


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       		=    logging.get_logger(__name__)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[int]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







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

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

						# finally, normalize the bounding boxes
						__UpperCAmelCase			:							Tuple	    =	[]
						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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						['''pixel_values''']


	def __init__(    self				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = PILImageResampling.BILINEAR				,     lowercase__ = True				,     lowercase__ = 1 / 2_5_5				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = ""				,     **lowercase__				,     ):
							super().__init__(**lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase			:							List[Any]	    =	get_size_dict(lowercase__)

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


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = PILImageResampling.BILINEAR				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__)
							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			:							Optional[Any]	    =	(size['''height'''], size['''width'''])
							return resize(lowercase__				,     size=lowercase__				,     resample=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return rescale(lowercase__				,     scale=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return normalize(lowercase__				,     mean=lowercase__				,     std=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__=None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase			:							List[Any]	    =	size if size is not None else self.size
							__UpperCAmelCase			:							Optional[Any]	    =	get_size_dict(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	resample if resample is not None else self.resample
							__UpperCAmelCase			:							Tuple	    =	do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase			:							int	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase			:							List[str]	    =	do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase			:							int	    =	image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase			:							List[Any]	    =	image_std if image_std is not None else self.image_std
							__UpperCAmelCase			:							Tuple	    =	apply_ocr if apply_ocr is not None else self.apply_ocr
							__UpperCAmelCase			:							List[Any]	    =	ocr_lang if ocr_lang is not None else self.ocr_lang
							__UpperCAmelCase			:							List[Any]	    =	tesseract_config if tesseract_config is not None else self.tesseract_config

							__UpperCAmelCase			:							List[str]	    =	make_list_of_images(lowercase__)

							if not valid_images(lowercase__):
													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			:							List[str]	    =	[to_numpy_array(lowercase__) for image in images]

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

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

							if do_rescale:
													__UpperCAmelCase			:							Optional[int]	    =	[self.rescale(image=lowercase__				,     scale=lowercase__) for image in images]

							if do_normalize:
													__UpperCAmelCase			:							Union[str, Any]	    =	[self.normalize(image=lowercase__				,     mean=lowercase__				,     std=lowercase__) for image in images]

							__UpperCAmelCase			:							Union[str, Any]	    =	[to_channel_dimension_format(lowercase__				,     lowercase__) for image in images]

							__UpperCAmelCase			:							Optional[int]	    =	BatchFeature(data={'''pixel_values''': images}				,     tensor_type=lowercase__)

							if apply_ocr:
													__UpperCAmelCase			:							str	    =	words_batch
													__UpperCAmelCase			:							Optional[Any]	    =	boxes_batch
							return data





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						return 1 if input_a == input_a else 0




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						assert xnor_gate(0      ,       0      ) == 1
						assert xnor_gate(0      ,       1      ) == 0
						assert xnor_gate(1      ,       0      ) == 0
						assert xnor_gate(1      ,       1      ) == 1


if __name__ == "__main__":
				print(xnor_gate(0, 0))
				print(xnor_gate(0, 1))
				print(xnor_gate(1, 0))
				print(xnor_gate(1, 1))

