





'''simple docstring'''
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__							:							Optional[int] =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  Dict				,  __lowercase						:  Optional[int]				,  __lowercase						:  str     ) ->      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 				lowercase__   (   __lowercase						:  np.ndarray				,  __lowercase						:  Optional[str]				,  __lowercase						:  Optional[str] = None     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  tesseract_config if tesseract_config is not None else ''

       # apply OCR
       __UpperCamelCase       =  to_pil_image(__lowercase     )
       __UpperCamelCase       ,						__UpperCamelCase       =  pil_image.size
       __UpperCamelCase       =  pytesseract.image_to_data(__lowercase				,  lang=__lowercase				,  output_type='dict'				,  config=__lowercase     )
       __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(__lowercase     ) if not word.strip()]
       __UpperCamelCase       =  [word for idx, word in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]
       __UpperCamelCase       =  [coord for idx, coord in enumerate(__lowercase     ) if idx not in irrelevant_indices]

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

       # finally, normalize the bounding boxes
       __UpperCamelCase       =  []
       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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=["pixel_values"]






 def __init__(       self       :							str							,	__A       :							bool = True							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							bool = True							,	__A       :							Optional[str] = None							,	__A       :							Optional[str] = ""							,	**__A       :							int							,	):
        super().__init__(**__A	)
        __UpperCamelCase       =  size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        __UpperCamelCase       =  get_size_dict(__A	)

        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  resample
        __UpperCamelCase       =  apply_ocr
        __UpperCamelCase       =  ocr_lang
        __UpperCamelCase       =  tesseract_config






 def        _lowerCamelCase					(       self       :							str							,	__A       :							np.ndarray							,	__A       :							Dict[str, int]							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							Any							,	):
        __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        _lowerCamelCase					(       self       :							Dict							,	__A       :							ImageInput							,	__A       :							bool = None							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = None							,	__A       :							bool = None							,	__A       :							Optional[str] = None							,	__A       :							Optional[str] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							ChannelDimension = ChannelDimension.FIRST							,	**__A       :							Optional[int]							,	):
        __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       =  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.'	)

        # All transformations expect numpy arrays.
        __UpperCamelCase       =  [to_numpy_array(__A	) for image in images]

        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]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        __UpperCamelCase       =  [flip_channel_order(__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'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
