






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							:					str     						=     logging.get_logger(__name__)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		b.T
						__snake_case				=		np.sum(np.square(_lowerCAmelCase )      ,	axis=1 )
						__snake_case				=		np.sum(np.square(_lowerCAmelCase )      ,	axis=0 )
						__snake_case				=		np.matmul(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		aa[:, None] - 2 * ab + ba[None, :]
						return d





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case				=		x.reshape(-1      ,	3 )
						__snake_case				=		squared_euclidean_distance(_lowerCAmelCase      ,	_lowerCAmelCase )
						return np.argmin(_lowerCAmelCase      ,	axis=1 )






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	["""pixel_values"""]


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




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)
											__snake_case				=		size if size is not None else {"height": 2_5_6, "width": 2_5_6}
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											__snake_case				=		np.array(SCREAMING_SNAKE_CASE						) if clusters is not None else None
											__snake_case				=		do_resize
											__snake_case				=		size
											__snake_case				=		resample
											__snake_case				=		do_normalize
											__snake_case				=		do_color_quantize


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int]     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = PILImageResampling.BILINEAR     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    **SCREAMING_SNAKE_CASE	:		str     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											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(
											    SCREAMING_SNAKE_CASE     ,    size=(size["height"], size["width"])     ,    resample=SCREAMING_SNAKE_CASE     ,    data_format=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		np.ndarray     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = None     ,    )			->	np.ndarray:




											'''simple docstring'''

											__snake_case				=		rescale(image=SCREAMING_SNAKE_CASE     ,    scale=1 / 127.5     ,    data_format=SCREAMING_SNAKE_CASE						)
											__snake_case				=		image - 1
											return image






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		ImageInput     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Dict[str, int] = None     ,    SCREAMING_SNAKE_CASE	:		PILImageResampling = None     ,    SCREAMING_SNAKE_CASE	:		bool = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[List[List[int]], np.ndarray]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	PIL.Image.Image:




											'''simple docstring'''

											__snake_case				=		do_resize if do_resize is not None else self.do_resize
											__snake_case				=		size if size is not None else self.size
											__snake_case				=		get_size_dict(SCREAMING_SNAKE_CASE						)
											__snake_case				=		resample if resample is not None else self.resample
											__snake_case				=		do_normalize if do_normalize is not None else self.do_normalize
											__snake_case				=		do_color_quantize if do_color_quantize is not None else self.do_color_quantize
											__snake_case				=		clusters if clusters is not None else self.clusters
											__snake_case				=		np.array(SCREAMING_SNAKE_CASE						)

											__snake_case				=		make_list_of_images(SCREAMING_SNAKE_CASE						)

											if not valid_images(SCREAMING_SNAKE_CASE						):
																	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				=		[to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

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

											if do_normalize:
																	__snake_case				=		[self.normalize(image=SCREAMING_SNAKE_CASE						) for image in images]

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

																	# flatten to (batch_size, height*width)
																	__snake_case				=		images.shape[0]
																	__snake_case				=		images.reshape(SCREAMING_SNAKE_CASE     ,    -1						)

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

											__snake_case				=		{"input_ids": images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE     ,    tensor_type=SCREAMING_SNAKE_CASE						)









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





