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        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



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

        lowerCAmelCase__  			=      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__(lowercase__    ,					lowercase__)




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

        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
        lowerCAmelCase__  			=      self.image_processor(images=lowercase__    ,					return_tensors=lowercase__)

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

        lowerCAmelCase__  			=      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=lowercase__    ,					add_special_tokens=lowercase__    ,					padding=lowercase__    ,					truncation=lowercase__    ,					max_length=lowercase__    ,					stride=lowercase__    ,					pad_to_multiple_of=lowercase__    ,					return_token_type_ids=lowercase__    ,					return_attention_mask=lowercase__    ,					return_overflowing_tokens=lowercase__    ,					return_special_tokens_mask=lowercase__    ,					return_offsets_mapping=lowercase__    ,					return_length=lowercase__    ,					verbose=lowercase__    ,					return_tensors=lowercase__    ,					**lowercase__    ,					)

        # add pixel values
        lowerCAmelCase__  			=      features.pop('pixel_values')
        if return_overflowing_tokens is True:
               lowerCAmelCase__  			=      self.get_overflowing_images(lowercase__    ,					encoded_inputs['overflow_to_sample_mapping'])
        lowerCAmelCase__  			=      images

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



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

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

        return images_with_overflow




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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




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







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



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