



"""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


lowercase__    :  Union[str, Any]							    =	False




class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Tuple=32				):
     set_seed(0				)
     snake_case_ :     Dict				=	UNetaDModel(sample_size=lowercase_  ,     in_channels=3  ,     out_channels=3				)
     snake_case_ :     Any				=	torch.optim.SGD(model.parameters()  ,     lr=0.00_01				)
     return model, optimizer






 @slow
 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
     snake_case_ :     Tuple				=	DDPMScheduler(
         num_train_timesteps=1000  ,     beta_start=0.00_01  ,     beta_end=0.02  ,     beta_schedule='''linear'''  ,     clip_sample=lowercase_  ,     )
     snake_case_ :     Optional[int]				=	DDIMScheduler(
         num_train_timesteps=1000  ,     beta_start=0.00_01  ,     beta_end=0.02  ,     beta_schedule='''linear'''  ,     clip_sample=lowercase_  ,     )

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

     # shared batches for DDPM and DDIM
     set_seed(0				)
     snake_case_ :     List[Any]				=	[torch.randn((4, 3, 32, 32)				).clip(-1  ,     1				).to(lowercase_				) for _ in range(4				)]
     snake_case_ :     Optional[int]				=	[torch.randn((4, 3, 32, 32)				).to(lowercase_				) for _ in range(4				)]
     snake_case_ :     int				=	[torch.randint(0  ,     1000  ,     (4,)				).long().to(lowercase_				) for _ in range(4				)]

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

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

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










"""simple docstring"""

from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





