






'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					(DEISMultistepScheduler,)
			__a							:							str					     =					(("num_inference_steps", 25),)






			def 				A      (   self							:						int ,      **lowercase							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = {
									    """num_train_timesteps""": 1_0_0_0,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									    """solver_order""": 2,
									}

									config.update(**lowercase			)
									return config






			def 				A      (   self							:						Tuple ,      lowercase							:						Union[str, Any]=0 ,      **lowercase							:						List[Any]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
															UpperCamelCase__         = scheduler_class(**lowercase			)
															scheduler.set_timesteps(lowercase			)
															# copy over dummy past residuals
															UpperCamelCase__         = dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(lowercase			)
																					UpperCamelCase__         = scheduler_class.from_pretrained(lowercase			)
																					new_scheduler.set_timesteps(lowercase			)
																					# copy over dummy past residuals
																					UpperCamelCase__         = dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCamelCase__    ,   UpperCamelCase__         = sample, sample
															for t in range(lowercase ,      time_step + scheduler.config.solver_order + 1			):
																					UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
																					UpperCamelCase__         = new_scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample

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






			def 				A      (   self							:						Dict			)							->    List[Any]:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Any ,      lowercase							:						List[Any]=0 ,      **lowercase							:						Optional[int]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config()
															UpperCamelCase__         = scheduler_class(**lowercase			)
															scheduler.set_timesteps(lowercase			)

															# copy over dummy past residuals (must be after setting timesteps)
															UpperCamelCase__         = dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(lowercase			)
																					UpperCamelCase__         = scheduler_class.from_pretrained(lowercase			)
																					# copy over dummy past residuals
																					new_scheduler.set_timesteps(lowercase			)

																					# copy over dummy past residual (must be after setting timesteps)
																					UpperCamelCase__         = dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
															UpperCamelCase__         = new_scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample

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






			def 				A      (   self							:						Tuple ,      lowercase							:						Any=None ,      **lowercase							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									if scheduler is None:
															UpperCamelCase__         = self.scheduler_classes[0]
															UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
															UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									return sample






			def 				A      (   self							:						Optional[int]			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)

									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config()
															UpperCamelCase__         = scheduler_class(**lowercase			)

															UpperCamelCase__         = self.dummy_sample
															UpperCamelCase__         = 0.1 * sample

															if num_inference_steps is not None and hasattr(lowercase ,      """set_timesteps"""			):
																					scheduler.set_timesteps(lowercase			)
															elif num_inference_steps is not None and not hasattr(lowercase ,      """set_timesteps"""			):
																					UpperCamelCase__         = num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
															UpperCamelCase__         = dummy_past_residuals[: scheduler.config.solver_order]

															UpperCamelCase__         = scheduler.timesteps[5]
															UpperCamelCase__         = scheduler.timesteps[6]

															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample

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






			def 				A      (   self							:						Tuple			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = DEISMultistepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

									UpperCamelCase__         = DPMSolverSinglestepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DPMSolverMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = UniPCMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DEISMultistepScheduler.from_config(scheduler.config			)

									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3






			def 				A      (   self							:						Dict			)							->    List[str]:

									'''simple docstring'''







									for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=lowercase			)






			def 				A      (   self							:						Optional[int]			)							->    Optional[Any]:

									'''simple docstring'''







									self.check_over_configs(thresholding=lowercase			)
									for order in [1, 2, 3]:
															for solver_type in ["logrho"]:
																					for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    thresholding=lowercase ,      prediction_type=lowercase ,      sample_max_value=lowercase ,      algorithm_type="""deis""" ,      solver_order=lowercase ,      solver_type=lowercase ,      )






			def 				A      (   self							:						Tuple			)							->    str:

									'''simple docstring'''







									for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowercase			)






			def 				A      (   self							:						Tuple			)							->    List[str]:

