


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 (							UpperCAmelCase     ):
		_a      :    Any           =      ['image_processor', 'tokenizer']
		_a      :    int           =      'LayoutLMv2ImageProcessor'
		_a      :    Optional[Any]           =      ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'     ,       _SCREAMING_SNAKE_CASE     ,       )
																_UpperCAmelCase											=     kwargs.pop('feature_extractor'		)

									_UpperCAmelCase											=     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__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def __call__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 0     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			BatchEncoding:




									"""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
									_UpperCAmelCase											=     self.image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)

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

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

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

									return encoded_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





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

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

									return images_with_overflow
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)
		@property
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





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






		@property
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





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




from typing import TYPE_CHECKING

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


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :List[str]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
