

"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__				:List[str]				   =       logging.get_logger(__name__)




class 							snake_case							(			a__ ):

      '''simple docstring'''



      _A     :       str         =       ["""pixel_values"""]

      def __init__(							self       :      List[str]						,				__lowercase       :      Optional[int] = True						,				__lowercase       :      str = None						,				__lowercase       :      int = PILImageResampling.BILINEAR						,				__lowercase       :      int = True						,				__lowercase       :      str = None						,				__lowercase       :      Tuple = True						,				__lowercase       :      List[Any] = 1 / 255						,				__lowercase       :      str = True						,				__lowercase       :      Tuple = None						,				__lowercase       :      str = None						,				**__lowercase       :      Union[str, Any]						,				):


        '''simple docstring'''




        super().__init__(**lowercase__ )
        __UpperCAmelCase    :						Optional[int]       =						size if size is not None else {'''shortest_edge''': 256}
        __UpperCAmelCase    :						Union[str, Any]       =						get_size_dict(lowercase__						,				default_to_square=lowercase__ )
        __UpperCAmelCase    :						List[Any]       =						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        __UpperCAmelCase    :						Union[str, Any]       =						get_size_dict(lowercase__ )
        __UpperCAmelCase    :						int       =						do_resize
        __UpperCAmelCase    :						Dict       =						size
        __UpperCAmelCase    :						Optional[int]       =						resample
        __UpperCAmelCase    :						Dict       =						do_center_crop
        __UpperCAmelCase    :						Any       =						crop_size
        __UpperCAmelCase    :						List[Any]       =						do_rescale
        __UpperCAmelCase    :						List[str]       =						rescale_factor
        __UpperCAmelCase    :						Union[str, Any]       =						do_normalize
        __UpperCAmelCase    :						Any       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCAmelCase    :						Any       =						image_std if image_std is not None else IMAGENET_STANDARD_STD

      def       A_					(							self       :      str						,				__lowercase       :      Any						,				__lowercase       :      int						,				__lowercase       :      Union[str, Any] = PILImageResampling.BICUBIC						,				__lowercase       :      str = None						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						get_size_dict(lowercase__						,				default_to_square=lowercase__ )
        if "shortest_edge" not in size:
          raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
        __UpperCAmelCase    :						Optional[int]       =						get_resize_output_image_size(lowercase__						,				size=size['''shortest_edge''']						,				default_to_square=lowercase__ )
        return resize(lowercase__						,				size=lowercase__						,				resample=lowercase__						,				data_format=lowercase__						,				**lowercase__ )

      def       A_					(							self       :      Dict						,				__lowercase       :      str						,				__lowercase       :      List[str]						,				__lowercase       :      Dict = None						,				**__lowercase       :      Tuple						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						get_size_dict(lowercase__ )
        return center_crop(lowercase__						,				size=(size['''height'''], size['''width'''])						,				data_format=lowercase__						,				**lowercase__ )

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Dict						,				__lowercase       :      Optional[int]						,				__lowercase       :      Dict = None						,				**__lowercase       :      str ):


        '''simple docstring'''




        return rescale(lowercase__						,				scale=lowercase__						,				data_format=lowercase__						,				**lowercase__ )

      def       A_					(							self       :      Dict						,				__lowercase       :      Optional[Any]						,				__lowercase       :      int						,				__lowercase       :      int						,				__lowercase       :      Any = None						,				**__lowercase       :      Any						,				):


        '''simple docstring'''




        return normalize(lowercase__						,				mean=lowercase__						,				std=lowercase__						,				data_format=lowercase__						,				**lowercase__ )







      def       A_					(							self       :      Any						,				__lowercase       :      List[str]						,				__lowercase       :      Tuple = None						,				__lowercase       :      Tuple = None						,				__lowercase       :      Any = None						,				__lowercase       :      List[str] = None						,				__lowercase       :      Any = None						,				__lowercase       :      List[str] = None						,				__lowercase       :      Optional[Any] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      int = None						,				__lowercase       :      str = None						,				__lowercase       :      Tuple = None						,				__lowercase       :      List[str] = ChannelDimension.FIRST						,				**__lowercase       :      int						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase    :						int       =						size if size is not None else self.size
        __UpperCAmelCase    :						Optional[int]       =						get_size_dict(lowercase__						,				default_to_square=lowercase__ )
        __UpperCAmelCase    :						List[str]       =						resample if resample is not None else self.resample
        __UpperCAmelCase    :						Tuple       =						do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase    :						Union[str, Any]       =						crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase    :						Dict       =						get_size_dict(lowercase__ )
        __UpperCAmelCase    :						Optional[Any]       =						do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase    :						Dict       =						rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase    :						Optional[int]       =						do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase    :						Optional[Any]       =						image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase    :						Optional[int]       =						image_std if image_std is not None else self.image_std

        __UpperCAmelCase    :						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:
          raise ValueError('''Size must be specified if do_resize is True.''' )

        if do_center_crop and crop_size is None:
          raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

        if do_rescale and rescale_factor is None:
          raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

        # All transformations expect numpy arrays.
        __UpperCAmelCase    :						Union[str, Any]       =						[to_numpy_array(lowercase__ ) for image in images]

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

        if do_center_crop:
          __UpperCAmelCase    :						Any       =						[self.center_crop(image=lowercase__						,				size=lowercase__ ) for image in images]

        if do_rescale:
          __UpperCAmelCase    :						int       =						[self.rescale(image=lowercase__						,				scale=lowercase__ ) for image in images]

        if do_normalize:
          __UpperCAmelCase    :						Any       =						[self.normalize(image=lowercase__						,				mean=lowercase__						,				std=lowercase__ ) for image in images]

        __UpperCAmelCase    :						List[Any]       =						[to_channel_dimension_format(lowercase__						,				lowercase__ ) for image in images]

        __UpperCAmelCase    :						int       =						{'''pixel_values''': images}
        return BatchFeature(data=lowercase__						,				tensor_type=lowercase__ )






"""simple docstring"""





def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  for n in range(1  ,			1_00_00_00  ):
    yield n * (n + 1) // 2


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						1
  __UpperCAmelCase    :						Any       =						2
  while i * i <= n:
    __UpperCAmelCase    :						Tuple       =						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       lowerCamelCase_       (   )						->Optional[int]:





  """simple docstring"""


  return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase_  ) > 5_00  )


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