'''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


_a   :	List[Any] 							=   logging.get_logger(__name__)






class 		__A       (__magic_name__   ):
       snake_case       :Tuple				    =					["pixel_values"]






       def __init__(      self				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = PILImageResampling.BICUBIC				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = True				,     UpperCamelCase_ = 1 / 2_55				,     UpperCamelCase_ = True				,     UpperCamelCase_ = IMAGENET_DEFAULT_MEAN				,     UpperCamelCase_ = IMAGENET_DEFAULT_STD				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		size if size is not None else {"shortest_edge": 2_24}
              __UpperCAmelCase  :				str       				=		get_size_dict(UpperCamelCase_				,     default_to_square=UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
              __UpperCAmelCase  :				Dict       				=		get_size_dict(UpperCamelCase_				,     param_name="crop_size"      )

              __UpperCAmelCase  :				int       				=		do_resize
              __UpperCAmelCase  :				Any       				=		size
              __UpperCAmelCase  :				Any       				=		resample
              __UpperCAmelCase  :				Any       				=		do_center_crop
              __UpperCAmelCase  :				Optional[int]       				=		crop_size
              __UpperCAmelCase  :				int       				=		do_rescale
              __UpperCAmelCase  :				Any       				=		rescale_factor
              __UpperCAmelCase  :				Any       				=		do_normalize
              __UpperCAmelCase  :				Optional[int]       				=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
              __UpperCAmelCase  :				Union[str, Any]       				=		image_std if image_std is not None else IMAGENET_DEFAULT_STD






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = PILImageResampling.BICUBIC				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Union[str, Any]       				=		get_size_dict(UpperCamelCase_				,     default_to_square=UpperCamelCase_      )
              # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
              if "shortest_edge" in size:
                     __UpperCAmelCase  :				Optional[Any]       				=		int((2_56 / 2_24) * size["shortest_edge"]      )
                     __UpperCAmelCase  :				List[Any]       				=		get_resize_output_image_size(UpperCamelCase_				,     size=UpperCamelCase_				,     default_to_square=UpperCamelCase_      )
                     __UpperCAmelCase  :				Tuple       				=		{"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(
                  UpperCamelCase_				,     size=(size_dict["height"], size_dict["width"])				,     resample=UpperCamelCase_				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Tuple       				=		get_size_dict(UpperCamelCase_      )
              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(UpperCamelCase_				,     size=(size["height"], size["width"])				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              return rescale(UpperCamelCase_				,     scale=UpperCamelCase_				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              return normalize(UpperCamelCase_				,     mean=UpperCamelCase_				,     std=UpperCamelCase_				,     data_format=UpperCamelCase_				,     **UpperCamelCase_      )







       def 			_snake_case						(      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_				,     ):
              __UpperCAmelCase  :				int       				=		do_resize if do_resize is not None else self.do_resize
              __UpperCAmelCase  :				Dict       				=		resample if resample is not None else self.resample
              __UpperCAmelCase  :				int       				=		do_center_crop if do_center_crop is not None else self.do_center_crop
              __UpperCAmelCase  :				str       				=		do_rescale if do_rescale is not None else self.do_rescale
              __UpperCAmelCase  :				str       				=		rescale_factor if rescale_factor is not None else self.rescale_factor
              __UpperCAmelCase  :				Union[str, Any]       				=		do_normalize if do_normalize is not None else self.do_normalize
              __UpperCAmelCase  :				Any       				=		image_mean if image_mean is not None else self.image_mean
              __UpperCAmelCase  :				Any       				=		image_std if image_std is not None else self.image_std

              __UpperCAmelCase  :				Any       				=		size if size is not None else self.size
              __UpperCAmelCase  :				Tuple       				=		get_size_dict(UpperCamelCase_				,     default_to_square=UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		crop_size if crop_size is not None else self.crop_size
              __UpperCAmelCase  :				List[str]       				=		get_size_dict(UpperCamelCase_				,     param_name="crop_size"      )

              __UpperCAmelCase  :				int       				=		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:
                     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  :				int       				=		[to_numpy_array(UpperCamelCase_      ) for image in images]

              if do_resize:
                     __UpperCAmelCase  :				int       				=		[self.resize(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ) for image in images]

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

              if do_rescale:
                     __UpperCAmelCase  :				Optional[Any]       				=		[self.rescale(UpperCamelCase_				,     UpperCamelCase_      ) for image in images]

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

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

              __UpperCAmelCase  :				Union[str, Any]       				=		{"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:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		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()






