





from typing import TYPE_CHECKING

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


lowerCAmelCase_        =			{
    """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:
      lowerCAmelCase_        =			[
          """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:
      lowerCAmelCase_        =			[
          """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:
      lowerCAmelCase_        =			[
          """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

      lowerCAmelCase_        =			_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




