



"""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


_lowerCAmelCase    :Any     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self       ,	A=3_2		)							->					List[Any]:
         set_seed(0		)
         _UpperCAmelCase							:  Any											=    UNetaDModel(sample_size=A       ,	in_channels=3       ,	out_channels=3		)
         _UpperCAmelCase							:  str											=    torch.optim.SGD(model.parameters()       ,	lr=0.0_001		)
         return model, optimizer





     @slow
     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  int											=    '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         _UpperCAmelCase							:  Optional[Any]											=    DDPMScheduler(
             num_train_timesteps=1_0_0_0       ,	beta_start=0.0_001       ,	beta_end=0.02       ,	beta_schedule='''linear'''       ,	clip_sample=A       ,	)
         _UpperCAmelCase							:  List[str]											=    DDIMScheduler(
             num_train_timesteps=1_0_0_0       ,	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		)
         _UpperCAmelCase							:  Tuple											=    [torch.randn((4, 3, 3_2, 3_2)		).clip(-1       ,	1		).to(A		) for _ in range(4		)]
         _UpperCAmelCase							:  str											=    [torch.randn((4, 3, 3_2, 3_2)		).to(A		) for _ in range(4		)]
         _UpperCAmelCase							:  int											=    [torch.randint(0       ,	1_0_0_0       ,	(4,)		).long().to(A		) for _ in range(4		)]

         # train with a DDPM scheduler
         _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    self.get_model_optimizer(resolution=3_2		)
         model.train().to(A		)
         for i in range(4		):
             optimizer.zero_grad()
             _UpperCAmelCase							:  Any											=    ddpm_scheduler.add_noise(clean_images[i]       ,	noise[i]       ,	timesteps[i]		)
             _UpperCAmelCase							:  int											=    model(A       ,	timesteps[i]		).sample
             _UpperCAmelCase							:  Union[str, Any]											=    torch.nn.functional.mse_loss(A       ,	noise[i]		)
             loss.backward()
             optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    self.get_model_optimizer(resolution=3_2		)
         model.train().to(A		)
         for i in range(4		):
             optimizer.zero_grad()
             _UpperCAmelCase							:  Union[str, Any]											=    ddim_scheduler.add_noise(clean_images[i]       ,	noise[i]       ,	timesteps[i]		)
             _UpperCAmelCase							:  Tuple											=    model(A       ,	timesteps[i]		).sample
             _UpperCAmelCase							:  int											=    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		)		)







"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

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


