




'''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 UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		Optional[Any]=32		)  ->  List[str]:


       '''simple docstring'''

       set_seed(0		)
       lowercase					:   str				  =UNetaDModel(sample_size=UpperCAmelCase     ,      in_channels=3     ,      out_channels=3		)
       lowercase					:   int				  =torch.optim.SGD(model.parameters()     ,      lr=0.0_0_0_1		)
       return model, optimizer






      @slow
      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   int				  ='''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       lowercase					:   Tuple				  =DDPMScheduler(
           num_train_timesteps=1000     ,      beta_start=0.0_0_0_1     ,      beta_end=0.0_2     ,      beta_schedule='''linear'''     ,      clip_sample=UpperCAmelCase     ,      )
       lowercase					:   Union[str, Any]				  =DDIMScheduler(
           num_train_timesteps=1000     ,      beta_start=0.0_0_0_1     ,      beta_end=0.0_2     ,      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		)
       lowercase					:   Any				  =[torch.randn((4, 3, 32, 32)		).clip(-1     ,      1		).to(UpperCAmelCase		) for _ in range(4		)]
       lowercase					:   Tuple				  =[torch.randn((4, 3, 32, 32)		).to(UpperCAmelCase		) for _ in range(4		)]
       lowercase					:   str				  =[torch.randint(0     ,      1000     ,      (4,)		).long().to(UpperCAmelCase		) for _ in range(4		)]

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

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











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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



