






from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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        =			logging.get_logger(__name__)



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		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 					_lowerCamelCase(			lowerCAmelCase__				:		np.ndarray    ,    lowerCAmelCase__				:		Optional[str]    ,    lowerCAmelCase__				:		Optional[str] = None      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tesseract_config if tesseract_config is not None else ''

				# apply OCR
				SCREAMING_SNAKE_CASE_   :	str													=   to_pil_image(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   pil_image.size
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   pytesseract.image_to_data(lowerCAmelCase__    ,    lang=lowerCAmelCase__    ,    output_type='dict'    ,    config=lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   data['text'], data['left'], data['top'], data['width'], data['height']

				# filter empty words and corresponding coordinates
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [idx for idx, word in enumerate(lowerCAmelCase__      ) if not word.strip()]
				SCREAMING_SNAKE_CASE_   :	Dict													=   [word for idx, word in enumerate(lowerCAmelCase__      ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [coord for idx, coord in enumerate(lowerCAmelCase__      ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [coord for idx, coord in enumerate(lowerCAmelCase__      ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [coord for idx, coord in enumerate(lowerCAmelCase__      ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [coord for idx, coord in enumerate(lowerCAmelCase__      ) if idx not in irrelevant_indices]

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

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

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

				return words, normalized_boxes


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[Any]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PILImageResampling.BILINEAR ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "" ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   size if size is not None else {'height': 224, 'width': 224}
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_size_dict(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   do_resize
						SCREAMING_SNAKE_CASE_   :	Tuple													=   size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   resample
						SCREAMING_SNAKE_CASE_   :	Any													=   apply_ocr
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ocr_lang
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tesseract_config


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






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	str													=   do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   size if size is not None else self.size
						SCREAMING_SNAKE_CASE_   :	int													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   apply_ocr if apply_ocr is not None else self.apply_ocr
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ocr_lang if ocr_lang is not None else self.ocr_lang
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tesseract_config if tesseract_config is not None else self.tesseract_config

						SCREAMING_SNAKE_CASE_   :	str													=   make_list_of_images(UpperCamelCase__       )

						if not valid_images(UpperCamelCase__       ):
										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.'       )

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE_   :	Any													=   [to_numpy_array(UpperCamelCase__       ) for image in images]

						if apply_ocr:
										requires_backends(self ,    'pytesseract'       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   []
										SCREAMING_SNAKE_CASE_   :	Dict													=   []
										for image in images:
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   apply_tesseract(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
														words_batch.append(UpperCamelCase__       )
														boxes_batch.append(UpperCamelCase__       )

						if do_resize:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   [self.resize(image=UpperCamelCase__ ,    size=UpperCamelCase__ ,    resample=UpperCamelCase__       ) for image in images]

						# flip color channels from RGB to BGR (as Detectron2 requires this)
						SCREAMING_SNAKE_CASE_   :	Tuple													=   [flip_channel_order(UpperCamelCase__       ) for image in images]
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [to_channel_dimension_format(UpperCamelCase__ ,    UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   BatchFeature(data={'pixel_values': images} ,    tensor_type=UpperCamelCase__       )

						if apply_ocr:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   words_batch
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   boxes_batch
						return data







from timeit import timeit



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   0
				while number:
								number &= number - 1
								result += 1
				return result



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
				while number:
								if number % 2 == 1:
												result += 1
								number >>= 1
				return result



def 					_lowerCamelCase(			):





				'''simple docstring'''







				def do_benchmark(lowerCAmelCase__				:		int      ) -> None:
								SCREAMING_SNAKE_CASE_   :	int													=   'import __main__ as z'
								print(F'''Benchmark when {number = }:'''      )
								print(F'''{get_set_bits_count_using_modulo_operator(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	int													=   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,    setup=lowerCAmelCase__      )
								print(F'''timeit() runs in {timing} seconds'''      )
								print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   timeit(
								    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,    setup=lowerCAmelCase__    ,    )
								print(F'''timeit() runs in {timing} seconds'''      )

				for number in (25, 37, 58, 0):
								do_benchmark(lowerCAmelCase__      )
								print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()