



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

__lowerCamelCase  :				Dict					=					logging.get_logger(__name__)

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     , lowerCamelCase_     )			->							Dict:
						return [
						    int(1_000 * (box[0] / width)     ),
						    int(1_000 * (box[1] / height)     ),
						    int(1_000 * (box[2] / width)     ),
						    int(1_000 * (box[3] / height)     ),
						]

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     , lowerCamelCase_     )			->							Union[str, Any]:
						UpperCAmelCase								=  to_pil_image(_UpperCamelCase     )
						UpperCAmelCase							,		UpperCAmelCase								=  pil_image.size
						UpperCAmelCase								=  pytesseract.image_to_data(_UpperCamelCase     , lang=_UpperCamelCase     , output_type="dict"     , config=_UpperCamelCase     )
						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(_UpperCamelCase     ) if not word.strip()]
						UpperCAmelCase								=  [word for idx, word in enumerate(_UpperCamelCase     ) if idx not in irrelevant_indices]
						UpperCAmelCase								=  [coord for idx, coord in enumerate(_UpperCamelCase     ) if idx not in irrelevant_indices]
						UpperCAmelCase								=  [coord for idx, coord in enumerate(_UpperCamelCase     ) if idx not in irrelevant_indices]
						UpperCAmelCase								=  [coord for idx, coord in enumerate(_UpperCamelCase     ) if idx not in irrelevant_indices]
						UpperCAmelCase								=  [coord for idx, coord in enumerate(_UpperCamelCase     ) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						UpperCAmelCase								=  []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(_UpperCamelCase     , _UpperCamelCase     , _UpperCamelCase     )     )

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

						return words, normalized_boxes





class 			__magic_name__ (				lowerCAmelCase__    ):
		lowercase   :							Optional[Any]        =["""pixel_values"""]






		def __init__(    self   :  Optional[Any]					,		UpperCamelCase__   :  List[Any] = True					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  Optional[Any] = PILImageResampling.BILINEAR					,		UpperCamelCase__   :  Optional[int] = True					,		UpperCamelCase__   :  Optional[Any] = 1 / 2_55					,		UpperCamelCase__   :  int = True					,		UpperCamelCase__   :  Tuple = None					,		UpperCamelCase__   :  Dict = None					,		UpperCamelCase__   :  Dict = True					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  List[str] = ""					,		**UpperCamelCase__   :  Optional[Any]					,		)					->       int:
								'''simple docstring'''







								super().__init__(**__a   )
								UpperCAmelCase								=  size if size is not None else {"height": 2_24, "width": 2_24}
								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 SCREAMING_SNAKE_CASE_      (    self   :  List[Any]					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  List[Any] = PILImageResampling.BILINEAR					,		UpperCamelCase__   :  Tuple = None					,		**UpperCamelCase__   :  int					,		)					->       Union[str, Any]:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Any = None					,		**UpperCamelCase__   :  str					,		)					->       List[str]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  int					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Dict = None					,		**UpperCamelCase__   :  Optional[int]					,		)					->       Dict:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  Optional[int] = None					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  int=None					,		UpperCamelCase__   :  Optional[int] = None					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  List[Any] = None					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  Dict = None					,		UpperCamelCase__   :  int = None					,		UpperCamelCase__   :  str = None					,		UpperCamelCase__   :  Optional[Any] = None					,		UpperCamelCase__   :  Optional[int] = None					,		UpperCamelCase__   :  List[str] = ChannelDimension.FIRST					,		**UpperCamelCase__   :  List[str]					,		)					->       int:
								'''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(__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







"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




