




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_       ):
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       CONFIG_NAME







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

										"""simple docstring"""



										super().__init__()







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	np.ndarray				)		->					PIL.Image.Image:

										"""simple docstring"""



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

										return pil_images







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

										"""simple docstring"""



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

										return images







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	np.ndarray				)		->					torch.FloatTensor:

										"""simple docstring"""



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

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







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	torch.FloatTensor				)		->					np.ndarray:

										"""simple docstring"""



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







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	List[Any]				)		->					Union[str, Any]:

										"""simple docstring"""



										return 2.0 * images - 1.0







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	List[Any]				)		->					str:

										"""simple docstring"""



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







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	PIL.Image.Image				)		->					PIL.Image.Image:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	PIL.Image.Image   ,     __UpperCAmelCase   :	Optional[int] = None   ,     __UpperCAmelCase   :	Optional[int] = None   ,     )		->					PIL.Image.Image:

										"""simple docstring"""



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

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







							def        lowercase__							(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]   ,     __UpperCAmelCase   :	Optional[int] = None   ,     __UpperCAmelCase   :	Optional[int] = None   ,     )		->					torch.Tensor:

										"""simple docstring"""



										UpperCamelCase_     				= (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(__UpperCAmelCase   ,     __UpperCAmelCase				):
													UpperCamelCase_     				= [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:
																UpperCamelCase_     				= [self.convert_to_rgb(__UpperCAmelCase				) for i in image]
													if self.config.do_resize:
																UpperCamelCase_     				= [self.resize(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				) for i in image]
													UpperCamelCase_     				= self.pil_to_numpy(__UpperCAmelCase				)  # to np
													UpperCamelCase_     				= self.numpy_to_pt(__UpperCAmelCase				)  # to pt

										elif isinstance(image[0]   ,     np.ndarray				):
													UpperCamelCase_     				= np.concatenate(__UpperCAmelCase   ,     axis=0				) if image[0].ndim == 4 else np.stack(__UpperCAmelCase   ,     axis=0				)
													UpperCamelCase_     				= self.numpy_to_pt(__UpperCAmelCase				)
													UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_     				= 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				):
													UpperCamelCase_     				= torch.cat(__UpperCAmelCase   ,     axis=0				) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase   ,     axis=0				)
													UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_     				= 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]
										UpperCamelCase_     				= 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   ,     )
													UpperCamelCase_     				= False

										if do_normalize:
													UpperCamelCase_     				= self.normalize(__UpperCAmelCase				)

										return image







							def        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     __UpperCAmelCase   :	str = "pil"   ,     __UpperCAmelCase   :	Optional[List[bool]] = None   ,     )		->					Tuple:

										"""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"]:
													UpperCamelCase_     				= (
													    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				)
													UpperCamelCase_     				= 'np'

										if output_type == "latent":
													return image

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

										UpperCamelCase_     				= 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

										UpperCamelCase_     				= self.pt_to_numpy(__UpperCAmelCase				)

										if output_type == "np":
													return image

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







class     A					(				lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       CONFIG_NAME







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

										"""simple docstring"""



										super().__init__()







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	List[str]				)		->					int:

										"""simple docstring"""



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

										return pil_images







							@staticmethod
							def        lowercase__							(    __UpperCAmelCase   :	Dict				)		->					Tuple:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	List[Any]				)		->					Dict:

										"""simple docstring"""



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

										return pil_images







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

										"""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"]:
													UpperCamelCase_     				= (
													    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				)
													UpperCamelCase_     				= 'np'

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

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

										UpperCamelCase_     				= self.pt_to_numpy(__UpperCAmelCase				)

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






import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
