






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__UpperCAmelCase     							=						logging.get_logger(__name__)



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[str]							=  ['''pixel_values''']



		def __init__(							self    ,		_UpperCamelCase = True    ,		_UpperCamelCase = 1 / 2_5_5    ,		_UpperCamelCase = True    ,		_UpperCamelCase = 8    ,		**_UpperCamelCase    ,		)     ->							None:
							super().__init__(**_UpperCamelCase       )

							UpperCAmelCase_   :				int						=						do_rescale
							UpperCAmelCase_   :				List[Any]						=						rescale_factor
							UpperCAmelCase_   :				str						=						do_pad
							UpperCAmelCase_   :				Optional[Any]						=						pad_size



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase       )     ->							np.ndarray:
							return rescale(_UpperCamelCase    ,		scale=_UpperCamelCase    ,		data_format=_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[str]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						get_image_size(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						(old_height // size + 1) * size - old_height
							UpperCAmelCase_   :				int						=						(old_width // size + 1) * size - old_width

							return pad(_UpperCamelCase    ,		((0, pad_height), (0, pad_width))    ,		mode='symmetric'    ,		data_format=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = ChannelDimension.FIRST    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				Tuple						=						do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_   :				str						=						rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_   :				Optional[int]						=						do_pad if do_pad is not None else self.do_pad
							UpperCAmelCase_   :				List[Any]						=						pad_size if pad_size is not None else self.pad_size

							UpperCAmelCase_   :				List[Any]						=						make_list_of_images(_UpperCamelCase       )

							if not valid_images(_UpperCamelCase       ):
												raise ValueError(
												    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
												    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

							if do_rescale and rescale_factor is None:
												raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

							# All transformations expect numpy arrays.
							UpperCAmelCase_   :				List[str]						=						[to_numpy_array(_UpperCamelCase       ) for image in images]

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

							if do_pad:
												UpperCAmelCase_   :				Optional[int]						=						[self.pad(_UpperCamelCase    ,		size=_UpperCamelCase       ) for image in images]

							UpperCAmelCase_   :				List[str]						=						[to_channel_dimension_format(_UpperCamelCase    ,		_UpperCamelCase       ) for image in images]

							UpperCAmelCase_   :				Optional[Any]						=						{'pixel_values': images}
							return BatchFeature(data=_UpperCamelCase    ,		tensor_type=_UpperCamelCase       )














def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






