


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	:     Tuple   =False







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def   lowercase_       ( self      ,  __lowercase=3_2							)    ->			Tuple:
										set_seed(0							)
										lowerCAmelCase_       :					Optional[int]			 = UNetaDModel(sample_size=lowerCAmelCase__      ,  in_channels=3      ,  out_channels=3							)
										lowerCAmelCase_       :					Optional[int]			 = torch.optim.SGD(model.parameters()      ,  lr=0.00_01							)
										return model, optimizer




						@slow
						def   lowercase_       ( self							)    ->			Dict:
										lowerCAmelCase_       :					Any			 = "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
										lowerCAmelCase_       :					Optional[Any]			 = DDPMScheduler(
										    num_train_timesteps=1_0_0_0      ,  beta_start=0.00_01      ,  beta_end=0.02      ,  beta_schedule='''linear'''      ,  clip_sample=lowerCAmelCase__      ,  )
										lowerCAmelCase_       :					Any			 = DDIMScheduler(
										    num_train_timesteps=1_0_0_0      ,  beta_start=0.00_01      ,  beta_end=0.02      ,  beta_schedule='''linear'''      ,  clip_sample=lowerCAmelCase__      ,  )

										assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

										# shared batches for DDPM and DDIM
										set_seed(0							)
										lowerCAmelCase_       :					Tuple			 = [torch.randn((4, 3, 3_2, 3_2)							).clip(-1      ,  1							).to(lowerCAmelCase__							) for _ in range(4							)]
										lowerCAmelCase_       :					Tuple			 = [torch.randn((4, 3, 3_2, 3_2)							).to(lowerCAmelCase__							) for _ in range(4							)]
										lowerCAmelCase_       :					Union[str, Any]			 = [torch.randint(0      ,  1_0_0_0      ,  (4,)							).long().to(lowerCAmelCase__							) for _ in range(4							)]

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

										# recreate the model and optimizer, and retry with DDIM
										lowerCAmelCase_       :					List[str]			 = self.get_model_optimizer(resolution=3_2							)
										model.train().to(lowerCAmelCase__							)
										for i in range(4							):
														optimizer.zero_grad()
														lowerCAmelCase_       :					Union[str, Any]			 = ddim_scheduler.add_noise(clean_images[i]      ,  noise[i]      ,  timesteps[i]							)
														lowerCAmelCase_       :					Optional[Any]			 = model(lowerCAmelCase__      ,  timesteps[i]							).sample
														lowerCAmelCase_       :					List[Any]			 = torch.nn.functional.mse_loss(lowerCAmelCase__      ,  noise[i]							)
														loss.backward()
														optimizer.step()
										del model, optimizer

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






from typing import TYPE_CHECKING

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


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






