import unittest

import torch

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


__UpperCamelCase	: Optional[Any]    		=  False





class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       List[Any]=32		):





												'''simple docstring'''





												set_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							UNetaDModel(sample_size=UpperCamelCase__					,	in_channels=3					,	out_channels=3		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.optim.SGD(model.parameters()					,	lr=0.0001		)
												return model, optimizer



						@slow
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												SCREAMING_SNAKE_CASE							:					int           =							DDPMScheduler(
												    num_train_timesteps=1000					,	beta_start=0.0001					,	beta_end=0.02					,	beta_schedule='''linear'''					,	clip_sample=UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							DDIMScheduler(
												    num_train_timesteps=1000					,	beta_start=0.0001					,	beta_end=0.02					,	beta_schedule='''linear'''					,	clip_sample=UpperCamelCase__					,	)

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

												# shared batches for DDPM and DDIM
												set_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[torch.randn((4, 3, 32, 32)		).clip(-1					,	1		).to(UpperCamelCase__		) for _ in range(4		)]
												SCREAMING_SNAKE_CASE							:					int           =							[torch.randn((4, 3, 32, 32)		).to(UpperCamelCase__		) for _ in range(4		)]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[torch.randint(0					,	1000					,	(4,)		).long().to(UpperCamelCase__		) for _ in range(4		)]

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

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[Any]    		=  ['MaskFormerFeatureExtractor']
					__UpperCamelCase	: List[Any]    		=  ['MaskFormerImageProcessor']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[int]    		=  [
					    'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MaskFormerForInstanceSegmentation',
					    'MaskFormerModel',
					    'MaskFormerPreTrainedModel',
					]
					__UpperCamelCase	: Union[str, Any]    		=  [
					    'MaskFormerSwinBackbone',
					    'MaskFormerSwinModel',
					    'MaskFormerSwinPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
					from .configuration_maskformer_swin import MaskFormerSwinConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_maskformer import MaskFormerFeatureExtractor
										from .image_processing_maskformer import MaskFormerImageProcessor
					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskformer import (
										    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskFormerForInstanceSegmentation,
										    MaskFormerModel,
										    MaskFormerPreTrainedModel,
										)
										from .modeling_maskformer_swin import (
										    MaskFormerSwinBackbone,
										    MaskFormerSwinModel,
										    MaskFormerSwinPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase	: List[str]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure)





