



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_          =				logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  ['''pixel_values''']





   def __init__(						self							,  _lowerCAmelCase = True							,  _lowerCAmelCase = None							,  _lowerCAmelCase = PILImageResampling.BICUBIC							,  _lowerCAmelCase = True							,  _lowerCAmelCase = None							,  _lowerCAmelCase = True							,  _lowerCAmelCase = 1 / 255							,  _lowerCAmelCase = True							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = True							,  **_lowerCAmelCase							,  ):
         super().__init__(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	size if size is not None else {'''shortest_edge''': 224}
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase							,  default_to_square=_lowerCAmelCase      )
         _lowerCAmelCase			      =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase      )

         _lowerCAmelCase			      =	do_resize
         _lowerCAmelCase			      =	size
         _lowerCAmelCase			      =	resample
         _lowerCAmelCase			      =	do_center_crop
         _lowerCAmelCase			      =	crop_size
         _lowerCAmelCase			      =	do_rescale
         _lowerCAmelCase			      =	rescale_factor
         _lowerCAmelCase			      =	do_normalize
         _lowerCAmelCase			      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _lowerCAmelCase			      =	image_std if image_std is not None else OPENAI_CLIP_STD
         _lowerCAmelCase			      =	do_convert_rgb





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = PILImageResampling.BICUBIC							,  _lowerCAmelCase = None							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase							,  default_to_square=_lowerCAmelCase      )
         _lowerCAmelCase			      =	get_resize_output_image_size(
             _lowerCAmelCase							,  size=(size['''height'''], size['''width'''])							,  default_to_square=_lowerCAmelCase      )
         return resize(_lowerCAmelCase							,  size=_lowerCAmelCase							,  resample=_lowerCAmelCase							,  data_format=_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = None							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase      )
         return center_crop(_lowerCAmelCase							,  size=(size['''height'''], size['''width'''])							,  data_format=_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = None							,  **_lowerCAmelCase							,  ):
         return rescale(_lowerCAmelCase							,  scale=_lowerCAmelCase							,  data_format=_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = None							,  **_lowerCAmelCase							,  ):
         return normalize(_lowerCAmelCase							,  mean=_lowerCAmelCase							,  std=_lowerCAmelCase							,  data_format=_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = ChannelDimension.FIRST							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase			      =	size if size is not None else self.size
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase							,  default_to_square=_lowerCAmelCase      )
         _lowerCAmelCase			      =	resample if resample is not None else self.resample
         _lowerCAmelCase			      =	do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCAmelCase			      =	crop_size if crop_size is not None else self.crop_size
         _lowerCAmelCase			      =	get_size_dict(_lowerCAmelCase      )
         _lowerCAmelCase			      =	do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase			      =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase			      =	do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase			      =	image_mean if image_mean is not None else self.image_mean
         _lowerCAmelCase			      =	image_std if image_std is not None else self.image_std
         _lowerCAmelCase			      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _lowerCAmelCase			      =	make_list_of_images(_lowerCAmelCase      )

         if not valid_images(_lowerCAmelCase      ):
               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_center_crop and crop_size is None:
               raise ValueError('''Crop size must be specified if do_center_crop 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('''Image mean and std must be specified if do_normalize is True.'''      )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
               _lowerCAmelCase			      =	[convert_to_rgb(_lowerCAmelCase      ) for image in images]

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

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

         if do_center_crop:
               _lowerCAmelCase			      =	[self.center_crop(image=_lowerCAmelCase							,  size=_lowerCAmelCase      ) for image in images]

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

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

         _lowerCAmelCase			      =	[to_channel_dimension_format(_lowerCAmelCase							,  _lowerCAmelCase      ) for image in images]

         _lowerCAmelCase			      =	{'''pixel_values''': images}
         return BatchFeature(data=_lowerCAmelCase							,  tensor_type=_lowerCAmelCase      )




import math
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int					)->bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5  ,					int(math.sqrt(_SCREAMING_SNAKE_CASE					) + 1					)  ,					6					):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int = 6_0_0_8_5_1_4_7_5_1_4_3					)->int:

      try:
            _lowerCAmelCase			      =	int(_SCREAMING_SNAKE_CASE					)
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''					)
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''					)
      _lowerCAmelCase			      =	0
      if is_prime(_SCREAMING_SNAKE_CASE					):
            return n
      while n % 2 == 0:
            n //= 2
      if is_prime(_SCREAMING_SNAKE_CASE					):
            return n
      for i in range(3  ,					int(math.sqrt(_SCREAMING_SNAKE_CASE					)					) + 1  ,					2					):
            if n % i == 0:
                  if is_prime(n // i					):
                        _lowerCAmelCase			      =	n // i
                        break
                  elif is_prime(_SCREAMING_SNAKE_CASE					):
                        _lowerCAmelCase			      =	i
      return max_number


if __name__ == "__main__":
    print(F"""{solution() = }""")