



"""simple docstring"""






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__lowerCAmelCase							(unittest.TestCase  ):


					'''simple docstring'''


					a__        =     (ScoreSdeVeScheduler,)
					a__        =     ()







					@property
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						4
										snake_case_						:Tuple								=						3
										snake_case_						:Optional[int]								=						8
										snake_case_						:Optional[int]								=						8

										snake_case_						:Optional[int]								=						torch.rand((batch_size, num_channels, height, width)   )

										return sample







					@property
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						4
										snake_case_						:Optional[Any]								=						3
										snake_case_						:Tuple								=						8
										snake_case_						:Union[str, Any]								=						8

										snake_case_						:Dict								=						batch_size * num_channels * height * width
										snake_case_						:Optional[Any]								=						torch.arange(a   )
										snake_case_						:Union[str, Any]								=						sample.reshape(a    ,       a    ,       a    ,       a   )
										snake_case_						:Optional[int]								=						sample / num_elems
										snake_case_						:Any								=						sample.permute(3    ,       0    ,       1    ,       2   )

										return sample







					def 		_a (      self   ):




										"""simple docstring"""





										def model(a    ,       a    ,       *a   ):
															return sample * t / (t + 1)

										return model







					def 		_a (      self    ,       **a   ):




										"""simple docstring"""





										snake_case_						:Any								=						{
										    "num_train_timesteps": 20_00,
										    "snr": 0.15,
										    "sigma_min": 0.01,
										    "sigma_max": 13_48,
										    "sampling_eps": 1e-5,
										}

										config.update(**a   )
										return config







					def 		_a (      self    ,       a=0    ,       **a   ):




										"""simple docstring"""





										snake_case_						:List[Any]								=						dict(self.forward_default_kwargs   )

										for scheduler_class in self.scheduler_classes:
															snake_case_						:Any								=						self.dummy_sample
															snake_case_						:Optional[int]								=						0.1 * sample

															snake_case_						:List[str]								=						self.get_scheduler_config(**a   )
															snake_case_						:Union[str, Any]								=						scheduler_class(**a   )

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(a   )
																				snake_case_						:List[Any]								=						scheduler_class.from_pretrained(a   )

															snake_case_						:Dict								=						scheduler.step_pred(
															    a    ,       a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample
															snake_case_						:Optional[Any]								=						new_scheduler.step_pred(
															    a    ,       a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample

															assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

															snake_case_						:Any								=						scheduler.step_correct(a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample
															snake_case_						:Union[str, Any]								=						new_scheduler.step_correct(
															    a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample

															assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"







					def 		_a (      self    ,       a=0    ,       **a   ):




										"""simple docstring"""





										snake_case_						:Union[str, Any]								=						dict(self.forward_default_kwargs   )
										kwargs.update(a   )

										for scheduler_class in self.scheduler_classes:
															snake_case_						:Any								=						self.dummy_sample
															snake_case_						:List[str]								=						0.1 * sample

															snake_case_						:str								=						self.get_scheduler_config()
															snake_case_						:Union[str, Any]								=						scheduler_class(**a   )

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(a   )
																				snake_case_						:Optional[int]								=						scheduler_class.from_pretrained(a   )

															snake_case_						:List[Any]								=						scheduler.step_pred(
															    a    ,       a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample
															snake_case_						:Dict								=						new_scheduler.step_pred(
															    a    ,       a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample

															assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

															snake_case_						:Any								=						scheduler.step_correct(a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample
															snake_case_						:List[Any]								=						new_scheduler.step_correct(
															    a    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample

															assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"







					def 		_a (      self   ):




										"""simple docstring"""





										for timesteps in [10, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=a   )







					def 		_a (      self   ):




										"""simple docstring"""





										for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,       [1, 1_00, 10_00]   ):
															self.check_over_configs(sigma_min=a    ,       sigma_max=a   )







					def 		_a (      self   ):




										"""simple docstring"""





										for t in [0.1, 0.5, 0.75]:
															self.check_over_forward(time_step=a   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Dict								=						dict(self.forward_default_kwargs   )

										snake_case_						:List[str]								=						self.scheduler_classes[0]
										snake_case_						:Optional[int]								=						self.get_scheduler_config()
										snake_case_						:str								=						scheduler_class(**a   )

										snake_case_						:Optional[int]								=						3

										snake_case_						:Tuple								=						self.dummy_model()
										snake_case_						:Dict								=						self.dummy_sample_deter

										scheduler.set_sigmas(a   )
										scheduler.set_timesteps(a   )
										snake_case_						:Optional[int]								=						torch.manual_seed(0   )

										for i, t in enumerate(scheduler.timesteps   ):
															snake_case_						:Dict								=						scheduler.sigmas[i]

															for _ in range(scheduler.config.correct_steps   ):
																				with torch.no_grad():
																									snake_case_						:int								=						model(a    ,       a   )
																				snake_case_						:Union[str, Any]								=						scheduler.step_correct(a    ,       a    ,       generator=a    ,       **a   ).prev_sample

