




'''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


A									=logging.get_logger(__name__)





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]  ):
    UpperCAmelCase     =	b.T
    UpperCAmelCase     =	np.sum(np.square(_a  )					,     axis=1  )
    UpperCAmelCase     =	np.sum(np.square(_a  )					,     axis=0  )
    UpperCAmelCase     =	np.matmul(_a					,     _a  )
    UpperCAmelCase     =	aa[:, None] - 2 * ab + ba[None, :]
    return d





def 		snake_case_							(_a			:							Dict					,     _a			:							List[str]  ):
    UpperCAmelCase     =	x.reshape(-1					,     3  )
    UpperCAmelCase     =	squared_euclidean_distance(_a					,     _a  )
    return np.argmin(_a					,     axis=1  )







class     _a						(			__a				):
       __a       :  List[str]            =      ["""pixel_values"""]
       def __init__(			self		:			Optional[int]     ,       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		:			List[str]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )
           UpperCAmelCase     =	size if size is not None else {'''height''': 256, '''width''': 256}
           UpperCAmelCase     =	get_size_dict(lowercase       )
           UpperCAmelCase     =	np.array(lowercase       ) if clusters is not None else None
           UpperCAmelCase     =	do_resize
           UpperCAmelCase     =	size
           UpperCAmelCase     =	resample
           UpperCAmelCase     =	do_normalize
           UpperCAmelCase     =	do_color_quantize
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Dict[str, int]     ,       lowercase		:			PILImageResampling = PILImageResampling.BILINEAR     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       **lowercase		:			str     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	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 					A   (			self		:			Optional[int]     ,       lowercase		:			np.ndarray     ,       lowercase		:			Optional[Union[str, ChannelDimension]] = None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	rescale(image=lowercase     ,       scale=1 / 127.5     ,       data_format=lowercase       )
           UpperCAmelCase     =	image - 1
           return image







       def 					A   (			self		:			str     ,       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		:			List[Any]     ,       ):



           '''simple docstring'''

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

           UpperCAmelCase     =	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.
           UpperCAmelCase     =	[to_numpy_array(lowercase       ) for image in images]

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

           if do_normalize:
               UpperCAmelCase     =	[self.normalize(image=lowercase       ) for image in images]

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

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

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

           UpperCAmelCase     =	{'''input_ids''': images}
           return BatchFeature(data=lowercase     ,       tensor_type=lowercase       )










'''simple docstring'''







def 		snake_case_							():
    for n in range(1					,     1_0_0_0_0_0_0  ):
        yield n * (n + 1) // 2





def 		snake_case_							(_a			:							Union[str, Any]  ):
    UpperCAmelCase     =	1
    UpperCAmelCase     =	2
    while i * i <= n:
        UpperCAmelCase     =	0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count





def 		snake_case_							():
    return next(i for i in triangle_number_generator() if count_divisors(_a  ) > 5_0_0  )


if __name__ == "__main__":
   print(solution())




