





'''simple docstring'''



from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( _a  ):







	'''simple docstring'''

	a      :			Any     =    ["""pixel_values"""]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = PILImageResampling.BICUBIC       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = 1 / 255       ,_lowerCamelCase = True       ,_lowerCamelCase = IMAGENET_DEFAULT_MEAN       ,_lowerCamelCase = IMAGENET_DEFAULT_STD       ,**_lowerCamelCase       ,)							->							None:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)
				__lowercase			  =							size if size is not None else {'shortest_edge': 224}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)

				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							resample
				__lowercase			  =							do_center_crop
				__lowercase			  =							crop_size
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_factor
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowercase			  =							image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = PILImageResampling.BICUBIC       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
							__lowercase			  =							int((256 / 224) * size['''shortest_edge''']	)
							__lowercase			  =							get_resize_output_image_size(_lowerCamelCase       ,size=_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
							__lowercase			  =							{'height': output_size[0], 'width': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
							raise ValueError(
							    f"Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"	)
				return resize(
				    _lowerCamelCase       ,size=(size_dict['''height'''], size_dict['''width'''])       ,resample=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase	)
				if "height" not in size or "width" not in size:
							raise ValueError(f"Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"	)
				return center_crop(_lowerCamelCase       ,size=(size['''height'''], size['''width'''])       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = ChannelDimension.FIRST       ,**_lowerCamelCase       ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase			  =							do_resize if do_resize is not None else self.do_resize
				__lowercase			  =							resample if resample is not None else self.resample
				__lowercase			  =							do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowercase			  =							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase			  =							rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowercase			  =							do_normalize if do_normalize is not None else self.do_normalize
				__lowercase			  =							image_mean if image_mean is not None else self.image_mean
				__lowercase			  =							image_std if image_std is not None else self.image_std

				__lowercase			  =							size if size is not None else self.size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							crop_size if crop_size is not None else self.crop_size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)

				__lowercase			  =							make_list_of_images(_lowerCamelCase	)

				if not valid_images(_lowerCamelCase	):
							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.
				__lowercase			  =							[to_numpy_array(_lowerCamelCase	) for image in images]

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

				if do_center_crop:
							__lowercase			  =							[self.center_crop(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

				if do_rescale:
							__lowercase			  =							[self.rescale(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

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

				__lowercase			  =							[to_channel_dimension_format(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

				__lowercase			  =							{'pixel_values': images}
				return BatchFeature(data=_lowerCamelCase       ,tensor_type=_lowerCamelCase	)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						__lowercase			  =							0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors




def 				_lowerCAmelCase  (			):
			__lowercase			  =							1
			__lowercase			  =							1

			while True:
						i += 1
						t_num += i

						if count_divisors(lowerCamelCase_			) > 5_0_0:
									break

			return t_num


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





