

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				List[str]       						=						CONFIG_NAME






						@register_to_config
						def __init__(					self   ,							_A = True   ,							_A = 8   ,							_A = "lanczos"   ,							_A = True   ,							_A = False   ,							):







													'''simple docstring'''

													super().__init__()






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													if images.ndim == 3:
																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
													__SCREAMING_SNAKE_CASE								=      (images * 255).round().astype('uint8'	)
													if images.shape[-1] == 1:
																				# special case for grayscale (single channel) images
																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image.squeeze()   ,							mode='L'	) for image in images]
													else:
																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(_A	) for image in images]

													return pil_images






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													if not isinstance(_A   ,							_A	):
																				__SCREAMING_SNAKE_CASE								=      [images]
													__SCREAMING_SNAKE_CASE								=      [np.array(_A	).astype(np.floataa	) / 255.0 for image in images]
													__SCREAMING_SNAKE_CASE								=      np.stack(_A   ,							axis=0	)

													return images






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													if images.ndim == 3:
																				__SCREAMING_SNAKE_CASE								=      images[..., None]

													__SCREAMING_SNAKE_CASE								=      torch.from_numpy(images.transpose(0   ,							3   ,							1   ,							2	)	)
													return images






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      images.cpu().permute(0   ,							2   ,							3   ,							1	).float().numpy()
													return images






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													return 2.0 * images - 1.0






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													return (images / 2 + 0.5).clamp(0   ,							1	)






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      image.convert('RGB'	)
													return image






						def  _A			(					self   ,							_A   ,							_A = None   ,							_A = None   ,							):







													'''simple docstring'''

													if height is None:
																				__SCREAMING_SNAKE_CASE								=      image.height
													if width is None:
																				__SCREAMING_SNAKE_CASE								=      image.width

													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      (
													    x - x % self.config.vae_scale_factor for x in (width, height)
													)  # resize to integer multiple of vae_scale_factor
													__SCREAMING_SNAKE_CASE								=      image.resize((width, height)   ,							resample=PIL_INTERPOLATION[self.config.resample]	)
													return image






						def  _A			(					self   ,							_A   ,							_A = None   ,							_A = None   ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      (PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(_A   ,							_A	):
																				__SCREAMING_SNAKE_CASE								=      [image]
													elif not (isinstance(_A   ,							_A	) and all(isinstance(_A   ,							_A	) for i in image	)):
																				raise ValueError(
																				    f"""Input is in incorrect format: {[type(_A	) for i in image]}. Currently, we only support {", ".join(_A	)}"""	)

													if isinstance(image[0]   ,							PIL.Image.Image	):
																				if self.config.do_convert_rgb:
																											__SCREAMING_SNAKE_CASE								=      [self.convert_to_rgb(_A	) for i in image]
																				if self.config.do_resize:
																											__SCREAMING_SNAKE_CASE								=      [self.resize(_A   ,							_A   ,							_A	) for i in image]
																				__SCREAMING_SNAKE_CASE								=      self.pil_to_numpy(_A	)  # to np
																				__SCREAMING_SNAKE_CASE								=      self.numpy_to_pt(_A	)  # to pt

													elif isinstance(image[0]   ,							np.ndarray	):
																				__SCREAMING_SNAKE_CASE								=      np.concatenate(_A   ,							axis=0	) if image[0].ndim == 4 else np.stack(_A   ,							axis=0	)
																				__SCREAMING_SNAKE_CASE								=      self.numpy_to_pt(_A	)
																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      image.shape
																				if self.config.do_resize and (
																				    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																				):
																											raise ValueError(
																											    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																											    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""	)

													elif isinstance(image[0]   ,							torch.Tensor	):
																				__SCREAMING_SNAKE_CASE								=      torch.cat(_A   ,							axis=0	) if image[0].ndim == 4 else torch.stack(_A   ,							axis=0	)
																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      image.shape

																				# don't need any preprocess if the image is latents
																				if channel == 4:
																											return image

																				if self.config.do_resize and (
																				    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																				):
																											raise ValueError(
																											    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																											    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""	)

        # expected range [0,1], normalize to [-1,1]
													__SCREAMING_SNAKE_CASE								=      self.config.do_normalize
													if image.min() < 0:
																				warnings.warn(
																				    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																				    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   ,							_A   ,							)
																				__SCREAMING_SNAKE_CASE								=      False

													if do_normalize:
																				__SCREAMING_SNAKE_CASE								=      self.normalize(_A	)

													return image






						def  _A			(					self   ,							_A   ,							_A = "pil"   ,							_A = None   ,							):







