



'''simple docstring'''





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 lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     return [
         int(1_000 * (box[0] / width)),
         int(1_000 * (box[1] / height)),
         int(1_000 * (box[2] / width)),
         int(1_000 * (box[3] / height)),
     ]




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  to_pil_image(__A)
     _a  ,    _a      =  pil_image.size
     _a      =  pytesseract.image_to_data(__A  ,	lang=__A  ,	output_type='''dict'''  ,	config=__A)
     _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(__A) if not word.strip()]
     _a      =  [word for idx, word in enumerate(__A) if idx not in irrelevant_indices]
     _a      =  [coord for idx, coord in enumerate(__A) if idx not in irrelevant_indices]
     _a      =  [coord for idx, coord in enumerate(__A) if idx not in irrelevant_indices]
     _a      =  [coord for idx, coord in enumerate(__A) if idx not in irrelevant_indices]
     _a      =  [coord for idx, coord in enumerate(__A) if idx not in irrelevant_indices]

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

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

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

     return words, normalized_boxes






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       ['pixel_values']



 def __init__(self ,   A = True ,   A = None ,   A = PILImageResampling.BILINEAR ,   A = True ,   A = 1 / 255 ,   A = True ,   A = None ,   A = None ,   A = True ,   A = None ,   A = "" ,   **A ,   )    ->     None:


      """simple docstring"""



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

      _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 					a__  (self ,   A ,   A ,   A = PILImageResampling.BILINEAR ,   A = None ,   **A ,   )    ->     np.ndarray:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A = None ,   **A ,   )    ->     np.ndarray:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A ,   A ,   A = None ,   **A ,   )    ->     np.ndarray:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A = None ,   A = None ,   A=None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = None ,   A = ChannelDimension.FIRST ,   **A ,   )    ->     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(A       )
      _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(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.'''       )

      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(A       ) 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(A ,   A ,   A       )
                words_batch.append(A       )
                boxes_batch.append(A       )

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

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

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

      _a      =  [to_channel_dimension_format(A ,   A       ) for image in images]

      _a      =  BatchFeature(data={'''pixel_values''': images} ,   tensor_type=A       )

      if apply_ocr:
           _a      =  words_batch
           _a      =  boxes_batch
      return data








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



