






"""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[str]   			= False




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  List[Any]=32):




          '''simple docstring'''


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



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		DDPMScheduler(
              num_train_timesteps=1000						,	beta_start=0.00_01						,	beta_end=0.02						,	beta_schedule='''linear'''						,	clip_sample=_SCREAMING_SNAKE_CASE						,	)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		DDIMScheduler(
              num_train_timesteps=1000						,	beta_start=0.00_01						,	beta_end=0.02						,	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)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[torch.randn((4, 3, 32, 32)).clip(-1						,	1).to(_SCREAMING_SNAKE_CASE) for _ in range(4)]
          SCREAMING_SNAKE_CASE_      :  Any    						=		[torch.randn((4, 3, 32, 32)).to(_SCREAMING_SNAKE_CASE) for _ in range(4)]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[torch.randint(0						,	1000						,	(4,)).long().to(_SCREAMING_SNAKE_CASE) for _ in range(4)]

          # train with a DDPM scheduler
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.get_model_optimizer(resolution=32)
          model.train().to(_SCREAMING_SNAKE_CASE)
          for i in range(4):
              optimizer.zero_grad()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ddpm_scheduler.add_noise(clean_images[i]						,	noise[i]						,	timesteps[i])
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(_SCREAMING_SNAKE_CASE						,	timesteps[i]).sample
              SCREAMING_SNAKE_CASE_      :  str    						=		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
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_model_optimizer(resolution=32)
          model.train().to(_SCREAMING_SNAKE_CASE)
          for i in range(4):
              optimizer.zero_grad()
              SCREAMING_SNAKE_CASE_      :  int    						=		ddim_scheduler.add_noise(clean_images[i]						,	noise[i]						,	timesteps[i])
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(_SCREAMING_SNAKE_CASE						,	timesteps[i]).sample
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
