




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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase								=					logging.get_logger(__name__)


if is_vision_available():
     import PIL





class 			__lowerCamelCase	(   _lowercase   ):




    """simple docstring"""

    snake_case__ =				["pixel_values"]


    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BICUBIC       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Union[int, float] = 1 / 255       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     **SCREAMING_SNAKE_CASE__					:					Any       ,     )				->	None:
         super().__init__(**__UpperCamelCase       )
         lowerCAmelCase__          =			size if size is not None else {"shortest_edge": 224}
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       ,     default_to_square=__UpperCamelCase       )
         lowerCAmelCase__          =			crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       ,     default_to_square=__UpperCamelCase       ,     param_name="crop_size"       )

         lowerCAmelCase__          =			do_resize
         lowerCAmelCase__          =			size
         lowerCAmelCase__          =			resample
         lowerCAmelCase__          =			do_center_crop
         lowerCAmelCase__          =			crop_size
         lowerCAmelCase__          =			do_rescale
         lowerCAmelCase__          =			rescale_factor
         lowerCAmelCase__          =			do_normalize
         lowerCAmelCase__          =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowerCAmelCase__          =			image_std if image_std is not None else OPENAI_CLIP_STD
         lowerCAmelCase__          =			do_convert_rgb


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int]       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BICUBIC       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       ,     default_to_square=__UpperCamelCase       )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'       )
         lowerCAmelCase__          =			get_resize_output_image_size(__UpperCamelCase       ,     size=size["shortest_edge"]       ,     default_to_square=__UpperCamelCase       )
         return resize(__UpperCamelCase       ,     size=__UpperCamelCase       ,     resample=__UpperCamelCase       ,     data_format=__UpperCamelCase       ,     **__UpperCamelCase       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Any       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'       )
         return center_crop(__UpperCamelCase       ,     size=(size["height"], size["width"])       ,     data_format=__UpperCamelCase       ,     **__UpperCamelCase       )


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


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


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					ImageInput       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					int = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					float = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[ChannelDimension] = ChannelDimension.FIRST       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     )				->	PIL.Image.Image:
         lowerCAmelCase__          =			do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__          =			size if size is not None else self.size
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       ,     param_name="size"       ,     default_to_square=__UpperCamelCase       )
         lowerCAmelCase__          =			resample if resample is not None else self.resample
         lowerCAmelCase__          =			do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__          =			crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__          =			get_size_dict(__UpperCamelCase       ,     param_name="crop_size"       ,     default_to_square=__UpperCamelCase       )
         lowerCAmelCase__          =			do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__          =			rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__          =			do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__          =			image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__          =			image_std if image_std is not None else self.image_std
         lowerCAmelCase__          =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowerCAmelCase__          =			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."       )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowerCAmelCase__          =			[convert_to_rgb(__UpperCamelCase       ) for image in images]

         # All transformations expect numpy arrays.
         lowerCAmelCase__          =			[to_numpy_array(__UpperCamelCase       ) for image in images]

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

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

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

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

         lowerCAmelCase__          =			[to_channel_dimension_format(__UpperCamelCase       ,     __UpperCamelCase       ) for image in images]

         lowerCAmelCase__          =			{"pixel_values": images}
         return BatchFeature(data=__UpperCamelCase       ,     tensor_type=__UpperCamelCase       )






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()
