


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

A	:		Tuple    =       logging.get_logger(__name__)






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Dict   ,   __magic_name__					:							str					)       ->  Any:
		"""simple docstring"""




		return [
		    int(1000 * (box[0] / width)					),
		    int(1000 * (box[1] / height)					),
		    int(1000 * (box[2] / width)					),
		    int(1000 * (box[3] / height)					),
		]






def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							List[Any] = None					)       ->  Tuple:
		"""simple docstring"""




		lowercase__					      =		tesseract_config if tesseract_config is not None else ""

		# apply OCR
		lowercase__					      =		to_pil_image(__snake_case					)
		lowercase__					      =		pil_image.size
		lowercase__					      =		pytesseract.image_to_data(__snake_case   ,   lang=__snake_case   ,   output_type="""dict"""   ,   config=__snake_case					)
		lowercase__					      =		data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

		# finally, normalize the bounding boxes
		lowercase__					      =		[]
		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 							A   (	__SCREAMING_SNAKE_CASE							):
					'''simple docstring'''






					A__      =			['''pixel_values''']

					def __init__(self   :						List[str]				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Dict[str, int] = None				, _UpperCAmelCase   :						PILImageResampling = PILImageResampling.BILINEAR				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[str] = ""				, **_UpperCAmelCase   :						Union[str, Any]				, )						->       None:




							"""simple docstring"""


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

							lowercase__					      =		do_resize
							lowercase__					      =		size
							lowercase__					      =		resample
							lowercase__					      =		apply_ocr
							lowercase__					      =		ocr_lang
							lowercase__					      =		tesseract_config

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						np.ndarray				, _UpperCAmelCase   :						Dict[str, int]				, _UpperCAmelCase   :						PILImageResampling = PILImageResampling.BILINEAR				, _UpperCAmelCase   :						Optional[Union[str, ChannelDimension]] = None				, **_UpperCAmelCase   :						Any				, )						->       np.ndarray:




							"""simple docstring"""


							lowercase__					      =		get_size_dict(_SCREAMING_SNAKE_CASE		)
							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()}'''		)
							lowercase__					      =		(size["height"], size["width"])
							return resize(_SCREAMING_SNAKE_CASE				, size=_SCREAMING_SNAKE_CASE				, resample=_SCREAMING_SNAKE_CASE				, data_format=_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE		)


					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						ImageInput				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						Dict[str, int] = None				, _UpperCAmelCase   :						PILImageResampling = None				, _UpperCAmelCase   :						bool = None				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[Union[str, TensorType]] = None				, _UpperCAmelCase   :						ChannelDimension = ChannelDimension.FIRST				, **_UpperCAmelCase   :						List[str]				, )						->       PIL.Image.Image:




							"""simple docstring"""


							lowercase__					      =		do_resize if do_resize is not None else self.do_resize
							lowercase__					      =		size if size is not None else self.size
							lowercase__					      =		get_size_dict(_SCREAMING_SNAKE_CASE		)
							lowercase__					      =		resample if resample is not None else self.resample
							lowercase__					      =		apply_ocr if apply_ocr is not None else self.apply_ocr
							lowercase__					      =		ocr_lang if ocr_lang is not None else self.ocr_lang
							lowercase__					      =		tesseract_config if tesseract_config is not None else self.tesseract_config

							lowercase__					      =		make_list_of_images(_SCREAMING_SNAKE_CASE		)

							if not valid_images(_SCREAMING_SNAKE_CASE		):
									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.
							lowercase__					      =		[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

							if apply_ocr:
									requires_backends(self				, """pytesseract"""		)
									lowercase__					      =		[]
									lowercase__					      =		[]
									for image in images:
											lowercase__					      =		apply_tesseract(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE		)
											words_batch.append(_SCREAMING_SNAKE_CASE		)
											boxes_batch.append(_SCREAMING_SNAKE_CASE		)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							lowercase__					      =		[flip_channel_order(_SCREAMING_SNAKE_CASE		) for image in images]
							lowercase__					      =		[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE		) for image in images]

							lowercase__					      =		BatchFeature(data={"""pixel_values""": images}				, tensor_type=_SCREAMING_SNAKE_CASE		)

							if apply_ocr:
									lowercase__					      =		words_batch
									lowercase__					      =		boxes_batch
							return data








from functools import lru_cache






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  set:
		"""simple docstring"""




		lowercase__					      =		2
		lowercase__					      =		set()
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.add(__magic_name__					)
		if n > 1:
				factors.add(__magic_name__					)
		return factors






@lru_cache
def 			UpperCamelCase (     __magic_name__					:							int					)       ->  int:
		"""simple docstring"""




		return len(unique_prime_factors(__magic_name__					)					)






def 			UpperCamelCase (     __magic_name__					:							list					)       ->  bool:
		"""simple docstring"""




		return len(set(__magic_name__					)					) in (0, 1)






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  list:
		"""simple docstring"""




		lowercase__					      =		2

		while True:
				# Increment each value of a generated range
				lowercase__					      =		[base + i for i in range(__magic_name__					)]

				# Run elements through out unique_prime_factors function
				# Append our target number to the end.
				lowercase__					      =		[upf_len(__magic_name__					) for x in group]
				checker.append(__magic_name__					)

				# If all numbers in the list are equal, return the group variable.
				if equality(__magic_name__					):
						return group

				# Increment our base variable by 1
				base += 1






def 			UpperCamelCase (     __magic_name__					:							int = 4					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		run(__magic_name__					)
		return results[0] if len(__magic_name__					) else None


if __name__ == "__main__":
	print(solution())

