






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__							=     False




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self			,						a__=32    ) ->      Optional[Any]:






            '''simple docstring'''





            set_seed(0    )
            __snake_case			:int          =						UNetaDModel(sample_size=a__			,						in_channels=3			,						out_channels=3    )
            __snake_case			:Tuple          =						torch.optim.SGD(model.parameters()			,						lr=0.00_01    )
            return model, optimizer



       @slow
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
            __snake_case			:List[str]          =						DDPMScheduler(
                num_train_timesteps=10_00			,						beta_start=0.00_01			,						beta_end=0.02			,						beta_schedule="""linear"""			,						clip_sample=a__			,						)
            __snake_case			:Optional[Any]          =						DDIMScheduler(
                num_train_timesteps=10_00			,						beta_start=0.00_01			,						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    )
            __snake_case			:int          =						[torch.randn((4, 3, 32, 32)    ).clip(-1			,						1    ).to(a__    ) for _ in range(4    )]
            __snake_case			:Tuple          =						[torch.randn((4, 3, 32, 32)    ).to(a__    ) for _ in range(4    )]
            __snake_case			:Tuple          =						[torch.randint(0			,						10_00			,						(4,)    ).long().to(a__    ) for _ in range(4    )]

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

            # recreate the model and optimizer, and retry with DDIM
            __snake_case       , __snake_case			:str          =						self.get_model_optimizer(resolution=32    )
            model.train().to(a__    )
            for i in range(4    ):
                 optimizer.zero_grad()
                 __snake_case			:Optional[int]          =						ddim_scheduler.add_noise(clean_images[i]			,						noise[i]			,						timesteps[i]    )
                 __snake_case			:Optional[Any]          =						model(a__			,						timesteps[i]    ).sample
                 __snake_case			:Any          =						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_tokenizers_available, is_torch_available


lowerCamelCase__							=     {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CanineForMultipleChoice""",
         """CanineForQuestionAnswering""",
         """CanineForSequenceClassification""",
         """CanineForTokenClassification""",
         """CanineLayer""",
         """CanineModel""",
         """CaninePreTrainedModel""",
         """load_tf_weights_in_canine""",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

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


