


"""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 			lowerCamelCase_		(			lowercase_   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Dict      							=					['''pixel_values''']



				def __init__(						self       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = PIL.Image.BICUBIC       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = 1 / 255       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								super().__init__(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				size if size is not None else {"height": 256, "width": 256}
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				crop_size if crop_size is not None else {"height": 224, "width": 224}
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__       ,		param_name="crop_size"     )

								SCREAMING_SNAKE_CASE__     							=				do_resize
								SCREAMING_SNAKE_CASE__     							=				size
								SCREAMING_SNAKE_CASE__     							=				resample
								SCREAMING_SNAKE_CASE__     							=				do_center_crop
								SCREAMING_SNAKE_CASE__     							=				crop_size
								SCREAMING_SNAKE_CASE__     							=				do_rescale
								SCREAMING_SNAKE_CASE__     							=				rescale_factor
								SCREAMING_SNAKE_CASE__     							=				do_normalize
								SCREAMING_SNAKE_CASE__     							=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								SCREAMING_SNAKE_CASE__     							=				image_std if image_std is not None else IMAGENET_STANDARD_STD



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = PIL.Image.BICUBIC       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								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(
								    UpperCAmelCase__       ,		size=(size["height"], size["width"])       ,		resample=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								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(UpperCAmelCase__       ,		size=(size["height"], size["width"])       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								return rescale(UpperCAmelCase__       ,		scale=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								return normalize(UpperCAmelCase__       ,		mean=UpperCAmelCase__       ,		std=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = ChannelDimension.FIRST       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE__     							=				resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE__     							=				do_center_crop if do_center_crop is not None else self.do_center_crop
								SCREAMING_SNAKE_CASE__     							=				do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE__     							=				rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE__     							=				do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE__     							=				image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE__     							=				image_std if image_std is not None else self.image_std

								SCREAMING_SNAKE_CASE__     							=				size if size is not None else self.size
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				crop_size if crop_size is not None else self.crop_size
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__       ,		param_name="crop_size"     )

								SCREAMING_SNAKE_CASE__     							=				make_list_of_images(UpperCAmelCase__     )

								if not valid_images(UpperCAmelCase__     ):
												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.
								SCREAMING_SNAKE_CASE__     							=				[to_numpy_array(UpperCAmelCase__     ) for image in images]

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

								if do_center_crop:
												SCREAMING_SNAKE_CASE__     							=				[self.center_crop(image=UpperCAmelCase__       ,		size=UpperCAmelCase__     ) for image in images]

								if do_rescale:
												SCREAMING_SNAKE_CASE__     							=				[self.rescale(image=UpperCAmelCase__       ,		scale=UpperCAmelCase__     ) for image in images]

								if do_normalize:
												SCREAMING_SNAKE_CASE__     							=				[self.normalize(image=UpperCAmelCase__       ,		mean=UpperCAmelCase__       ,		std=UpperCAmelCase__     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				[to_channel_dimension_format(UpperCAmelCase__       ,		UpperCAmelCase__     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				{"pixel_values": images}
								return BatchFeature(data=UpperCAmelCase__       ,		tensor_type=UpperCAmelCase__     )




"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				prime_factors(lowerCamelCase_  )
				if is_square_free(lowerCamelCase_  ):
								return -1 if len(lowerCamelCase_  ) % 2 else 1
				return 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()
