





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_a     :    Tuple         =						logging.get_logger(__name__)
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Optional[Any]:




				"""simple docstring"""






				snake_case   :					int    						=				b.T
				snake_case   :					Dict    						=				np.sum(np.square(lowerCamelCase_   )		,     axis=1   )
				snake_case   :					Any    						=				np.sum(np.square(lowerCamelCase_   )		,     axis=0   )
				snake_case   :					Dict    						=				np.matmul(lowerCamelCase_		,     lowerCamelCase_   )
				snake_case   :					Tuple    						=				aa[:, None] - 2 * ab + ba[None, :]
				return d
def 							a_	(    __magic_name__		,     __magic_name__   )				->			List[Any]:




				"""simple docstring"""






				snake_case   :					Optional[int]    						=				x.reshape(-1		,     3   )
				snake_case   :					Dict    						=				squared_euclidean_distance(lowerCamelCase_		,     lowerCamelCase_   )
				return np.argmin(lowerCamelCase_		,     axis=1   )
class   a_ (			lowercase_      ):
			A__      :			str       	=  ['pixel_values']

			def __init__(   self   :  Any     , UpperCAmelCase__   :  Optional[Union[List[List[int]], np.ndarray]] = None     , UpperCAmelCase__   :  bool = True     , UpperCAmelCase__   :  Dict[str, int] = None     , UpperCAmelCase__   :  PILImageResampling = PILImageResampling.BILINEAR     , UpperCAmelCase__   :  bool = True     , UpperCAmelCase__   :  bool = True     , **UpperCAmelCase__   :  str     , ):






							"""simple docstring"""
							super().__init__(**lowerCamelCase_						)
							snake_case   :					Union[str, Any]    						=				size if size is not None else {"""height""": 256, """width""": 256}
							snake_case   :					List[str]    						=				get_size_dict(lowerCamelCase_						)
							snake_case   :					Optional[Any]    						=				np.array(lowerCamelCase_						) if clusters is not None else None
							snake_case   :					Union[str, Any]    						=				do_resize
							snake_case   :					List[str]    						=				size
							snake_case   :					Optional[Any]    						=				resample
							snake_case   :					Dict    						=				do_normalize
							snake_case   :					str    						=				do_color_quantize

			def      lowerCAmelCase(   self   :  Optional[Any]     , UpperCAmelCase__   :  np.ndarray     , UpperCAmelCase__   :  Dict[str, int]     , UpperCAmelCase__   :  PILImageResampling = PILImageResampling.BILINEAR     , UpperCAmelCase__   :  Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase__   :  Tuple     , ):






							"""simple docstring"""
							snake_case   :					int    						=				get_size_dict(lowerCamelCase_						)
							if "height" not in size or "width" not in size:
											raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}"						)
							return resize(
							    lowerCamelCase_     , size=(size['''height'''], size['''width'''])     , resample=lowerCamelCase_     , data_format=lowerCamelCase_     , **lowerCamelCase_						)

			def      lowerCAmelCase(   self   :  Optional[Any]     , UpperCAmelCase__   :  np.ndarray     , UpperCAmelCase__   :  Optional[Union[str, ChannelDimension]] = None     , ):






							"""simple docstring"""
							snake_case   :					Any    						=				rescale(image=lowerCamelCase_     , scale=1 / 127.5     , data_format=lowerCamelCase_						)
							snake_case   :					List[Any]    						=				image - 1
							return image



			def      lowerCAmelCase(   self   :  Optional[int]     , UpperCAmelCase__   :  ImageInput     , UpperCAmelCase__   :  bool = None     , UpperCAmelCase__   :  Dict[str, int] = None     , UpperCAmelCase__   :  PILImageResampling = None     , UpperCAmelCase__   :  bool = None     , UpperCAmelCase__   :  Optional[bool] = None     , UpperCAmelCase__   :  Optional[Union[List[List[int]], np.ndarray]] = None     , UpperCAmelCase__   :  Optional[Union[str, TensorType]] = None     , UpperCAmelCase__   :  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     , **UpperCAmelCase__   :  Any     , ):






							"""simple docstring"""
							snake_case   :					Tuple    						=				do_resize if do_resize is not None else self.do_resize
							snake_case   :					Dict    						=				size if size is not None else self.size
							snake_case   :					str    						=				get_size_dict(lowerCamelCase_						)
							snake_case   :					Dict    						=				resample if resample is not None else self.resample
							snake_case   :					Tuple    						=				do_normalize if do_normalize is not None else self.do_normalize
							snake_case   :					Optional[Any]    						=				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
							snake_case   :					List[str]    						=				clusters if clusters is not None else self.clusters
							snake_case   :					Optional[Any]    						=				np.array(lowerCamelCase_						)

							snake_case   :					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_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

							if do_color_quantize and clusters is None:
											raise ValueError('''Clusters must be specified if do_color_quantize is True.'''						)

							# All transformations expect numpy arrays.
							snake_case   :					Dict    						=				[to_numpy_array(lowerCamelCase_						) for image in images]

							if do_resize:
											snake_case   :					Optional[int]    						=				[self.resize(image=lowerCamelCase_     , size=lowerCamelCase_     , resample=lowerCamelCase_						) for image in images]

							if do_normalize:
											snake_case   :					Union[str, Any]    						=				[self.normalize(image=lowerCamelCase_						) for image in images]

							if do_color_quantize:
											snake_case   :					Optional[Any]    						=				[to_channel_dimension_format(lowerCamelCase_     , ChannelDimension.LAST						) for image in images]
											# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
											snake_case   :					Any    						=				np.array(lowerCamelCase_						)
											snake_case   :					Tuple    						=				color_quantize(lowerCamelCase_     , lowerCamelCase_						).reshape(images.shape[:-1]						)

											# flatten to (batch_size, height*width)
											snake_case   :					str    						=				images.shape[0]
											snake_case   :					int    						=				images.reshape(lowerCamelCase_     , -1						)

											# We need to convert back to a list of images to keep consistent behaviour across processors.
											snake_case   :					Optional[int]    						=				list(lowerCamelCase_						)
							else:
											snake_case   :					str    						=				[to_channel_dimension_format(lowerCamelCase_     , lowerCamelCase_						) for image in images]

							snake_case   :					Dict    						=				{"""input_ids""": images}
							return BatchFeature(data=lowerCamelCase_     , tensor_type=lowerCamelCase_						)







'''simple docstring'''




from math import factorial



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		if n < k or k < 0:
				raise ValueError("""Please enter positive integers for n and k where n >= k"""	)
		return factorial(lowerCamelCase_	) // (factorial(lowerCamelCase_	) * factorial(n - k	))


if __name__ == "__main__":
							print(
							    """The number of five-card hands possible from a standard""",
							    f'''fifty-two card deck is: {combinations(52, 5)}\n''',
							)

							print(
							    """If a class of 40 students must be arranged into groups of""",
							    f'''4 for group projects, there are {combinations(40, 4)} ways''',
							    """to arrange them.\n""",
							)

							print(
							    """If 10 teams are competing in a Formula One race, there""",
							    f'''are {combinations(10, 3)} ways that first, second and''',
							    """third place can be awarded.""",
							)



