





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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> 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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   Any  				=     to_pil_image(__a				)
							lowercase_				,		lowercase_	:   Optional[Any]  				=     pil_image.size
							lowercase_	:   List[str]  				=     pytesseract.image_to_data(__a				,       lang=__a				,       output_type="""dict"""				,       config=__a				)
							lowercase_				,		lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   List[str]  				=     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

							# filter empty words and corresponding coordinates
							lowercase_	:   Dict  				=     [idx for idx, word in enumerate(__a				) if not word.strip()]
							lowercase_	:   Optional[int]  				=     [word for idx, word in enumerate(__a				) if idx not in irrelevant_indices]
							lowercase_	:   Any  				=     [coord for idx, coord in enumerate(__a				) if idx not in irrelevant_indices]
							lowercase_	:   Optional[int]  				=     [coord for idx, coord in enumerate(__a				) if idx not in irrelevant_indices]
							lowercase_	:   Dict  				=     [coord for idx, coord in enumerate(__a				) if idx not in irrelevant_indices]
							lowercase_	:   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
							lowercase_	:   Optional[int]  				=     []
							for x, y, w, h in zip(__a				,       __a				,       __a				,       __a				):
														lowercase_	:   str  				=     [x, y, x + w, y + h]
														actual_boxes.append(__a				)

							# finally, normalize the bounding boxes
							lowercase_	:   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     UpperCamelCase__					(     __lowercase				):






		'''simple docstring'''






		__a       :       Optional[int]       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   snake_case__ = True,   snake_case__ = None,   snake_case__ = "",   **snake_case__,   )       -> Optional[Any]:






									"""simple docstring"""
									super().__init__(**SCREAMING_SNAKE_CASE_  )
									lowercase_	:   Any  				=     size if size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Dict  				=     get_size_dict(SCREAMING_SNAKE_CASE_  )

									lowercase_	:   List[str]  				=     do_resize
									lowercase_	:   Optional[int]  				=     size
									lowercase_	:   Optional[Any]  				=     resample
									lowercase_	:   List[Any]  				=     do_rescale
									lowercase_	:   Optional[Any]  				=     rescale_value
									lowercase_	:   List[Any]  				=     do_normalize
									lowercase_	:   Tuple  				=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase_	:   Optional[Any]  				=     image_std if image_std is not None else IMAGENET_STANDARD_STD
									lowercase_	:   Tuple  				=     apply_ocr
									lowercase_	:   Optional[Any]  				=     ocr_lang
									lowercase_	:   str  				=     tesseract_config






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     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_	:   Optional[Any]  				=     (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 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> Dict:






									"""simple docstring"""
									return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__=None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     do_resize if do_resize is not None else self.do_resize
									lowercase_	:   int  				=     size if size is not None else self.size
									lowercase_	:   int  				=     get_size_dict(SCREAMING_SNAKE_CASE_  )
									lowercase_	:   Any  				=     resample if resample is not None else self.resample
									lowercase_	:   int  				=     do_rescale if do_rescale is not None else self.do_rescale
									lowercase_	:   Optional[int]  				=     rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase_	:   Union[str, Any]  				=     do_normalize if do_normalize is not None else self.do_normalize
									lowercase_	:   Optional[Any]  				=     image_mean if image_mean is not None else self.image_mean
									lowercase_	:   List[Any]  				=     image_std if image_std is not None else self.image_std
									lowercase_	:   Tuple  				=     apply_ocr if apply_ocr is not None else self.apply_ocr
									lowercase_	:   Optional[Any]  				=     ocr_lang if ocr_lang is not None else self.ocr_lang
									lowercase_	:   Tuple  				=     tesseract_config if tesseract_config is not None else self.tesseract_config

									lowercase_	:   Union[str, Any]  				=     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."""  )

									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.
									lowercase_	:   Union[str, Any]  				=     [to_numpy_array(SCREAMING_SNAKE_CASE_  ) for image in images]

									# Tesseract OCR to get words + normalized bounding boxes
									if apply_ocr:
																requires_backends(self,   """pytesseract"""  )
																lowercase_	:   Dict  				=     []
																lowercase_	:   Dict  				=     []
																for image in images:
																							lowercase_				,		lowercase_	:   Tuple  				=     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_	:   int  				=     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_  ) for image in images]

									if do_rescale:
																lowercase_	:   Dict  				=     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_  ) for image in images]

									if do_normalize:
																lowercase_	:   Dict  				=     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_  ) for image in images]

									lowercase_	:   List[Any]  				=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_  ) for image in images]

									lowercase_	:   Dict  				=     BatchFeature(data={"""pixel_values""": images},   tensor_type=SCREAMING_SNAKE_CASE_  )

									if apply_ocr:
																lowercase_	:   Optional[Any]  				=     words_batch
																lowercase_	:   int  				=     boxes_batch
									return data

"""simple docstring"""




def 							_lowerCamelCase			(			__a     ):
		if divisor % 5 == 0 or divisor % 2 == 0:
				return 0
		SCREAMING_SNAKE_CASE_    			=			1
		SCREAMING_SNAKE_CASE_    			=			1
		while repunit:
				SCREAMING_SNAKE_CASE_    			=			(10 * repunit + 1) % divisor
				repunit_index += 1
		return repunit_index



def 							_lowerCamelCase			(			__a = 1_000_000     ):
		SCREAMING_SNAKE_CASE_    			=			limit - 1
		if divisor % 2 == 0:
				divisor += 1
		while least_divisible_repunit(__a     ) <= limit:
				divisor += 2
		return divisor


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