




'''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


UpperCamelCase_						:      List[Any]       =						False

class        _a    (						unittest.TestCase							):






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE=32   )							->      Union[str, Any]:
          set_seed(0   )
          _snake_case							=				UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE ,in_channels=3 ,out_channels=3   )
          _snake_case							=				torch.optim.SGD(model.parameters() ,lr=0.0_0_0_1   )
          return model, optimizer






     @slow
     def     _lowercase     (			self   )							->      str:
          _snake_case							=				"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
          _snake_case							=				DDPMScheduler(
              num_train_timesteps=1_000 ,beta_start=0.0_0_0_1 ,beta_end=0.0_2 ,beta_schedule="linear" ,clip_sample=_SCREAMING_SNAKE_CASE ,)
          _snake_case							=				DDIMScheduler(
              num_train_timesteps=1_000 ,beta_start=0.0_0_0_1 ,beta_end=0.0_2 ,beta_schedule="linear" ,clip_sample=_SCREAMING_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(_SCREAMING_SNAKE_CASE   ) for _ in range(4   )]
          _snake_case							=				[torch.randn((4, 3, 32, 32)   ).to(_SCREAMING_SNAKE_CASE   ) for _ in range(4   )]
          _snake_case							=				[torch.randint(0 ,1_000 ,(4,)   ).long().to(_SCREAMING_SNAKE_CASE   ) for _ in range(4   )]

          # train with a DDPM scheduler
          _snake_case							, _snake_case							=				self.get_model_optimizer(resolution=32   )
          model.train().to(_SCREAMING_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(_SCREAMING_SNAKE_CASE ,timesteps[i]   ).sample
               _snake_case							=				torch.nn.functional.mse_loss(_SCREAMING_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(_SCREAMING_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(_SCREAMING_SNAKE_CASE ,timesteps[i]   ).sample
               _snake_case							=				torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,noise[i]   )
               loss.backward()
               optimizer.step()
          del model, optimizer

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








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


