

'''simple docstring'''





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 		__SCREAMING_SNAKE_CASE       ( _snake_case			):
				lowerCamelCase_							=						['pixel_values']







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Union[int, float] = 1 / 255		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		int = 8		,				**UpperCAmelCase__    :		Optional[Any]		,				):
										'''simple docstring'''
										super().__init__(**lowerCAmelCase__      )

										lowercase   :       Any								=do_rescale
										lowercase   :       Optional[Any]								=rescale_factor
										lowercase   :       Dict								=do_pad
										lowercase   :       Tuple								=pad_size







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		np.ndarray		,				UpperCAmelCase__    :		float		,				UpperCAmelCase__    :		Optional[Union[str, ChannelDimension]] = None		,				**UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										return rescale(lowerCAmelCase__		,				scale=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		np.ndarray		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Optional[Union[str, ChannelDimension]] = None      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Optional[int]								=get_image_size(lowerCAmelCase__      )
										lowercase   :       List[Any]								=(old_height // size + 1) * size - old_height
										lowercase   :       List[Any]								=(old_width // size + 1) * size - old_width

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







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		ImageInput		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[float] = None		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		Optional[Union[str, TensorType]] = None		,				UpperCAmelCase__    :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,				**UpperCAmelCase__    :		Union[str, Any]		,				):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=do_rescale if do_rescale is not None else self.do_rescale
										lowercase   :       Optional[int]								=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase   :       str								=do_pad if do_pad is not None else self.do_pad
										lowercase   :       List[str]								=pad_size if pad_size is not None else self.pad_size

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

										# All transformations expect numpy arrays.
										lowercase   :       str								=[to_numpy_array(lowerCAmelCase__      ) for image in images]

										if do_rescale:
																lowercase   :       Optional[int]								=[self.rescale(image=lowerCAmelCase__		,				scale=lowerCAmelCase__      ) for image in images]

										if do_pad:
																lowercase   :       int								=[self.pad(lowerCAmelCase__		,				size=lowerCAmelCase__      ) for image in images]

										lowercase   :       str								=[to_channel_dimension_format(lowerCAmelCase__		,				lowerCAmelCase__      ) for image in images]

										lowercase   :       Any								={'''pixel_values''': images}
										return BatchFeature(data=lowerCAmelCase__		,				tensor_type=lowerCAmelCase__      )








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]	)							->     str:
						lowercase   :       Optional[Any]								=[0 for i in range(r + 1	)]
						# nc0 = 1
						lowercase   :       Optional[Any]								=1
						for i in range(1      ,   n + 1	):
												# to compute current row from previous row.
												lowercase   :       str								=min(__magic_name__      ,   __magic_name__	)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]







print(binomial_coefficient(n=10, r=5))





