






"""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, 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, is_vision_available, logging


if is_vision_available():
							import PIL


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["pixel_values"]

					def __init__(			self  :      Any     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = PIL.Image.BICUBIC     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      Union[int, float] = 1 / 2_5_5     ,		__snake_case  :      bool = True     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							None:
											super().__init__(**__snake_case					)
											__magic_name__:	Optional[Any]							=       size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
											__magic_name__:	Dict							=       get_size_dict(__snake_case					)
											__magic_name__:	Tuple							=       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											__magic_name__:	Optional[Any]							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											__magic_name__:	Any							=       do_resize
											__magic_name__:	Optional[Any]							=       size
											__magic_name__:	Union[str, Any]							=       resample
											__magic_name__:	List[Any]							=       do_center_crop
											__magic_name__:	Union[str, Any]							=       crop_size
											__magic_name__:	int							=       do_rescale
											__magic_name__:	Optional[Any]							=       rescale_factor
											__magic_name__:	Any							=       do_normalize
											__magic_name__:	List[str]							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__magic_name__:	int							=       image_std if image_std is not None else IMAGENET_STANDARD_STD

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      PILImageResampling = PIL.Image.BICUBIC     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      int     ,		)			->							np.ndarray:
											__magic_name__:	List[str]							=       get_size_dict(__snake_case					)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
											return resize(
											    __snake_case     ,		size=(size["""height"""], size["""width"""])     ,		resample=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      List[str]     ,		)			->							np.ndarray:
											__magic_name__:	Optional[Any]							=       get_size_dict(__snake_case					)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
											return center_crop(__snake_case     ,		size=(size["""height"""], size["""width"""])     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[int, float]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Union[str, Any]     ,		)			->							Any:
											return rescale(__snake_case     ,		scale=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							np.ndarray:
											return normalize(__snake_case     ,		mean=__snake_case     ,		std=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)






					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      Dict=None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		__snake_case  :      ChannelDimension = ChannelDimension.FIRST     ,		**__snake_case  :      List[Any]     ,		)			->							PIL.Image.Image:
											__magic_name__:	Optional[int]							=       do_resize if do_resize is not None else self.do_resize
											__magic_name__:	Dict							=       resample if resample is not None else self.resample
											__magic_name__:	Dict							=       do_center_crop if do_center_crop is not None else self.do_center_crop
											__magic_name__:	Any							=       do_rescale if do_rescale is not None else self.do_rescale
											__magic_name__:	Tuple							=       rescale_factor if rescale_factor is not None else self.rescale_factor
											__magic_name__:	str							=       do_normalize if do_normalize is not None else self.do_normalize
											__magic_name__:	Any							=       image_mean if image_mean is not None else self.image_mean
											__magic_name__:	Dict							=       image_std if image_std is not None else self.image_std

											__magic_name__:	Optional[Any]							=       size if size is not None else self.size
											__magic_name__:	List[str]							=       get_size_dict(__snake_case					)
											__magic_name__:	int							=       crop_size if crop_size is not None else self.crop_size
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											__magic_name__:	Optional[Any]							=       make_list_of_images(__snake_case					)

											if not valid_images(__snake_case					):
																	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_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.
											__magic_name__:	Optional[Any]							=       [to_numpy_array(__snake_case					) for image in images]

											if do_resize:
																	__magic_name__:	str							=       [self.resize(image=__snake_case     ,		size=__snake_case     ,		resample=__snake_case					) for image in images]

											if do_center_crop:
																	__magic_name__:	List[str]							=       [self.center_crop(image=__snake_case     ,		size=__snake_case					) for image in images]

											if do_rescale:
																	__magic_name__:	Union[str, Any]							=       [self.rescale(image=__snake_case     ,		scale=__snake_case					) for image in images]

											if do_normalize:
																	__magic_name__:	Optional[int]							=       [self.normalize(image=__snake_case     ,		mean=__snake_case     ,		std=__snake_case					) for image in images]

											__magic_name__:	List[str]							=       [to_channel_dimension_format(__snake_case     ,		__snake_case					) for image in images]

											__magic_name__:	Optional[int]							=       {"""pixel_values""": images}
											return BatchFeature(data=__snake_case     ,		tensor_type=__snake_case					)







from maths.prime_factors import prime_factors


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase							,      _UpperCamelCase	):
						__lowercase			       = F'Input value of [number={number}] must be an integer'
						raise TypeError(_UpperCamelCase	)
			if number < 1:
						raise ValueError('''Input must be a positive integer'''	)
			return -1 if len(prime_factors(_UpperCamelCase	)	) % 2 else 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()


