



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


SCREAMING_SNAKE_CASE  					=    logging.get_logger(__name__)


if is_vision_available():
			import PIL



class 						lowerCamelCase   (    lowercase__	):







					'''simple docstring'''







					lowerCAmelCase_					:     Optional[Any]					       =	['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__(**A_  )
							UpperCAmelCase_						       =					size if size is not None else {"""shortest_edge""": 224}
							UpperCAmelCase_						       =					get_size_dict(A_     ,						default_to_square=A_  )
							UpperCAmelCase_						       =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							UpperCAmelCase_						       =					get_size_dict(A_     ,						default_to_square=A_     ,						param_name="crop_size"  )

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



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase = PILImageResampling.BICUBIC     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							UpperCAmelCase_						       =					get_size_dict(A_     ,						default_to_square=A_  )
							if "shortest_edge" not in size:
									raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
							UpperCAmelCase_						       =					get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_  )
							return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_  )



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							UpperCAmelCase_						       =					get_size_dict(A_  )
							if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
							return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_  )



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_  )



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_  )



					def  A__		(      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     ,						):
							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_     ,						param_name="size"     ,						default_to_square=A_  )
							UpperCAmelCase_						       =					resample if resample is not None else self.resample
							UpperCAmelCase_						       =					do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase_						       =					crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_						       =					get_size_dict(A_     ,						param_name="crop_size"     ,						default_to_square=A_  )
							UpperCAmelCase_						       =					do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_						       =					rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_						       =					do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_						       =					image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_						       =					image_std if image_std is not None else self.image_std
							UpperCAmelCase_						       =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							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."  )

							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:
									UpperCAmelCase_						       =					[convert_to_rgb(A_  ) for image in images]

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

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

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

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

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

							UpperCAmelCase_						       =					[to_channel_dimension_format(A_     ,						A_  ) for image in images]

							UpperCAmelCase_						       =					{"""pixel_values""": images}
							return BatchFeature(data=A_     ,						tensor_type=A_  )





def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return x if y == 0 else greatest_common_divisor(snake_case__						,   x % y     )




def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return (x * y) // greatest_common_divisor(snake_case__						,   snake_case__     )




def    a__    (		snake_case__					:							int = 20     ):
  _UpperCAmelCase		:							Union[str, Any]							=     1
  for i in range(1						,   n + 1     ):
    _UpperCAmelCase		:							Dict							=     lcm(snake_case__						,   snake_case__     )
  return g


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





