



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__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     ['image_processor', 'tokenizer']
				A_   :     int								=     'LayoutLMv2ImageProcessor'
				A_   :     Optional[Any]								=     ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')


				def __init__(							self      :  str						, __UpperCamelCase      :  Tuple=None						, __UpperCamelCase      :  Union[str, Any]=None						, **__UpperCamelCase      :  Optional[Any]       )		->     Any:
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'						, __UpperCamelCase						, )
																		A      						=    kwargs.pop('feature_extractor'       )

											A      						=    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      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __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] = None						, __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      :  List[str]						, )		->     BatchEncoding:
											# verify input
											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
											A      						=    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       ):
																									A      						=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		A      						=    features['words']

											A      						=    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
											A      						=    features.pop('pixel_values'       )
											if return_overflowing_tokens is True:
																		A      						=    self.get_overflowing_images(__UpperCamelCase						, encoded_inputs['overflow_to_sample_mapping']       )
											A      						=    images

											return encoded_inputs


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  int       )		->     str:
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											A      						=    []
											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      :  Optional[int]						, *__UpperCamelCase      :  int						, **__UpperCamelCase      :  List[str]       )		->     Union[str, Any]:
											return self.tokenizer.batch_decode(*__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  List[Any]						, **__UpperCamelCase      :  Optional[int]       )		->     Dict:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


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


				@property
				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											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      :  List[Any]       )		->     Optional[Any]:
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						, __UpperCamelCase						, )
											return self.image_processor






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''pixel_values''']






				def __init__(  self			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = PILImageResampling.BILINEAR			, _lowerCAmelCase = True			, _lowerCAmelCase = 1 / 255			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size if size is not None else {'''shortest_edge''': 384}
										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size
										# Default value set here for backwards compatibility where the value in config is None
										__SCREAMING_SNAKE_CASE:				List[str]           =   crop_pct if crop_pct is not None else 224 / 256
										__SCREAMING_SNAKE_CASE:				List[Any]           =   resample
										__SCREAMING_SNAKE_CASE:				Any           =   do_rescale
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   rescale_factor
										__SCREAMING_SNAKE_CASE:				Any           =   do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BICUBIC			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
										if "shortest_edge" not in size:
																raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
										__SCREAMING_SNAKE_CASE:				str           =   size['''shortest_edge''']

										if shortest_edge < 384:
																# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																__SCREAMING_SNAKE_CASE:				Dict           =   int(shortest_edge / crop_pct					)
																__SCREAMING_SNAKE_CASE:				Any           =   get_resize_output_image_size(_lowerCAmelCase			, size=_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
																# then crop to (shortest_edge, shortest_edge)
																return center_crop(image=_lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
										else:
																# warping (no cropping) when evaluated at 384 or larger
																return resize(
																    _lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										return rescale(_lowerCAmelCase			, scale=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										return normalize(_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = ChannelDimension.FIRST			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   crop_pct if crop_pct is not None else self.crop_pct
										__SCREAMING_SNAKE_CASE:				Tuple           =   resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE:				Any           =   rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE:				str           =   image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE:				List[Any]           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				Dict           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   make_list_of_images(_lowerCAmelCase					)

										if not valid_images(_lowerCAmelCase					):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

										if do_resize and size is None or resample is None:
																raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

										if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																raise ValueError('''crop_pct must be specified if size < 384.'''					)

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

										# All transformations expect numpy arrays.
										__SCREAMING_SNAKE_CASE:				Tuple           =   [to_numpy_array(_lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				str           =   [self.resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, crop_pct=_lowerCAmelCase			, resample=_lowerCAmelCase					) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE:				Any           =   [self.rescale(image=_lowerCAmelCase			, scale=_lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				str           =   [to_channel_dimension_format(_lowerCAmelCase			, _lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				List[str]           =   {'''pixel_values''': images}
										return BatchFeature(data=_lowerCAmelCase			, tensor_type=_lowerCAmelCase					)



