





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

UpperCamelCase__											= logging.get_logger(__name__)





def     _UpperCamelCase     (a__   :Optional[Any]   ,			a__   :int   ,			a__   :Tuple       ):






				"""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     (a__   :Union[str, Any]   ,			a__   :Dict   ,			a__   :str       ):






				"""simple docstring"""





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

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

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

				# finally, normalize the bounding boxes
				UpperCamelCase__				     =			[]
				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        __SCREAMING_SNAKE_CASE					(     _UpperCAmelCase       ):
							snake_case     :      Union[str, Any]						     =       ['''pixel_values''']






							def __init__(						self  , __lowerCAmelCase = True  , __lowerCAmelCase = None  , __lowerCAmelCase = PILImageResampling.BILINEAR  , __lowerCAmelCase = True  , __lowerCAmelCase = 1 / 255  , __lowerCAmelCase = True  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = True  , __lowerCAmelCase = None  , __lowerCAmelCase = ""  , **__lowerCAmelCase  , ):
											super().__init__(**A_  )
											UpperCamelCase__				     =			size if size is not None else {"""height""": 224, """width""": 224}
											UpperCamelCase__				     =			get_size_dict(A_  )

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = PILImageResampling.BILINEAR  , __lowerCAmelCase = None  , **__lowerCAmelCase  , ):
											UpperCamelCase__				     =			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()}"""  )
											UpperCamelCase__				     =			(size["""height"""], size["""width"""])
											return resize(A_  , size=A_  , resample=A_  , data_format=A_  , **A_  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  , **__lowerCAmelCase  , ):
											return rescale(A_  , scale=A_  , data_format=A_  , **A_  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  , **__lowerCAmelCase  , ):
											return normalize(A_  , mean=A_  , std=A_  , data_format=A_  , **A_  )







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase=None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = ChannelDimension.FIRST  , **__lowerCAmelCase  , ):
											UpperCamelCase__				     =			do_resize if do_resize is not None else self.do_resize
											UpperCamelCase__				     =			size if size is not None else self.size
											UpperCamelCase__				     =			get_size_dict(A_  )
											UpperCamelCase__				     =			resample if resample is not None else self.resample
											UpperCamelCase__				     =			do_rescale if do_rescale is not None else self.do_rescale
											UpperCamelCase__				     =			rescale_factor if rescale_factor is not None else self.rescale_factor
											UpperCamelCase__				     =			do_normalize if do_normalize is not None else self.do_normalize
											UpperCamelCase__				     =			image_mean if image_mean is not None else self.image_mean
											UpperCamelCase__				     =			image_std if image_std is not None else self.image_std
											UpperCamelCase__				     =			apply_ocr if apply_ocr is not None else self.apply_ocr
											UpperCamelCase__				     =			ocr_lang if ocr_lang is not None else self.ocr_lang
											UpperCamelCase__				     =			tesseract_config if tesseract_config is not None else self.tesseract_config

											UpperCamelCase__				     =			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.
											UpperCamelCase__				     =			[to_numpy_array(A_  ) for image in images]

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

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

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

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

											UpperCamelCase__				     =			[to_channel_dimension_format(A_  , A_  ) for image in images]

											UpperCamelCase__				     =			BatchFeature(data={"""pixel_values""": images}  , tensor_type=A_  )

											if apply_ocr:
															UpperCamelCase__				     =			words_batch
															UpperCamelCase__				     =			boxes_batch
											return data











def     _UpperCamelCase     (a__   :int       ):






							"""simple docstring"""





							if divisor % 5 == 0 or divisor % 2 == 0:
														return 0
							UpperCamelCase__				     =			1
							UpperCamelCase__				     =			1
							while repunit:
														UpperCamelCase__				     =			(10 * repunit + 1) % divisor
														repunit_index += 1
							return repunit_index





def     _UpperCamelCase     (a__   :int = 100_0000       ):






							"""simple docstring"""





							UpperCamelCase__				     =			limit - 1
							if divisor % 2 == 0:
														divisor += 1
							while least_divisible_repunit(a__       ) <= limit:
														divisor += 2
							return divisor


if __name__ == "__main__":
					print(f"""{solution() = }""")




