






import unittest

import torch

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


__A      =				False





class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   Optional[Any]=32   )    ->				int:
												set_seed(0   )
												_A								= UNetaDModel(sample_size=__A		,     in_channels=3		,     out_channels=3   )
												_A								= torch.optim.SGD(model.parameters()		,     lr=0.0_001   )
												return model, optimizer



							@slow
							def       __A (  self:   Union[str, Any]   )    ->				Tuple:
												_A								= '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												_A								= DDPMScheduler(
												    num_train_timesteps=10_00		,     beta_start=0.0_001		,     beta_end=0.02		,     beta_schedule='''linear'''		,     clip_sample=__A		,     )
												_A								= DDIMScheduler(
												    num_train_timesteps=10_00		,     beta_start=0.0_001		,     beta_end=0.02		,     beta_schedule='''linear'''		,     clip_sample=__A		,     )

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

												# shared batches for DDPM and DDIM
												set_seed(0   )
												_A								= [torch.randn((4, 3, 32, 32)   ).clip(-1		,     1   ).to(__A   ) for _ in range(4   )]
												_A								= [torch.randn((4, 3, 32, 32)   ).to(__A   ) for _ in range(4   )]
												_A								= [torch.randint(0		,     10_00		,     (4,)   ).long().to(__A   ) for _ in range(4   )]

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

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

												self.assertTrue(torch.allclose(__A		,     __A		,     atol=1e-5   )   )
												self.assertTrue(torch.allclose(__A		,     __A		,     atol=1e-5   )   )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

				__A      =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



