

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

lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[Any]:





    '''simple docstring'''






    return [
        int(1000 * (box[0] / width)      ),
        int(1000 * (box[1] / height)      ),
        int(1000 * (box[2] / width)      ),
        int(1000 * (box[3] / height)      ),
    ]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	Optional[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[str]      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   to_pil_image(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   pil_image.size
    A__          =   pytesseract.image_to_data(SCREAMING_SNAKE_CASE__    ,			lang=SCREAMING_SNAKE_CASE__    ,			output_type='dict'    ,			config=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__      ,						A__      ,						A__      ,						A__          =   data['text'], data['left'], data['top'], data['width'], data['height']

    # filter empty words and corresponding coordinates
    A__          =   [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE__      ) if not word.strip()]
    A__          =   [word for idx, word in enumerate(SCREAMING_SNAKE_CASE__      ) if idx not in irrelevant_indices]
    A__          =   [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__      ) if idx not in irrelevant_indices]
    A__          =   [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__      ) if idx not in irrelevant_indices]
    A__          =   [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__      ) if idx not in irrelevant_indices]
    A__          =   [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE__      ) if idx not in irrelevant_indices]

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

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

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

    return words, normalized_boxes


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							['pixel_values']


    def __init__(      self :       Tuple,lowercase_ :       bool = True,lowercase_ :       Dict[str, int] = None,lowercase_ :       PILImageResampling = PILImageResampling.BILINEAR,lowercase_ :       bool = True,lowercase_ :       float = 1 / 2_5_5,lowercase_ :       bool = True,lowercase_ :       Union[float, Iterable[float]] = None,lowercase_ :       Union[float, Iterable[float]] = None,lowercase_ :       bool = True,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = "",**lowercase_ :       Optional[int],)->    None:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        A__          =   size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A__          =   get_size_dict(lowercase_		)

        A__          =   do_resize
        A__          =   size
        A__          =   resample
        A__          =   do_rescale
        A__          =   rescale_value
        A__          =   do_normalize
        A__          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A__          =   image_std if image_std is not None else IMAGENET_STANDARD_STD
        A__          =   apply_ocr
        A__          =   ocr_lang
        A__          =   tesseract_config


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





        '''simple docstring'''







        A__          =   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()}'		)
        A__          =   (size['height'], size['width'])
        return resize(lowercase_,size=lowercase_,resample=lowercase_,data_format=lowercase_,**lowercase_		)


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





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       np.ndarray,lowercase_ :       Union[float, Iterable[float]],lowercase_ :       Union[float, Iterable[float]],lowercase_ :       Optional[Union[str, ChannelDimension]] = None,**lowercase_ :       Optional[int],)->    np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict,lowercase_ :       ImageInput,lowercase_ :       bool = None,lowercase_ :       Dict[str, int] = None,lowercase_ :       List[Any]=None,lowercase_ :       bool = None,lowercase_ :       float = None,lowercase_ :       bool = None,lowercase_ :       Union[float, Iterable[float]] = None,lowercase_ :       Union[float, Iterable[float]] = None,lowercase_ :       bool = None,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[str] = None,lowercase_ :       Optional[Union[str, TensorType]] = None,lowercase_ :       ChannelDimension = ChannelDimension.FIRST,**lowercase_ :       Tuple,)->    PIL.Image.Image:





        '''simple docstring'''







        A__          =   do_resize if do_resize is not None else self.do_resize
        A__          =   size if size is not None else self.size
        A__          =   get_size_dict(lowercase_		)
        A__          =   resample if resample is not None else self.resample
        A__          =   do_rescale if do_rescale is not None else self.do_rescale
        A__          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A__          =   do_normalize if do_normalize is not None else self.do_normalize
        A__          =   image_mean if image_mean is not None else self.image_mean
        A__          =   image_std if image_std is not None else self.image_std
        A__          =   apply_ocr if apply_ocr is not None else self.apply_ocr
        A__          =   ocr_lang if ocr_lang is not None else self.ocr_lang
        A__          =   tesseract_config if tesseract_config is not None else self.tesseract_config

        A__          =   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.
        A__          =   [to_numpy_array(lowercase_		) for image in images]

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

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

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

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

        A__          =   [to_channel_dimension_format(lowercase_,lowercase_		) for image in images]

        A__          =   BatchFeature(data={'pixel_values': images},tensor_type=lowercase_		)

        if apply_ocr:
            A__          =   words_batch
            A__          =   boxes_batch
        return data




from timeit import timeit
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        number &= number - 1
        result += 1
    return result
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result
def 						_snake_case(    )							->      None:





    '''simple docstring'''






    def do_benchmark(SCREAMING_SNAKE_CASE__			:	int      ) -> None:
        A__          =   'import __main__ as z'
        print(f'Benchmark when {number = }:'      )
        print(f'{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,			setup=SCREAMING_SNAKE_CASE__      )
        print(f'timeit() runs in {timing} seconds'      )
        print(f'{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit(
            'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,			setup=SCREAMING_SNAKE_CASE__    ,			)
        print(f'timeit() runs in {timing} seconds'      )

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

