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__		:					List[Any]						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			str     ,  lowercase__  :			int     ,  lowercase__  :			Any ):







					'''simple docstring'''


					return [
					    int(10_00 * (box[0] / width) ),
					    int(10_00 * (box[1] / height) ),
					    int(10_00 * (box[2] / width) ),
					    int(10_00 * (box[3] / height) ),
					]







def     _a				(      lowercase__  :			np.ndarray     ,  lowercase__  :			Optional[str]     ,  lowercase__  :			Optional[str] ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				List[str]			=		to_pil_image(lowercase__ )
					SCREAMING_SNAKE_CASE__				:				List[Any]			=		pil_image.size
					SCREAMING_SNAKE_CASE__				:				Dict			=		pytesseract.image_to_data(lowercase__     ,  lang=lowercase__     ,  output_type='dict'     ,  config=lowercase__ )
					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		data['text'], data['left'], data['top'], data['width'], data['height']

					# filter empty words and corresponding coordinates
					SCREAMING_SNAKE_CASE__				:				List[str]			=		[idx for idx, word in enumerate(lowercase__ ) if not word.strip()]
					SCREAMING_SNAKE_CASE__				:				Any			=		[word for idx, word in enumerate(lowercase__ ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE__				:				str			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE__				:				List[str]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE__				:				List[str]			=		[coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]

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

					# finally, normalize the bounding boxes
					SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
					for box in actual_boxes:
										normalized_boxes.append(normalize_box(lowercase__     ,  lowercase__     ,  lowercase__ ) )

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

					return words, normalized_boxes







class       snake_case					(      UpperCamelCase_						):
		lowercase_			=     ['pixel_values']




		def __init__(      self     :	List[Any]     , a_     :	bool = True     , a_     :	Dict[str, int] = None     , a_     :	PILImageResampling = PILImageResampling.BILINEAR     , a_     :	bool = True     , a_     :	float = 1 / 255     , 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_     :	Union[str, Any]     , )->       None:




							"""simple docstring"""
							super().__init__(**a_				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		size if size is not None else {'height': 224, 'width': 224}
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		get_size_dict(a_				)

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




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				int			=		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()}'''				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		(size['height'], size['width'])
							return resize(a_     , size=a_     , resample=a_     , data_format=a_     , **a_				)




		def 	__lowercase(      self     :	List[Any]     , a_     :	np.ndarray     , a_     :	Union[int, float]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	List[Any]     , )->       np.ndarray:




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




		def 	__lowercase(      self     :	Any     , a_     :	np.ndarray     , a_     :	Union[float, Iterable[float]]     , a_     :	Union[float, Iterable[float]]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	Tuple     , )->       np.ndarray:




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




		def 	__lowercase(      self     :	int     , a_     :	ImageInput     , a_     :	bool = None     , a_     :	Dict[str, int] = None     , a_     :	Union[str, Any]=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_     :	List[str]     , )->       PIL.Image.Image:




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

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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.
							SCREAMING_SNAKE_CASE__				:				Any			=		[to_numpy_array(a_				) for image in images]

							# Tesseract OCR to get words + normalized bounding boxes
							if apply_ocr:
												requires_backends(self     , 'pytesseract'				)
												SCREAMING_SNAKE_CASE__				:				str			=		[]
												SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
												for image in images:
																	SCREAMING_SNAKE_CASE__				:				Tuple			=		apply_tesseract(a_     , a_     , a_				)
																	words_batch.append(a_				)
																	boxes_batch.append(a_				)

							if do_resize:
												SCREAMING_SNAKE_CASE__				:				Tuple			=		[self.resize(image=a_     , size=a_     , resample=a_				) for image in images]

							if do_rescale:
												SCREAMING_SNAKE_CASE__				:				List[str]			=		[self.rescale(image=a_     , scale=a_				) for image in images]

							if do_normalize:
												SCREAMING_SNAKE_CASE__				:				str			=		[self.normalize(image=a_     , mean=a_     , std=a_				) for image in images]

							SCREAMING_SNAKE_CASE__				:				Dict			=		[to_channel_dimension_format(a_     , a_				) for image in images]

							SCREAMING_SNAKE_CASE__				:				Any			=		BatchFeature(data={'pixel_values': images}     , tensor_type=a_				)

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




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



