

"""simple docstring"""





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


__UpperCamelCase    : str				 =						logging.get_logger(__name__)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Optional[Any]    							=      b.T
	lowerCAmelCase__      :		int    							=      np.sum(np.square(A_					)		,	axis=1					)
	lowerCAmelCase__      :		str    							=      np.sum(np.square(A_					)		,	axis=0					)
	lowerCAmelCase__      :		Any    							=      np.matmul(A_		,	A_					)
	lowerCAmelCase__      :		int    							=      aa[:, None] - 2 * ab + ba[None, :]
	return d

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Any    							=      x.reshape(-1		,	3					)
	lowerCAmelCase__      :		str    							=      squared_euclidean_distance(A_		,	A_					)
	return np.argmin(A_		,	axis=1					)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			["pixel_values"]







					def __init__(			self     :							List[Any]  ,lowercase_     :							Optional[Union[List[List[int]], np.ndarray]] = None  ,lowercase_     :							bool = True  ,lowercase_     :							Dict[str, int] = None  ,lowercase_     :							PILImageResampling = PILImageResampling.BILINEAR  ,lowercase_     :							bool = True  ,lowercase_     :							bool = True  ,**lowercase_     :							Optional[int]  ,):
						super().__init__(**lowercase_						)
						lowerCAmelCase__      :		int    							=      size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
						lowerCAmelCase__      :		Union[str, Any]    							=      get_size_dict(lowercase_						)
						lowerCAmelCase__      :		Any    							=      np.array(lowercase_						) if clusters is not None else None
						lowerCAmelCase__      :		int    							=      do_resize
						lowerCAmelCase__      :		str    							=      size
						lowerCAmelCase__      :		List[str]    							=      resample
						lowerCAmelCase__      :		str    							=      do_normalize
						lowerCAmelCase__      :		int    							=      do_color_quantize







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							np.ndarray  ,lowercase_     :							Dict[str, int]  ,lowercase_     :							PILImageResampling = PILImageResampling.BILINEAR  ,lowercase_     :							Optional[Union[str, ChannelDimension]] = None  ,**lowercase_     :							List[Any]  ,):
						lowerCAmelCase__      :		Tuple    							=      get_size_dict(lowercase_						)
						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(
						    lowercase_  ,size=(size['''height'''], size['''width'''])  ,resample=lowercase_  ,data_format=lowercase_  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							np.ndarray  ,lowercase_     :							Optional[Union[str, ChannelDimension]] = None  ,):
						lowerCAmelCase__      :		Dict    							=      rescale(image=lowercase_  ,scale=1 / 127.5  ,data_format=lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      image - 1
						return image







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							ImageInput  ,lowercase_     :							bool = None  ,lowercase_     :							Dict[str, int] = None  ,lowercase_     :							PILImageResampling = None  ,lowercase_     :							bool = None  ,lowercase_     :							Optional[bool] = None  ,lowercase_     :							Optional[Union[List[List[int]], np.ndarray]] = None  ,lowercase_     :							Optional[Union[str, TensorType]] = None  ,lowercase_     :							Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST  ,**lowercase_     :							int  ,):
						lowerCAmelCase__      :		Tuple    							=      do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__      :		int    							=      size if size is not None else self.size
						lowerCAmelCase__      :		Tuple    							=      get_size_dict(lowercase_						)
						lowerCAmelCase__      :		str    							=      resample if resample is not None else self.resample
						lowerCAmelCase__      :		Optional[int]    							=      do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__      :		Tuple    							=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						lowerCAmelCase__      :		Any    							=      clusters if clusters is not None else self.clusters
						lowerCAmelCase__      :		Optional[Any]    							=      np.array(lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      make_list_of_images(lowercase_						)

						if not valid_images(lowercase_						):
							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.
						lowerCAmelCase__      :		Optional[Any]    							=      [to_numpy_array(lowercase_						) for image in images]

						if do_resize:
							lowerCAmelCase__      :		Union[str, Any]    							=      [self.resize(image=lowercase_  ,size=lowercase_  ,resample=lowercase_						) for image in images]

						if do_normalize:
							lowerCAmelCase__      :		Dict    							=      [self.normalize(image=lowercase_						) for image in images]

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

							# flatten to (batch_size, height*width)
							lowerCAmelCase__      :		Optional[Any]    							=      images.shape[0]
							lowerCAmelCase__      :		Tuple    							=      images.reshape(lowercase_  ,-1						)

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

						lowerCAmelCase__      :		Optional[Any]    							=      {'''input_ids''': images}
						return BatchFeature(data=lowercase_  ,tensor_type=lowercase_						)







"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      int(A_					)
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(A_					)
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      divmod(A_		,	2					)
	return binary_recursive(A_					) + str(A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      str(A_					).strip()
	if not number:
		raise ValueError('''No input value was provided'''					)
	lowerCAmelCase__      :		Any    							=      '''-''' if number.startswith('''-'''					) else ''''''
	lowerCAmelCase__      :		Dict    							=      number.lstrip('''-'''					)
	if not number.isnumeric():
		raise ValueError('''Input value is not an integer'''					)
	return f'{negative}0b{binary_recursive(int(A_					)					)}'


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

							testmod()




