniversalSegmentation',
      'AutoModelForVideoClassification',
      'AutoModelForVision2Seq',
      'AutoModelForVisualQuestionAnswering',
      'AutoModelForDocumentQuestionAnswering',
      'AutoModelWithLMHead',
      'AutoModelForZeroShotImageClassification',
      'AutoModelForZeroShotObjectDetection',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  [
      'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
      'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
      'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
      'TF_MODEL_FOR_MASKED_LM_MAPPING',
      'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'TF_MODEL_FOR_PRETRAINING_MAPPING',
      'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
      'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
      'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
      'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
      'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
      'TF_MODEL_MAPPING',
      'TF_MODEL_WITH_LM_HEAD_MAPPING',
      'TFAutoModel',
      'TFAutoModelForAudioClassification',
      'TFAutoModelForCausalLM',
      'TFAutoModelForImageClassification',
      'TFAutoModelForMaskedImageModeling',
      'TFAutoModelForMaskedLM',
      'TFAutoModelForMaskGeneration',
      'TFAutoModelForMultipleChoice',
      'TFAutoModelForNextSentencePrediction',
      'TFAutoModelForPreTraining',
      'TFAutoModelForDocumentQuestionAnswering',
      'TFAutoModelForQuestionAnswering',
      'TFAutoModelForSemanticSegmentation',
      'TFAutoModelForSeq2SeqLM',
      'TFAutoModelForSequenceClassification',
      'TFAutoModelForSpeechSeq2Seq',
      'TFAutoModelForTableQuestionAnswering',
      'TFAutoModelForTextEncoding',
      'TFAutoModelForTokenClassification',
      'TFAutoModelForVision2Seq',
      'TFAutoModelForZeroShotImageClassification',
      'TFAutoModelWithLMHead',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  [
      'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
      'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
      'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
      'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
      'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
      'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
      'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
      'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
      'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
      'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
      'FLAX_MODEL_MAPPING',
      'FlaxAutoModel',
      'FlaxAutoModelForCausalLM',
      'FlaxAutoModelForImageClassification',
      'FlaxAutoModelForMaskedLM',
      'FlaxAutoModelForMultipleChoice',
      'FlaxAutoModelForNextSentencePrediction',
      'FlaxAutoModelForPreTraining',
      'FlaxAutoModelForQuestionAnswering',
      'FlaxAutoModelForSeq2SeqLM',
      'FlaxAutoModelForSequenceClassification',
      'FlaxAutoModelForSpeechSeq2Seq',
      'FlaxAutoModelForTokenClassification',
      'FlaxAutoModelForVision2Seq',
  ]


if TYPE_CHECKING:
  from .auto_factory import get_values
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_auto import (
        MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_XVECTOR_MAPPING,
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
        MODEL_FOR_CAUSAL_LM_MAPPING,
        MODEL_FOR_CTC_MAPPING,
        MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
        MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_MASK_GENERATION_MAPPING,
        MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        MODEL_FOR_MASKED_LM_MAPPING,
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        MODEL_FOR_OBJECT_DETECTION_MAPPING,
        MODEL_FOR_PRETRAINING_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_TEXT_ENCODING_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        MODEL_FOR_VISION_2_SEQ_MAPPING,
        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
        MODEL_MAPPING,
        MODEL_WITH_LM_HEAD_MAPPING,
        AutoBackbone,
        AutoModel,
        AutoModelForAudioClassification,
        AutoModelForAudioFrameClassification,
        AutoModelForAudioXVector,
        AutoModelForCausalLM,
        AutoModelForCTC,
        AutoModelForDepthEstimation,
        AutoModelForDocumentQuestionAnswering,
        AutoModelForImageClassification,
        AutoModelForImageSegmentation,
        AutoModelForInstanceSegmentation,
        AutoModelForMaskedImageModeling,
        AutoModelForMaskedLM,
        AutoModelForMaskGeneration,
        AutoModelForMultipleChoice,
        AutoModelForNextSentencePrediction,
        AutoModelForObjectDetection,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSemanticSegmentation,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelForSpeechSeqaSeq,
        AutoModelForTableQuestionAnswering,
        AutoModelForTextEncoding,
        AutoModelForTokenClassification,
        AutoModelForUniversalSegmentation,
        AutoModelForVideoClassification,
        AutoModelForVisionaSeq,
        AutoModelForVisualQuestionAnswering,
        AutoModelForZeroShotImageClassification,
        AutoModelForZeroShotObjectDetection,
        AutoModelWithLMHead,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_auto import (
        TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASK_GENERATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_MAPPING,
        TF_MODEL_WITH_LM_HEAD_MAPPING,
        TFAutoModel,
        TFAutoModelForAudioClassification,
        TFAutoModelForCausalLM,
        TFAutoModelForDocumentQuestionAnswering,
        TFAutoModelForImageClassification,
        TFAutoModelForMaskedImageModeling,
        TFAutoModelForMaskedLM,
        TFAutoModelForMaskGeneration,
        TFAutoModelForMultipleChoice,
        TFAutoModelForNextSentencePrediction,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSemanticSegmentation,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelForSpeechSeqaSeq,
        TFAutoModelForTableQuestionAnswering,
        TFAutoModelForTextEncoding,
        TFAutoModelForTokenClassification,
        TFAutoModelForVisionaSeq,
        TFAutoModelForZeroShotImageClassification,
        TFAutoModelWithLMHead,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_auto import (
        FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_MASKED_LM_MAPPING,
        FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        FLAX_MODEL_FOR_PRETRAINING_MAPPING,
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForCausalLM,
        FlaxAutoModelForImageClassification,
        FlaxAutoModelForMaskedLM,
        FlaxAutoModelForMultipleChoice,
        FlaxAutoModelForNextSentencePrediction,
        FlaxAutoModelForPreTraining,
        FlaxAutoModelForQuestionAnswering,
        FlaxAutoModelForSeqaSeqLM,
        FlaxAutoModelForSequenceClassification,
        FlaxAutoModelForSpeechSeqaSeq,
        FlaxAutoModelForTokenClassification,
        FlaxAutoModelForVisionaSeq,
    )

else:
  import sys

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











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			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Dict														=		None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'					, UpperCamelCase					, )
      _snake_case      : Dict														=		kwargs.pop('feature_extractor'							)

    _snake_case      : Optional[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__(UpperCamelCase					, UpperCamelCase							)







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


    '''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.'							)

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'							)

    # first, apply the image processor
    _snake_case      : Optional[Any]														=		self.image_processor(images=UpperCamelCase					, return_tensors=UpperCamelCase							)

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

    _snake_case      : Optional[int]														=		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=UpperCamelCase					, add_special_tokens=UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, max_length=UpperCamelCase					, stride=UpperCamelCase					, pad_to_multiple_of=UpperCamelCase					, return_token_type_ids=UpperCamelCase					, return_attention_mask=UpperCamelCase					, return_overflowing_tokens=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, return_offsets_mapping=UpperCamelCase					, return_length=UpperCamelCase					, verbose=UpperCamelCase					, return_tensors=UpperCamelCase					, **UpperCamelCase					, )

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

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





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

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

    return images_with_overflow







  def UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





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




