


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   A__							(						__lowerCamelCase     ):
						UpperCAmelCase									=  CONFIG_NAME




						@register_to_config
						def __init__(				self    :					Optional[Any] ,			_a    :					bool = True ,			_a    :					int = 8 ,			_a    :					str = "lanczos" ,			_a    :					bool = True ,			_a    :					bool = False ,			)							->   int:


										"""simple docstring"""





										super().__init__()




						@staticmethod
						def 							__UpperCamelCase (				_a    :					np.ndarray     )							->   PIL.Image.Image:


										"""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(_UpperCamelCase     ) for image in images]

										return pil_images




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Union[List[PIL.Image.Image], PIL.Image.Image]     )							->   np.ndarray:


										"""simple docstring"""





										if not isinstance(_UpperCamelCase ,			_UpperCamelCase     ):
														_SCREAMING_SNAKE_CASE	     =[images]
										_SCREAMING_SNAKE_CASE	     =[np.array(_UpperCamelCase     ).astype(np.floataa     ) / 2_55.0 for image in images]
										_SCREAMING_SNAKE_CASE	     =np.stack(_UpperCamelCase ,			axis=0     )

										return images




						@staticmethod
						def 							__UpperCamelCase (				_a    :					np.ndarray     )							->   torch.FloatTensor:


										"""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 							__UpperCamelCase (				_a    :					torch.FloatTensor     )							->   np.ndarray:


										"""simple docstring"""





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




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Optional[Any]     )							->   List[str]:


										"""simple docstring"""





										return 2.0 * images - 1.0




						@staticmethod
						def 							__UpperCamelCase (				_a    :					str     )							->   Optional[int]:


										"""simple docstring"""





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




						@staticmethod
						def 							__UpperCamelCase (				_a    :					PIL.Image.Image     )							->   PIL.Image.Image:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					PIL.Image.Image ,			_a    :					Optional[int] = None ,			_a    :					Optional[int] = None ,			)							->   PIL.Image.Image:


										"""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 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Union[torch.FloatTensor, PIL.Image.Image, np.ndarray] ,			_a    :					Optional[int] = None ,			_a    :					Optional[int] = None ,			)							->   torch.Tensor:


										"""simple docstring"""





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

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

										elif isinstance(image[0] ,			np.ndarray     ):
														_SCREAMING_SNAKE_CASE	     =np.concatenate(_UpperCamelCase ,			axis=0     ) if image[0].ndim == 4 else np.stack(_UpperCamelCase ,			axis=0     )
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pt(_UpperCamelCase     )
														_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(_UpperCamelCase ,			axis=0     ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase ,			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()}]" ,			_UpperCamelCase ,			)
														_SCREAMING_SNAKE_CASE	     =False

										if do_normalize:
														_SCREAMING_SNAKE_CASE	     =self.normalize(_UpperCamelCase     )

										return image




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					torch.FloatTensor ,			_a    :					str = "pil" ,			_a    :					Optional[List[bool]] = None ,			)							->   Optional[int]:


										"""simple docstring"""





										if not isinstance(_UpperCamelCase ,			torch.Tensor     ):
														raise ValueError(
														    f"Input for postprocessing is in incorrect format: {type(_UpperCamelCase     )}. 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''' ,			_UpperCamelCase ,			standard_warn=_UpperCamelCase     )
														_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(_UpperCamelCase     )

										if output_type == "np":
														return image

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

class   A__							(						__lowerCamelCase     ):
						UpperCAmelCase									=  CONFIG_NAME




						@register_to_config
						def __init__(				self    :					List[str] ,			_a    :					bool = True ,			_a    :					int = 8 ,			_a    :					str = "lanczos" ,			_a    :					bool = True ,			)							->   int:


										"""simple docstring"""





										super().__init__()




						@staticmethod
						def 							__UpperCamelCase (				_a    :					int     )							->   Union[str, Any]:


										"""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 							__UpperCamelCase (				_a    :					Dict     )							->   List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					Any     )							->   Dict:


										"""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(_UpperCamelCase     ) ,			mode='''I;16'''     ) for image_depth in images_depth
														]
										elif images.shape[-1] == 4:
														_SCREAMING_SNAKE_CASE	     =(images_depth * 6_5535.0).astype(np.uintaa     )
														_SCREAMING_SNAKE_CASE	     =[Image.fromarray(_UpperCamelCase ,			mode='''I;16'''     ) for image_depth in images_depth]
										else:
														raise Exception('''Not supported'''     )

										return pil_images




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					torch.FloatTensor ,			_a    :					str = "pil" ,			_a    :					Optional[List[bool]] = None ,			)							->   str:


										"""simple docstring"""





										if not isinstance(_UpperCamelCase ,			torch.Tensor     ):
														raise ValueError(
														    f"Input for postprocessing is in incorrect format: {type(_UpperCamelCase     )}. 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''' ,			_UpperCamelCase ,			standard_warn=_UpperCamelCase     )
														_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(_UpperCamelCase     )

										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(_UpperCamelCase     ), self.numpy_to_depth(_UpperCamelCase     )
										else:
														raise Exception(f"This type {output_type} is not supported"     )





def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=1.0
    for i in range(1,				r + 1	):
        ret *= (n + 1 - i) / i
    return round(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return choose(8 + n,				__UpperCamelCase	) + choose(9 + n,				__UpperCamelCase	) - 10



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return sum(non_bouncy_exact(__UpperCamelCase	) for i in range(1,				n + 1	)	)



def 	UpperCAmelCase_    (	__UpperCamelCase = 100	):
    return non_bouncy_upto(__UpperCamelCase	)


if __name__ == "__main__":
    print(f"""{solution() = }""")






