"""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								=		False
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self   ,      snake_case_=32							):







										'''simple docstring'''


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





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
										A__       :     Tuple								=			DDPMScheduler(
										    num_train_timesteps=1_000   ,      beta_start=0.00_01   ,      beta_end=0.02   ,      beta_schedule="""linear"""   ,      clip_sample=snake_case__   ,      )
										A__       :     Union[str, Any]								=			DDIMScheduler(
										    num_train_timesteps=1_000   ,      beta_start=0.00_01   ,      beta_end=0.02   ,      beta_schedule="""linear"""   ,      clip_sample=snake_case__   ,      )

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

										# shared batches for DDPM and DDIM
										set_seed(0							)
										A__       :     int								=			[torch.randn((4, 3, 32, 32)							).clip(-1   ,      1							).to(snake_case__							) for _ in range(4							)]
										A__       :     Optional[int]								=			[torch.randn((4, 3, 32, 32)							).to(snake_case__							) for _ in range(4							)]
										A__       :     List[Any]								=			[torch.randint(0   ,      1_000   ,      (4,)							).long().to(snake_case__							) for _ in range(4							)]

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

										# recreate the model and optimizer, and retry with DDIM
										A__       :     str								=			self.get_model_optimizer(resolution=32							)
										model.train().to(snake_case__							)
										for i in range(4							):
																	optimizer.zero_grad()
																	A__       :     str								=			ddim_scheduler.add_noise(clean_images[i]   ,      noise[i]   ,      timesteps[i]							)
																	A__       :     List[Any]								=			model(snake_case__   ,      timesteps[i]							).sample
																	A__       :     int								=			torch.nn.functional.mse_loss(snake_case__   ,      noise[i]							)
																	loss.backward()
																	optimizer.step()
										del model, optimizer

										self.assertTrue(torch.allclose(snake_case__   ,      snake_case__   ,      atol=1E-5							)							)
										self.assertTrue(torch.allclose(snake_case__   ,      snake_case__   ,      atol=1E-5							)							)









'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase     :  int											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

