




'''simple docstring'''





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							:       Tuple						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Any							,    snake_case							:	List[str]					):

							'''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_(     snake_case							:	np.ndarray							,    snake_case							:	Optional[str]							,    snake_case							:	Optional[str]					):

							'''simple docstring'''






							snake_case_      =				to_pil_image(snake_case					)
							snake_case_ ,				snake_case_      =				pil_image.size
							snake_case_      =				pytesseract.image_to_data(snake_case							,    lang=snake_case							,    output_type="dict"							,    config=snake_case					)
							snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

							return words, normalized_boxes




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Tuple							=   ["pixel_values"]




			def __init__( self    ,							a__ = True    ,							a__ = None    ,							a__ = PILImageResampling.BILINEAR    ,							a__ = True    ,							a__ = 1 / 255    ,							a__ = True    ,							a__ = None    ,							a__ = None    ,							a__ = True    ,							a__ = None    ,							a__ = ""    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										super().__init__(**a__							)
										snake_case_      =				size if size is not None else {"height": 224, "width": 224}
										snake_case_      =				get_size_dict(a__							)

										snake_case_      =				do_resize
										snake_case_      =				size
										snake_case_      =				resample
										snake_case_      =				do_rescale
										snake_case_      =				rescale_value
										snake_case_      =				do_normalize
										snake_case_      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case_      =				image_std if image_std is not None else IMAGENET_STANDARD_STD
										snake_case_      =				apply_ocr
										snake_case_      =				ocr_lang
										snake_case_      =				tesseract_config




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__ = PILImageResampling.BILINEAR    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




										snake_case_      =				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()}'							)
										snake_case_      =				(size["height"], size["width"])
										return resize(a__    ,							size=a__    ,							resample=a__    ,							data_format=a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__ = None    ,							**a__    ,							)    ->    np.ndarray:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = None    ,							a__=None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = None    ,							a__ = ChannelDimension.FIRST    ,							**a__    ,							)    ->    PIL.Image.Image:

										'''simple docstring'''




										snake_case_      =				do_resize if do_resize is not None else self.do_resize
										snake_case_      =				size if size is not None else self.size
										snake_case_      =				get_size_dict(a__							)
										snake_case_      =				resample if resample is not None else self.resample
										snake_case_      =				do_rescale if do_rescale is not None else self.do_rescale
										snake_case_      =				rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case_      =				do_normalize if do_normalize is not None else self.do_normalize
										snake_case_      =				image_mean if image_mean is not None else self.image_mean
										snake_case_      =				image_std if image_std is not None else self.image_std
										snake_case_      =				apply_ocr if apply_ocr is not None else self.apply_ocr
										snake_case_      =				ocr_lang if ocr_lang is not None else self.ocr_lang
										snake_case_      =				tesseract_config if tesseract_config is not None else self.tesseract_config

										snake_case_      =				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.
										snake_case_      =				[to_numpy_array(a__							) for image in images]

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

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

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

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

										snake_case_      =				[to_channel_dimension_format(a__    ,							a__							) for image in images]

										snake_case_      =				BatchFeature(data={"pixel_values": images}    ,							tensor_type=a__							)

										if apply_ocr:
																	snake_case_      =				words_batch
																	snake_case_      =				boxes_batch
										return data










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






							while b:
														snake_case_ ,				snake_case_      =				b, a % b
							return a


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     ):

							'''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()




