
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Union[str, Any]       =					(KDPMaDiscreteScheduler,)
	_UpperCAmelCase       :		Union[str, Any]       =					1_0



	def  A							(  self	:    List[Any]			,  **lowercase	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			{
				    'num_train_timesteps': 1_100,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				}

				config.update(**lowercase	)
				return config



	def  A							(  self	:    int	):


				'''simple docstring'''


				for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,  [0.0002, 0.002, 0.02]	):
							self.check_over_configs(beta_start=lowercase			,  beta_end=lowercase	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowercase	)



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


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



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config(prediction_type='v_prediction'	)
				_snake_case		=			scheduler_class(**lowercase	)

				scheduler.set_timesteps(self.num_inference_steps	)

				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter * scheduler.init_noise_sigma
				_snake_case		=			sample.to(lowercase	)

				for i, t in enumerate(scheduler.timesteps	):
							_snake_case		=			scheduler.scale_model_input(lowercase			,  lowercase	)

							_snake_case		=			model(lowercase			,  lowercase	)

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase	)
							_snake_case		=			output.prev_sample

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07	) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10	) < 1E-3
				else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07	) < 1E-2
							assert abs(result_mean.item() - 0.0002	) < 1E-3



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


				'''simple docstring'''


				if torch_device == "mps":
							return
				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config()
				_snake_case		=			scheduler_class(**lowercase	)

				scheduler.set_timesteps(self.num_inference_steps	)

				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter * scheduler.init_noise_sigma
				_snake_case		=			sample.to(lowercase	)

				for i, t in enumerate(scheduler.timesteps	):
							_snake_case		=			scheduler.scale_model_input(lowercase			,  lowercase	)

							_snake_case		=			model(lowercase			,  lowercase	)

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase	)
							_snake_case		=			output.prev_sample

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.4125	) < 1E-2
							assert abs(result_mean.item() - 0.0266	) < 1E-3
				else:
							# CUDA
							assert abs(result_sum.item() - 20.4125	) < 1E-2
							assert abs(result_mean.item() - 0.0266	) < 1E-3





	def  A							(  self	:    Dict	):


				'''simple docstring'''


				if torch_device == "mps":
							return
				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config()
				_snake_case		=			scheduler_class(**lowercase	)

				scheduler.set_timesteps(self.num_inference_steps			,  device=lowercase	)

				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter.to(lowercase	) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
							_snake_case		=			scheduler.scale_model_input(lowercase			,  lowercase	)

							_snake_case		=			model(lowercase			,  lowercase	)

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase	)
							_snake_case		=			output.prev_sample

				_snake_case		=			torch.sum(torch.abs(lowercase	)	)
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				if str(lowercase	).startswith('cpu'	):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.4125	) < 1E-2
							assert abs(result_mean.item() - 0.0266	) < 1E-3
				else:
							# CUDA
							assert abs(result_sum.item() - 20.4125	) < 1E-2
							assert abs(result_mean.item() - 0.0266	) < 1E-3

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)