
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

SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[Any]:







				"""simple docstring"""



				return [
				    int(1_0_0_0 * (box[0] / width)						),
				    int(1_0_0_0 * (box[1] / height)						),
				    int(1_0_0_0 * (box[2] / width)						),
				    int(1_0_0_0 * (box[3] / height)						),
				]


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= to_pil_image(a_						)
				__A			,							__A   			= pil_image.size
				__A   			= pytesseract.image_to_data(a_		,   lang=a_		,   output_type="dict"		,   config=a_						)
				__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(a_						) if not word.strip()]
				__A   			= [word for idx, word in enumerate(a_						) if idx not in irrelevant_indices]
				__A   			= [coord for idx, coord in enumerate(a_						) if idx not in irrelevant_indices]
				__A   			= [coord for idx, coord in enumerate(a_						) if idx not in irrelevant_indices]
				__A   			= [coord for idx, coord in enumerate(a_						) if idx not in irrelevant_indices]
				__A   			= [coord for idx, coord in enumerate(a_						) if idx not in irrelevant_indices]

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

				# finally, normalize the bounding boxes
				__A   			= []
				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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["pixel_values"]






	def __init__(       self					: Union[str, Any]							,A					: bool = True							,A					: Dict[str, int] = None							,A					: PILImageResampling = PILImageResampling.BILINEAR							,A					: bool = True							,A					: float = 1 / 2_55							,A					: bool = True							,A					: Union[float, Iterable[float]] = None							,A					: Union[float, Iterable[float]] = None							,A					: bool = True							,A					: Optional[str] = None							,A					: Optional[str] = ""							,**A					: Optional[Any]							,):
					super().__init__(**A )
					__A   			= size if size is not None else {"height": 2_24, "width": 2_24}
					__A   			= get_size_dict(A )

					__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 				UpperCamelCase_  (       self					: Any							,A					: np.ndarray							,A					: Dict[str, int]							,A					: PILImageResampling = PILImageResampling.BILINEAR							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[str]							,):
					__A   			= 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   			= (size["height"], size["width"])
					return resize(A							,size=A							,resample=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Tuple							,A					: np.ndarray							,A					: Union[int, float]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[Any]							,):
					return rescale(A							,scale=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: np.ndarray							,A					: Union[float, Iterable[float]]							,A					: Union[float, Iterable[float]]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[Any]							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A )







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

					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(A ) 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(A							,A							,A )
													words_batch.append(A )
													boxes_batch.append(A )

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

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

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

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

					__A   			= BatchFeature(data={"pixel_values": images}							,tensor_type=A )

					if apply_ocr:
									__A   			= words_batch
									__A   			= boxes_batch
					return data

def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		while b:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    b, a % b
		return a




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase   , a % b	)




def       _snake_case	(   ):
		"""simple docstring"""







		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3   , 5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5   , 3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1   , 3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3   , 6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6   , 3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3   , 5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5   , 3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1   , 3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3   , 6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6   , 3	)}'	)


if __name__ == "__main__":
		main()