															with torch.no_grad():
																				snake_case_						:int								=						model(a    ,       a   )

															snake_case_						:Optional[int]								=						scheduler.step_pred(a    ,       a    ,       a    ,       generator=a    ,       **a   )
															snake_case_	,  snake_case_						:int								=						output.prev_sample, output.prev_sample_mean

										snake_case_						:str								=						torch.sum(torch.abs(a   )   )
										snake_case_						:Dict								=						torch.mean(torch.abs(a   )   )

										assert np.isclose(result_sum.item()    ,       14372758528.0   )
										assert np.isclose(result_mean.item()    ,       18714530.0   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						dict(self.forward_default_kwargs   )

										snake_case_						:int								=						kwargs.pop("num_inference_steps"    ,       a   )

										for scheduler_class in self.scheduler_classes:
															snake_case_						:Optional[int]								=						self.get_scheduler_config()
															snake_case_						:Tuple								=						scheduler_class(**a   )

															snake_case_						:Union[str, Any]								=						self.dummy_sample
															snake_case_						:Dict								=						0.1 * sample

															if num_inference_steps is not None and hasattr(a    ,       "set_timesteps"   ):
																				scheduler.set_timesteps(a   )
															elif num_inference_steps is not None and not hasattr(a    ,       "set_timesteps"   ):
																				snake_case_						:Any								=						num_inference_steps

															snake_case_						:Optional[int]								=						scheduler.step_pred(a    ,       0    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample
															snake_case_						:Union[str, Any]								=						scheduler.step_pred(a    ,       1    ,       a    ,       generator=torch.manual_seed(0   )    ,       **a   ).prev_sample

															self.assertEqual(output_a.shape    ,       sample.shape   )
															self.assertEqual(output_a.shape    ,       output_a.shape   )






"""simple docstring"""






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 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     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:
															snake_case_						:Union[str, Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:List[str]								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:str								=						[Image.fromarray(a   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if not isinstance(a    ,       a   ):
															snake_case_						:Dict								=						[images]
										snake_case_						:Optional[int]								=						[np.array(a   ).astype(np.floataa   ) / 255.0 for image in images]
										snake_case_						:Optional[int]								=						np.stack(a    ,       axis=0   )

										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:int								=						images[..., None]

										snake_case_						:str								=						torch.from_numpy(images.transpose(0    ,       3    ,       1    ,       2   )   )
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:str								=						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"""





										snake_case_						:List[str]								=						image.convert("RGB"   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										if height is None:
															snake_case_						:Optional[Any]								=						image.height
										if width is None:
															snake_case_						:Dict								=						image.width

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







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(a    ,       a   ):
															snake_case_						:str								=						[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:
																				snake_case_						:int								=						[self.convert_to_rgb(a   ) for i in image]
															if self.config.do_resize:
																				snake_case_						:Tuple								=						[self.resize(a    ,       a    ,       a   ) for i in image]
															snake_case_						:Dict								=						self.pil_to_numpy(a   )  # to np
															snake_case_						:int								=						self.numpy_to_pt(a   )  # to pt

										elif isinstance(image[0]    ,       np.ndarray   ):
															snake_case_						:Optional[Any]								=						np.concatenate(a    ,       axis=0   ) if image[0].ndim == 4 else np.stack(a    ,       axis=0   )
															snake_case_						:List[Any]								=						self.numpy_to_pt(a   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[int]								=						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   ):
															snake_case_						:Optional[Any]								=						torch.cat(a    ,       axis=0   ) if image[0].ndim == 4 else torch.stack(a    ,       axis=0   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:int								=						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]
										snake_case_						:Union[str, Any]								=						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    ,       )
															snake_case_						:Optional[int]								=						False

										if do_normalize:
															snake_case_						:Tuple								=						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"]:
															snake_case_						:List[Any]								=						(
															    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   )
															snake_case_						:Dict								=						"np"

										if output_type == "latent":
															return image

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

										snake_case_						:Any								=						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

										snake_case_						:int								=						self.pt_to_numpy(a   )

										if output_type == "np":
															return image

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



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     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:
															snake_case_						:List[Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:str								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:Any								=						[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:
															snake_case_						:Optional[int]								=						images[None, ...]
										snake_case_						:Union[str, Any]								=						images[:, :, :, 3:]
										if images.shape[-1] == 6:
															snake_case_						:Tuple								=						(images_depth * 2_55).round().astype("uint8"   )
															snake_case_						:Optional[Any]								=						[
															    Image.fromarray(self.rgblike_to_depthmap(a   )    ,       mode="I;16"   ) for image_depth in images_depth
															]
										elif images.shape[-1] == 4:
															snake_case_						:Optional[Any]								=						(images_depth * 65535.0).astype(np.uintaa   )
															snake_case_						:List[Any]								=						[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"]:
															snake_case_						:Any								=						(
															    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   )
															snake_case_						:Optional[Any]								=						"np"

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

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

										snake_case_						:List[str]								=						self.pt_to_numpy(a   )

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

