

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

_a:    Optional[int] 							=			logging.get_logger(__name__)



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



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
 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 						__UpperCamelCase    (					lowercase						):
 SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




 def __init__(		self	:						Tuple		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						float = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[str] = ""		,	**lowerCAmelCase	:						Union[str, Any]		,	):


  '''simple docstring'''







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

  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       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


  '''simple docstring'''







  UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase							)
  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(lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase		,	data_format=lowerCAmelCase		,	**lowerCAmelCase							)




 def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


  '''simple docstring'''







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




 def       __A					(		self	:						Any		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, Iterable[float]]		,	lowerCAmelCase	:						Union[float, Iterable[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						str		,	):


  '''simple docstring'''







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




 def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						Union[float, Iterable[float]] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						List[Any]		,	):


  '''simple docstring'''







  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(lowerCAmelCase							)
  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(lowerCAmelCase							)

  if not valid_images(lowerCAmelCase							):
   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(lowerCAmelCase							) 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(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
    words_batch.append(lowerCAmelCase							)
    boxes_batch.append(lowerCAmelCase							)

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

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

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

  UpperCAmelCase_       				=			[to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							) for image in images]

  UpperCAmelCase_       				=			BatchFeature(data={"pixel_values": images}		,	tensor_type=lowerCAmelCase							)

  if apply_ocr:
   UpperCAmelCase_       				=			words_batch
   UpperCAmelCase_       				=			boxes_batch
  return data


from collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


if __name__ == "__main__":
				from doctest import testmod

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')