
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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: List[Any]=32    )			->	List[str]:







    """simple docstring"""






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





  @slow
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
    __magic_name__				     =       DDPMScheduler(
        num_train_timesteps=1000			,							beta_start=0.0001			,							beta_end=0.02			,							beta_schedule="""linear"""			,							clip_sample=UpperCamelCase__			,							)
    __magic_name__				     =       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    )
    __magic_name__				     =       [torch.randn((4, 3, 32, 32)    ).clip(-1			,							1    ).to(UpperCamelCase__    ) for _ in range(4    )]
    __magic_name__				     =       [torch.randn((4, 3, 32, 32)    ).to(UpperCamelCase__    ) for _ in range(4    )]
    __magic_name__				     =       [torch.randint(0			,							1000			,							(4,)    ).long().to(UpperCamelCase__    ) for _ in range(4    )]

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

    # recreate the model and optimizer, and retry with DDIM
    __magic_name__     ,    __magic_name__				     =       self.get_model_optimizer(resolution=32    )
    model.train().to(UpperCamelCase__    )
    for i in range(4    ):
      optimizer.zero_grad()
      __magic_name__				     =       ddim_scheduler.add_noise(clean_images[i]			,							noise[i]			,							timesteps[i]    )
      __magic_name__				     =       model(UpperCamelCase__			,							timesteps[i]    ).sample
      __magic_name__				     =       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


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