									'''simple docstring'''







									for algorithm_type in ["deis"]:
															for solver_type in ["logrho"]:
																					for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    solver_order=lowercase ,      solver_type=lowercase ,      prediction_type=lowercase ,      algorithm_type=lowercase ,      )
																																	UpperCamelCase__         = self.full_loop(
																																	    solver_order=lowercase ,      solver_type=lowercase ,      prediction_type=lowercase ,      algorithm_type=lowercase ,      )
																																	assert not torch.isnan(lowercase			).any(), "Samples have nan numbers"






			def 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									self.check_over_configs(lower_order_final=lowercase			)
									self.check_over_configs(lower_order_final=lowercase			)






			def 				A      (   self							:						Optional[Any]			)							->    int:

									'''simple docstring'''







									for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_forward(num_inference_steps=lowercase ,      time_step=0			)






			def 				A      (   self							:						str			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop()
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3






			def 				A      (   self							:						Dict			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction"""			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.0_9_1			) < 1e-3







			def 				A      (   self							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(thresholding=lowercase ,      dynamic_thresholding_ratio=0			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter.half()
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									assert sample.dtype == torch.floataa








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase_				: str    	=							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if isinstance(_A						,      torch.Tensor					):
												return image
						elif isinstance(_A						,      PIL.Image.Image					):
												UpperCamelCase__         = [image]

						UpperCamelCase__         = [trans(img.convert("""RGB"""					)					) for img in image]
						UpperCamelCase__         = torch.stack(_A					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						Dict ,      lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCamelCase__         = DDIMScheduler.from_config(scheduler.config			)

									self.register_modules(unet=lowercase ,      scheduler=lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									if strength < 0 or strength > 1:
															raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"			)






			def 				A      (   self							:						str ,      lowercase							:						List[Any] ,      lowercase							:						Dict ,      lowercase							:						List[str]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

									UpperCamelCase__         = max(num_inference_steps - init_timestep ,      0			)
									UpperCamelCase__         = self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict ,      lowercase							:						Tuple ,      lowercase							:						int=None			)							->    int:

									'''simple docstring'''







									if not isinstance(lowercase ,      (torch.Tensor, PIL.Image.Image, list)			):
															raise ValueError(
															    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

									if isinstance(lowercase ,      lowercase			) and len(lowercase			) != batch_size:
															raise ValueError(
															    f"You have passed a list of generators of length {len(lowercase			)}, but requested an effective batch"
															    f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									print("""add noise to latents at timestep""" ,      lowercase			)
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = init_latents

									return latents







			@torch.no_grad()
			def __call__(   self							:						Any ,      lowercase							:						Union[torch.FloatTensor, PIL.Image.Image] = None ,      lowercase							:						float = 0.8 ,      lowercase							:						int = 1 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						float = 0.0 ,      lowercase							:						int = 5_0 ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      )							->    Union[ImagePipelineOutput, Tuple]:

									'''simple docstring'''







									self.check_inputs(lowercase			)

									# 2. Preprocess image
									UpperCamelCase__         = preprocess(lowercase			)

									# 3. set timesteps
									self.scheduler.set_timesteps(lowercase ,      device=self.device			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      self.device			)
									UpperCamelCase__         = timesteps[:1].repeat(lowercase			)

									# 4. Prepare latent variables
									UpperCamelCase__         = self.prepare_latents(lowercase ,      lowercase ,      lowercase ,      self.unet.dtype ,      self.device ,      lowercase			)
									UpperCamelCase__         = latents

									# 5. Denoising loop
									for t in self.progress_bar(lowercase			):
															# 1. predict noise model_output
															UpperCamelCase__         = self.unet(lowercase ,      lowercase			).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															UpperCamelCase__         = self.scheduler.step(
															    lowercase ,      lowercase ,      lowercase ,      eta=lowercase ,      use_clipped_model_output=lowercase ,      generator=lowercase ,      ).prev_sample

									UpperCamelCase__         = (image / 2 + 0.5).clamp(0 ,      1			)
									UpperCamelCase__         = image.cpu().permute(0 ,      2 ,      3 ,      1			).numpy()
									if output_type == "pil":
															UpperCamelCase__         = self.numpy_to_pil(lowercase			)

									if not return_dict:
															return (image, latent_timestep.item())

									return ImagePipelineOutput(images=lowercase			)
