
"""simple docstring"""







import unittest

import torch

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


_SCREAMING_SNAKE_CASE	       =      False







class 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict=32    ):
												set_seed(0    )
												__snake_case     			=       UNetaDModel(sample_size=snake_case_							,						in_channels=3							,						out_channels=3    )
												__snake_case     			=       torch.optim.SGD(model.parameters()							,						lr=0.0001    )
												return model, optimizer





					@slow
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												__snake_case     			=       DDPMScheduler(
												    num_train_timesteps=1000							,						beta_start=0.0001							,						beta_end=0.02							,						beta_schedule="linear"							,						clip_sample=snake_case_							,						)
												__snake_case     			=       DDIMScheduler(
												    num_train_timesteps=1000							,						beta_start=0.0001							,						beta_end=0.02							,						beta_schedule="linear"							,						clip_sample=snake_case_							,						)

												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(snake_case_    ) for _ in range(4    )]
												__snake_case     			=       [torch.randn((4, 3, 32, 32)    ).to(snake_case_    ) for _ in range(4    )]
												__snake_case     			=       [torch.randint(0							,						1000							,						(4,)    ).long().to(snake_case_    ) for _ in range(4    )]

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

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


"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
