





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

snake_case_				=			logging.get_logger(__name__)






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Dict       ,      snake_case_	:  str       )      ->				Optional[Any]:
   return [
       int(1000 * (box[0] / width)       ),
       int(1000 * (box[1] / height)       ),
       int(1000 * (box[2] / width)       ),
       int(1000 * (box[3] / height)       ),
   ]






def lowerCamelCase__    (							snake_case_	:  np.ndarray       ,      snake_case_	:  Optional[str]       ,      snake_case_	:  Optional[str] = None       )      ->				List[str]:
   __snake_case							       =   tesseract_config if tesseract_config is not None else ''''''

   # apply OCR
   __snake_case							       =   to_pil_image(snake_case_       )
   __snake_case       ,	__snake_case							       =   pil_image.size
   __snake_case							       =   pytesseract.image_to_data(snake_case_       ,      lang=snake_case_       ,      output_type='''dict'''       ,      config=snake_case_       )
   __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

   return words, normalized_boxes







class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				str							=    ['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				,			):





         """simple docstring"""



         super().__init__(**a__      )
         __snake_case							       =   size if size is not None else {'''height''': 224, '''width''': 224}
         __snake_case							       =   get_size_dict(a__      )

         __snake_case							       =   do_resize
         __snake_case							       =   size
         __snake_case							       =   resample
         __snake_case							       =   apply_ocr
         __snake_case							       =   ocr_lang
         __snake_case							       =   tesseract_config


      def    a (self       :      List[Any]				,			a__       :      np.ndarray				,			a__       :      Dict[str, int]				,			a__       :      PILImageResampling = PILImageResampling.BILINEAR				,			a__       :      Optional[Union[str, ChannelDimension]] = None				,			**a__       :      List[str]				,			):





         """simple docstring"""



         __snake_case							       =   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()}"""      )
         __snake_case							       =   (size['''height'''], size['''width'''])
         return resize(a__				,			size=a__				,			resample=a__				,			data_format=a__				,			**a__      )


      def    a (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__       :      List[str]				,			):





         """simple docstring"""



         __snake_case							       =   do_resize if do_resize is not None else self.do_resize
         __snake_case							       =   size if size is not None else self.size
         __snake_case							       =   get_size_dict(a__      )
         __snake_case							       =   resample if resample is not None else self.resample
         __snake_case							       =   apply_ocr if apply_ocr is not None else self.apply_ocr
         __snake_case							       =   ocr_lang if ocr_lang is not None else self.ocr_lang
         __snake_case							       =   tesseract_config if tesseract_config is not None else self.tesseract_config

         __snake_case							       =   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.
         __snake_case							       =   [to_numpy_array(a__      ) for image in images]

         if apply_ocr:
            requires_backends(self				,			'''pytesseract'''      )
            __snake_case							       =   []
            __snake_case							       =   []
            for image in images:
               __snake_case       ,	__snake_case							       =   apply_tesseract(a__				,			a__				,			a__      )
               words_batch.append(a__      )
               boxes_batch.append(a__      )

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

         # flip color channels from RGB to BGR (as Detectron2 requires this)
         __snake_case							       =   [flip_channel_order(a__      ) for image in images]
         __snake_case							       =   [to_channel_dimension_format(a__				,			a__      ) for image in images]

         __snake_case							       =   BatchFeature(data={'''pixel_values''': images}				,			tensor_type=a__      )

         if apply_ocr:
            __snake_case							       =   words_batch
            __snake_case							       =   boxes_batch
         return data








def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if not isinstance(snake_case_       ,      snake_case_       ) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''       )

   __snake_case							       =   0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()