													'''simple docstring'''

													if not isinstance(_A   ,							torch.Tensor	):
																				raise ValueError(
																				    f"""Input for postprocessing is in incorrect format: {type(_A	)}. We only support pytorch tensor"""	)
													if output_type not in ["latent", "pt", "np", "pil"]:
																				__SCREAMING_SNAKE_CASE								=      (
																				    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																				    '`pil`, `np`, `pt`, `latent`'
																				)
																				deprecate('Unsupported output_type'   ,							'1.0.0'   ,							_A   ,							standard_warn=_A	)
																				__SCREAMING_SNAKE_CASE								=      'np'

													if output_type == "latent":
																				return image

													if do_denormalize is None:
																				__SCREAMING_SNAKE_CASE								=      [self.config.do_normalize] * image.shape[0]

													__SCREAMING_SNAKE_CASE								=      torch.stack(
													    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

													if output_type == "pt":
																				return image

													__SCREAMING_SNAKE_CASE								=      self.pt_to_numpy(_A	)

													if output_type == "np":
																				return image

													if output_type == "pil":
																				return self.numpy_to_pil(_A	)







class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[int]       						=						CONFIG_NAME






						@register_to_config
						def __init__(					self   ,							_A = True   ,							_A = 8   ,							_A = "lanczos"   ,							_A = True   ,							):







													'''simple docstring'''

													super().__init__()






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													if images.ndim == 3:
																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
													__SCREAMING_SNAKE_CASE								=      (images * 255).round().astype('uint8'	)
													if images.shape[-1] == 1:
																				# special case for grayscale (single channel) images
																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image.squeeze()   ,							mode='L'	) for image in images]
													else:
																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image[:, :, :3]	) for image in images]

													return pil_images






						@staticmethod
						def  _A			(					_A	):







													'''simple docstring'''

													return image[:, :, 1] * 2**8 + image[:, :, 2]






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													if images.ndim == 3:
																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
													__SCREAMING_SNAKE_CASE								=      images[:, :, :, 3:]
													if images.shape[-1] == 6:
																				__SCREAMING_SNAKE_CASE								=      (images_depth * 255).round().astype('uint8'	)
																				__SCREAMING_SNAKE_CASE								=      [
																				    Image.fromarray(self.rgblike_to_depthmap(_A	)   ,							mode='I;16'	) for image_depth in images_depth
																				]
													elif images.shape[-1] == 4:
																				__SCREAMING_SNAKE_CASE								=      (images_depth * 65_535.0).astype(np.uintaa	)
																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(_A   ,							mode='I;16'	) for image_depth in images_depth]
													else:
																				raise Exception('Not supported'	)

													return pil_images






						def  _A			(					self   ,							_A   ,							_A = "pil"   ,							_A = None   ,							):







													'''simple docstring'''

													if not isinstance(_A   ,							torch.Tensor	):
																				raise ValueError(
																				    f"""Input for postprocessing is in incorrect format: {type(_A	)}. We only support pytorch tensor"""	)
													if output_type not in ["latent", "pt", "np", "pil"]:
																				__SCREAMING_SNAKE_CASE								=      (
																				    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																				    '`pil`, `np`, `pt`, `latent`'
																				)
																				deprecate('Unsupported output_type'   ,							'1.0.0'   ,							_A   ,							standard_warn=_A	)
																				__SCREAMING_SNAKE_CASE								=      'np'

													if do_denormalize is None:
																				__SCREAMING_SNAKE_CASE								=      [self.config.do_normalize] * image.shape[0]

													__SCREAMING_SNAKE_CASE								=      torch.stack(
													    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

													__SCREAMING_SNAKE_CASE								=      self.pt_to_numpy(_A	)

													if output_type == "np":
																				if image.shape[-1] == 6:
																											__SCREAMING_SNAKE_CASE								=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]	) for im in image]   ,							axis=0	)
																				else:
																											__SCREAMING_SNAKE_CASE								=      image[:, :, :, 3:]
																				return image[:, :, :, :3], image_depth

													if output_type == "pil":
																				return self.numpy_to_pil(_A	), self.numpy_to_depth(_A	)
													else:
																				raise Exception(f"""This type {output_type} is not supported"""	)











def 			__lowercase     (				a__							,     a__     )       ->				List[str]:
							if n == 0:
														return 1

							elif n % 2 == 1:
														return binary_exponentiation(a__							,     n - 1     ) * a

							else:
														__SCREAMING_SNAKE_CASE								=      binary_exponentiation(a__							,     n / 2     )
														return b * b


if __name__ == "__main__":
			try:
						lowerCAmelCase__	:  str 		=int(input('''Enter Base : ''').strip())
						lowerCAmelCase__	:  Optional[int] 		=int(input('''Enter Power : ''').strip())
			except ValueError:
						print('''Invalid literal for integer''')

			lowerCAmelCase__	:  Any 		=binary_exponentiation(BASE, POWER)
			print(F'''{BASE}^({POWER}) : {RESULT}''')






