






from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
					import PIL

# soft dependency
if is_pytesseract_available():
					import pytesseract

lowerCAmelCase_     :   Union[str, Any]           = logging.get_logger(__name__)







def      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       Dict   ):
				return [
				    int(10_00 * (box[0] / width)   ),
				    int(10_00 * (box[1] / height)   ),
				    int(10_00 * (box[2] / width)   ),
				    int(10_00 * (box[3] / height)   ),
				]







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[str]   ):
				A	 =      to_pil_image(_lowerCAmelCase   )
				A ,		A	 =      pil_image.size
				A	 =      pytesseract.image_to_data(_lowerCAmelCase,						lang=_lowerCAmelCase,						output_type='dict',						config=_lowerCAmelCase   )
				A ,		A ,		A ,		A ,		A	 =      data['text'], data['left'], data['top'], data['width'], data['height']

				# filter empty words and corresponding coordinates
				A	 =      [idx for idx, word in enumerate(_lowerCAmelCase   ) if not word.strip()]
				A	 =      [word for idx, word in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
				A	 =      [coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
				A	 =      [coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
				A	 =      [coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]
				A	 =      [coord for idx, coord in enumerate(_lowerCAmelCase   ) if idx not in irrelevant_indices]

				# turn coordinates into (left, top, left+width, top+height) format
				A	 =      []
				for x, y, w, h in zip(_lowerCAmelCase,						_lowerCAmelCase,						_lowerCAmelCase,						_lowerCAmelCase   ):
								A	 =      [x, y, x + w, y + h]
								actual_boxes.append(_lowerCAmelCase   )

				# finally, normalize the bounding boxes
				A	 =      []
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(_lowerCAmelCase,						_lowerCAmelCase,						_lowerCAmelCase   )   )

				assert len(_lowerCAmelCase   ) == len(_lowerCAmelCase   ), "Not as many words as there are bounding boxes"

				return words, normalized_boxes



class      UpperCamelCase (				__UpperCAmelCase ):







		"""simple docstring"""



		snake_case    =   ["pixel_values"]
		def __init__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Dict[str, int] = None						,_SCREAMING_SNAKE_CASE		:       PILImageResampling = PILImageResampling.BILINEAR						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       float = 1 / 2_5_5						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = ""						,**_SCREAMING_SNAKE_CASE		:       Dict						,)    ->   Optional[Any]:


						'''simple docstring'''
						super().__init__(**_lowerCamelCase		)
						A	 =      size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A	 =      get_size_dict(_lowerCamelCase		)

						A	 =      do_resize
						A	 =      size
						A	 =      resample
						A	 =      do_rescale
						A	 =      rescale_value
						A	 =      do_normalize
						A	 =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A	 =      image_std if image_std is not None else IMAGENET_STANDARD_STD
						A	 =      apply_ocr
						A	 =      ocr_lang
						A	 =      tesseract_config
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Dict[str, int]						,_SCREAMING_SNAKE_CASE		:       PILImageResampling = PILImageResampling.BILINEAR						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       List[Any]						,)    ->   Dict:


						'''simple docstring'''
						A	 =      get_size_dict(_lowerCamelCase		)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
						A	 =      (size['height'], size['width'])
						return resize(_lowerCamelCase						,size=_lowerCamelCase						,resample=_lowerCamelCase						,data_format=_lowerCamelCase						,**_lowerCamelCase		)
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Union[int, float]						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   Dict:


						'''simple docstring'''
						return rescale(_lowerCamelCase						,scale=_lowerCamelCase						,data_format=_lowerCamelCase						,**_lowerCamelCase		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       np.ndarray						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]]						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]]						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, ChannelDimension]] = None						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   Dict:


						'''simple docstring'''
						return normalize(_lowerCamelCase						,mean=_lowerCamelCase						,std=_lowerCamelCase						,data_format=_lowerCamelCase						,**_lowerCamelCase		)







		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       ImageInput						,_SCREAMING_SNAKE_CASE		:       bool = None						,_SCREAMING_SNAKE_CASE		:       Dict[str, int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None						,_SCREAMING_SNAKE_CASE		:       bool = None						,_SCREAMING_SNAKE_CASE		:       float = None						,_SCREAMING_SNAKE_CASE		:       bool = None						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       Union[float, Iterable[float]] = None						,_SCREAMING_SNAKE_CASE		:       bool = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,_SCREAMING_SNAKE_CASE		:       ChannelDimension = ChannelDimension.FIRST						,**_SCREAMING_SNAKE_CASE		:       Any						,)    ->   List[str]:


						'''simple docstring'''
						A	 =      do_resize if do_resize is not None else self.do_resize
						A	 =      size if size is not None else self.size
						A	 =      get_size_dict(_lowerCamelCase		)
						A	 =      resample if resample is not None else self.resample
						A	 =      do_rescale if do_rescale is not None else self.do_rescale
						A	 =      rescale_factor if rescale_factor is not None else self.rescale_factor
						A	 =      do_normalize if do_normalize is not None else self.do_normalize
						A	 =      image_mean if image_mean is not None else self.image_mean
						A	 =      image_std if image_std is not None else self.image_std
						A	 =      apply_ocr if apply_ocr is not None else self.apply_ocr
						A	 =      ocr_lang if ocr_lang is not None else self.ocr_lang
						A	 =      tesseract_config if tesseract_config is not None else self.tesseract_config

						A	 =      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:
										raise ValueError('Size must be specified if do_resize is True.'		)

						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('If do_normalize is True, image_mean and image_std must be specified.'		)

						# All transformations expect numpy arrays.
						A	 =      [to_numpy_array(_lowerCamelCase		) for image in images]

						# Tesseract OCR to get words + normalized bounding boxes
						if apply_ocr:
										requires_backends(self						,'pytesseract'		)
										A	 =      []
										A	 =      []
										for image in images:
														A ,		A	 =      apply_tesseract(_lowerCamelCase						,_lowerCamelCase						,_lowerCamelCase		)
														words_batch.append(_lowerCamelCase		)
														boxes_batch.append(_lowerCamelCase		)

						if do_resize:
										A	 =      [self.resize(image=_lowerCamelCase						,size=_lowerCamelCase						,resample=_lowerCamelCase		) for image in images]

						if do_rescale:
										A	 =      [self.rescale(image=_lowerCamelCase						,scale=_lowerCamelCase		) for image in images]

						if do_normalize:
										A	 =      [self.normalize(image=_lowerCamelCase						,mean=_lowerCamelCase						,std=_lowerCamelCase		) for image in images]

						A	 =      [to_channel_dimension_format(_lowerCamelCase						,_lowerCamelCase		) for image in images]

						A	 =      BatchFeature(data={'pixel_values': images}						,tensor_type=_lowerCamelCase		)

						if apply_ocr:
										A	 =      words_batch
										A	 =      boxes_batch
						return data













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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "LayoutLMv2ImageProcessor"
		snake_case    =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''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						,)
										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__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,_SCREAMING_SNAKE_CASE		:       Union[List[List[int]], List[List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[List[int], List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   BatchEncoding:


						'''simple docstring'''
						# 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=_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		):
														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=_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
						A	 =      features.pop('pixel_values'		)
						if return_overflowing_tokens is True:
										A	 =      self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,encoded_inputs['overflow_to_sample_mapping']		)
						A	 =      images

						return encoded_inputs
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Dict:


						'''simple docstring'''
						# 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(_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       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       A(  self		:       Dict		)    ->   int:


						'''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       A(  self		:       Optional[int]		)    ->   Union[str, 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





