'''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,
)

'''simple docstring'''




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=(DPMSolverSDEScheduler,)
     lowerCamelCase  :					List[str]							=1_0






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {
             '''num_train_timesteps''': 11_00,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**a   )
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,    [0.00_02, 0.0_02, 0.02]   ):
             self.check_over_configs(beta_start=a  ,    beta_end=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75   ) < 1e-2
             assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53   ) < 1e-2
             assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03   ) < 1e-2
             assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25   ) < 1e-2
             assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38   ) < 1e-2
             assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a  ,    use_karras_sigmas=a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
