


'''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,
    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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :int												=["""pixel_values"""]

  def __init__(					self     :							List[str]				,							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 / 2_5_5				,							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__     :							Dict				,							):





       '''simple docstring'''

       super().__init__(**UpperCamelCase_						)
       __a       =   size if size is not None else {'shortest_edge': 2_2_4}
       __a       =   get_size_dict(UpperCamelCase_				,							default_to_square=UpperCamelCase_						)
       __a       =   crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
       __a       =   get_size_dict(UpperCamelCase_				,							default_to_square=UpperCamelCase_				,							param_name="""crop_size"""						)

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

  def __a						(					self     :							Optional[int]				,							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__     :							Dict				,							):





       '''simple docstring'''

       __a       =   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()}'''						)
       __a       =   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     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							np.ndarray				,							SCREAMING_SNAKE_CASE__     :							Dict[str, int]				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE__     :							Tuple				,							):





       '''simple docstring'''

       __a       =   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__     :							List[str]				,							):





       '''simple docstring'''

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

  def __a						(					self     :							Tuple				,							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__     :							Tuple				,							):





       '''simple docstring'''

       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]				,							):





       '''simple docstring'''

       __a       =   do_resize if do_resize is not None else self.do_resize
       __a       =   size if size is not None else self.size
       __a       =   get_size_dict(UpperCamelCase_				,							param_name="""size"""				,							default_to_square=UpperCamelCase_						)
       __a       =   resample if resample is not None else self.resample
       __a       =   do_center_crop if do_center_crop is not None else self.do_center_crop
       __a       =   crop_size if crop_size is not None else self.crop_size
       __a       =   get_size_dict(UpperCamelCase_				,							param_name="""crop_size"""				,							default_to_square=UpperCamelCase_						)
       __a       =   do_rescale if do_rescale is not None else self.do_rescale
       __a       =   rescale_factor if rescale_factor is not None else self.rescale_factor
       __a       =   do_normalize if do_normalize is not None else self.do_normalize
       __a       =   image_mean if image_mean is not None else self.image_mean
       __a       =   image_std if image_std is not None else self.image_std
       __a       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       __a       =   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:
            __a       =   [convert_to_rgb(UpperCamelCase_						) for image in images]

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

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

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

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

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

       __a       =   [to_channel_dimension_format(UpperCamelCase_				,							UpperCamelCase_						) for image in images]

       __a       =   {'pixel_values': images}
       return BatchFeature(data=UpperCamelCase_				,							tensor_type=UpperCamelCase_						)










'''simple docstring'''







from math import pi, sqrt




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




     if num <= 0:
          raise ValueError("""math domain error"""		)
     if num > 171.5:
          raise OverflowError("""math range error"""		)
     elif num - int(__SCREAMING_SNAKE_CASE		) not in (0, 0.5):
          raise NotImplementedError("""num must be an integer or a half-integer"""		)
     elif num == 0.5:
          return sqrt(__SCREAMING_SNAKE_CASE		)
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)




def        __lowercase						(      )				->					None:
     """simple docstring"""




     assert gamma(0.5		) == sqrt(__SCREAMING_SNAKE_CASE		)
     assert gamma(1		) == 1.0
     assert gamma(2		) == 1.0


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 SCREAMING_SNAKE_CASE_      =			1.0
 while num:
  SCREAMING_SNAKE_CASE_      =			float(input('Gamma of: '))
  print(f"""gamma({num}) = {gamma(num)}""")
  print('\nEnter 0 to exit...')






