





from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

UpperCAmelCase						:   List[str]			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Any:
					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  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a = None  )      ->      Optional[int]:
					__A	:						Tuple       =				tesseract_config if tesseract_config is not None else ''

					# apply OCR
					__A	:						Optional[Any]       =				to_pil_image(a  )
					__A    ,				__A	:						Dict       =				pil_image.size
					__A	:						Dict       =				pytesseract.image_to_data(a					,						lang=a					,						output_type='dict'					,						config=a  )
					__A    ,				__A    ,				__A    ,				__A    ,				__A	:						List[str]       =				data['text'], data['left'], data['top'], data['width'], data['height']

					# filter empty words and corresponding coordinates
					__A	:						Any       =				[idx for idx, word in enumerate(a  ) if not word.strip()]
					__A	:						Optional[int]       =				[word for idx, word in enumerate(a  ) if idx not in irrelevant_indices]
					__A	:						List[Any]       =				[coord for idx, coord in enumerate(a  ) if idx not in irrelevant_indices]
					__A	:						Union[str, Any]       =				[coord for idx, coord in enumerate(a  ) if idx not in irrelevant_indices]
					__A	:						List[Any]       =				[coord for idx, coord in enumerate(a  ) if idx not in irrelevant_indices]
					__A	:						Optional[Any]       =				[coord for idx, coord in enumerate(a  ) if idx not in irrelevant_indices]

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

					# finally, normalize the bounding boxes
					__A	:						List[Any]       =				[]
					for box in actual_boxes:
										normalized_boxes.append(normalize_box(a					,						a					,						a  )  )

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

					return words, normalized_boxes





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Union[str, Any]					    =	['''pixel_values''']



				def __init__(			self     ,					_A = True     ,					_A = None     ,					_A = PILImageResampling.BILINEAR     ,					_A = True     ,					_A = None     ,					_A = ""     ,					**_A     ,					):
									super().__init__(**_A     )
									__A	:						int       =				size if size is not None else {'height': 224, 'width': 224}
									__A	:						List[str]       =				get_size_dict(_A     )

									__A	:						Optional[int]       =				do_resize
									__A	:						Dict       =				size
									__A	:						int       =				resample
									__A	:						Optional[int]       =				apply_ocr
									__A	:						List[str]       =				ocr_lang
									__A	:						Union[str, Any]       =				tesseract_config



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A = PILImageResampling.BILINEAR     ,					_A = None     ,					**_A     ,					):
									__A	:						List[str]       =				get_size_dict(_A     )
									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	:						Any       =				(size['height'], size['width'])
									return resize(_A     ,					size=_A     ,					resample=_A     ,					data_format=_A     ,					**_A     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = ChannelDimension.FIRST     ,					**_A     ,					):
									__A	:						Tuple       =				do_resize if do_resize is not None else self.do_resize
									__A	:						int       =				size if size is not None else self.size
									__A	:						Any       =				get_size_dict(_A     )
									__A	:						Union[str, Any]       =				resample if resample is not None else self.resample
									__A	:						List[str]       =				apply_ocr if apply_ocr is not None else self.apply_ocr
									__A	:						Dict       =				ocr_lang if ocr_lang is not None else self.ocr_lang
									__A	:						str       =				tesseract_config if tesseract_config is not None else self.tesseract_config

									__A	:						Any       =				make_list_of_images(_A     )

									if not valid_images(_A     ):
														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.'     )

									# All transformations expect numpy arrays.
									__A	:						Any       =				[to_numpy_array(_A     ) for image in images]

									if apply_ocr:
														requires_backends(self     ,					'pytesseract'     )
														__A	:						Any       =				[]
														__A	:						List[Any]       =				[]
														for image in images:
																			__A    ,				__A	:						Dict       =				apply_tesseract(_A     ,					_A     ,					_A     )
																			words_batch.append(_A     )
																			boxes_batch.append(_A     )

									if do_resize:
														__A	:						int       =				[self.resize(image=_A     ,					size=_A     ,					resample=_A     ) for image in images]

									# flip color channels from RGB to BGR (as Detectron2 requires this)
									__A	:						Tuple       =				[flip_channel_order(_A     ) for image in images]
									__A	:						Dict       =				[to_channel_dimension_format(_A     ,					_A     ) for image in images]

									__A	:						Union[str, Any]       =				BatchFeature(data={'pixel_values': images}     ,					tensor_type=_A     )

									if apply_ocr:
														__A	:						Dict       =				words_batch
														__A	:						Any       =				boxes_batch
									return data











def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




