

'''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


lowerCAmelCase__   		=      logging.get_logger(__name__)


def 						_A	(       A__	,							A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					x.reshape(-1	,							3 )
 __lowercase				    =					squared_euclidean_distance(A__	,							A__ )
 return np.argmin(A__	,							axis=1 )






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     ['pixel_values']






  def __init__( self  :	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[Any]   ,):
   super().__init__(**lowercase__     )
   __lowercase				    =					size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
   __lowercase				    =					get_size_dict(lowercase__     )
   __lowercase				    =					np.array(lowercase__     ) if clusters is not None else None
   __lowercase				    =					do_resize
   __lowercase				    =					size
   __lowercase				    =					resample
   __lowercase				    =					do_normalize
   __lowercase				    =					do_color_quantize






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	np.ndarray   ,lowercase__  :	Dict[str, int]   ,lowercase__  :	PILImageResampling = PILImageResampling.BILINEAR   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	str   ,):
   __lowercase				    =					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    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	np.ndarray   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,):
   __lowercase				    =					rescale(image=lowercase__   ,scale=1 / 1_2_7.5   ,data_format=lowercase__     )
   __lowercase				    =					image - 1
   return image






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,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__  :	Any   ,):
   __lowercase				    =					do_resize if do_resize is not None else self.do_resize
   __lowercase				    =					size if size is not None else self.size
   __lowercase				    =					get_size_dict(lowercase__     )
   __lowercase				    =					resample if resample is not None else self.resample
   __lowercase				    =					do_normalize if do_normalize is not None else self.do_normalize
   __lowercase				    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
   __lowercase				    =					clusters if clusters is not None else self.clusters
   __lowercase				    =					np.array(lowercase__     )

   __lowercase				    =					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.
   __lowercase				    =					[to_numpy_array(lowercase__     ) for image in images]

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

   if do_normalize:
    __lowercase				    =					[self.normalize(image=lowercase__     ) for image in images]

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

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

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

   __lowercase				    =					{'''input_ids''': images}
   return BatchFeature(data=lowercase__   ,tensor_type=lowercase__     )





from __future__ import annotations

import numpy as np



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    return np.maximum(0				,					lowerCamelCase_					)


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


