






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__ :      List[Any]			 =["""image_processor""", """tokenizer"""]
				UpperCamelCase__ :      Dict			 ="""LayoutLMv2ImageProcessor"""
				UpperCamelCase__ :      Any			 =("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")


				def __init__( self							,  lowerCamelCase__=None							,  lowerCamelCase__=None							,  **lowerCamelCase__    ):




									"""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      :					Optional[int]       		=kwargs.pop('feature_extractor'    )

									__UpperCamelCase      :					int       		=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							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = True							,  lowerCamelCase__ = False							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 0							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = False							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  ):




									"""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      :					List[Any]       		=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      :					Tuple       		=[text]  # add batch dimension (as the image processor always adds a batch dimension)
														__UpperCamelCase      :					Optional[Any]       		=features["words"]

									__UpperCamelCase      :					Union[str, Any]       		=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      :					Dict       		=features.pop('pixel_values'    )
									if return_overflowing_tokens is True:
														__UpperCamelCase      :					Dict       		=self.get_overflowing_images(_SCREAMING_SNAKE_CASE							,  encoded_inputs['overflow_to_sample_mapping']    )
									__UpperCamelCase      :					Union[str, Any]       		=images

									return encoded_inputs


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		=[]
									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       __lowercase							( self							,  *lowerCamelCase__							,  **lowerCamelCase__    ):




									"""simple docstring"""

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


				def       __lowercase							( self							,  *lowerCamelCase__							,  **lowerCamelCase__    ):




									"""simple docstring"""

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


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

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


				@property
				def       __lowercase							( self    ):




									"""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       __lowercase							( self    ):




									"""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






"""simple docstring"""






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__snake_case							:   Optional[int]      =    {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	__snake_case							:   Any      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)