


"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase    (				snake_case_     ):
			__UpperCAmelCase			:	Any							  =  (CMStochasticIterativeScheduler,)
			__UpperCAmelCase			:	int							  =  10







			def 			lowerCamelCase		(     self						,     **UpperCamelCase__						)     ->		Optional[int]:

							'''simple docstring'''




							snake_case   :				Dict				 =   {
							    "num_train_timesteps": 201,
							    "sigma_min": 0.002,
							    "sigma_max": 80.0,
							}

							config.update(**UpperCamelCase__						)
							return config







			def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				int				 =   10

							snake_case   :				int				 =   self.get_scheduler_config()
							snake_case   :				List[str]				 =   self.scheduler_classes[0](**UpperCamelCase__						)

							scheduler.set_timesteps(UpperCamelCase__						)

							snake_case   :				Optional[Any]				 =   scheduler.timesteps[0]
							snake_case   :				Optional[Any]				 =   scheduler.timesteps[1]

							snake_case   :				List[Any]				 =   self.dummy_sample
							snake_case   :				str				 =   0.1 * sample

							snake_case   :				str				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						).prev_sample
							snake_case   :				Union[str, Any]				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						).prev_sample

							self.assertEqual(output_a.shape						,     sample.shape						)
							self.assertEqual(output_a.shape						,     output_a.shape						)







			def 			lowerCamelCase		(     self						)     ->		List[str]:

							'''simple docstring'''




							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				Union[str, Any]				 =   self.scheduler_classes[0]
							snake_case   :				Tuple				 =   self.get_scheduler_config()
							snake_case   :				int				 =   scheduler_class(**UpperCamelCase__						)

							snake_case   :				int				 =   1
							scheduler.set_timesteps(UpperCamelCase__						)
							snake_case   :				Optional[Any]				 =   scheduler.timesteps

							snake_case   :				Union[str, Any]				 =   torch.manual_seed(0						)

							snake_case   :				Optional[Any]				 =   self.dummy_model()
							snake_case   :				Any				 =   self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(UpperCamelCase__						):
											# 1. scale model input
											snake_case   :				List[Any]				 =   scheduler.scale_model_input(UpperCamelCase__						,     UpperCamelCase__						)

											# 2. predict noise residual
											snake_case   :				Any				 =   model(UpperCamelCase__						,     UpperCamelCase__						)

											# 3. predict previous sample x_t-1
											snake_case   :				Optional[Any]				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     generator=UpperCamelCase__						).prev_sample

											snake_case   :				Any				 =   pred_prev_sample

							snake_case   :				Union[str, Any]				 =   torch.sum(torch.abs(UpperCamelCase__						)						)
							snake_case   :				Union[str, Any]				 =   torch.mean(torch.abs(UpperCamelCase__						)						)

							assert abs(result_sum.item() - 192.7614						) < 1e-2
							assert abs(result_mean.item() - 0.2510						) < 1e-3







			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.scheduler_classes[0]
							snake_case   :				Tuple				 =   self.get_scheduler_config()
							snake_case   :				List[Any]				 =   scheduler_class(**UpperCamelCase__						)

							snake_case   :				Dict				 =   [106, 0]
							scheduler.set_timesteps(timesteps=UpperCamelCase__						)
							snake_case   :				Optional[Any]				 =   scheduler.timesteps

							snake_case   :				List[str]				 =   torch.manual_seed(0						)

							snake_case   :				Any				 =   self.dummy_model()
							snake_case   :				Union[str, Any]				 =   self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											snake_case   :				Dict				 =   scheduler.scale_model_input(UpperCamelCase__						,     UpperCamelCase__						)

											# 2. predict noise residual
											snake_case   :				Any				 =   model(UpperCamelCase__						,     UpperCamelCase__						)

											# 3. predict previous sample x_t-1
											snake_case   :				Dict				 =   scheduler.step(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     generator=UpperCamelCase__						).prev_sample

											snake_case   :				Optional[Any]				 =   pred_prev_sample

							snake_case   :				Union[str, Any]				 =   torch.sum(torch.abs(UpperCamelCase__						)						)
							snake_case   :				Dict				 =   torch.mean(torch.abs(UpperCamelCase__						)						)

							assert abs(result_sum.item() - 347.6357						) < 1e-2
							assert abs(result_mean.item() - 0.4527						) < 1e-3







			def 			lowerCamelCase		(     self						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				Any				 =   self.scheduler_classes[0]
							snake_case   :				str				 =   self.get_scheduler_config()
							snake_case   :				Tuple				 =   scheduler_class(**UpperCamelCase__						)

							snake_case   :				Dict				 =   [39, 30, 12, 15, 0]

							with self.assertRaises(UpperCamelCase__						,     msg="`timesteps` must be in descending order."						):
											scheduler.set_timesteps(timesteps=UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				Tuple				 =   self.scheduler_classes[0]
							snake_case   :				Union[str, Any]				 =   self.get_scheduler_config()
							snake_case   :				Optional[Any]				 =   scheduler_class(**UpperCamelCase__						)

							snake_case   :				Tuple				 =   [39, 30, 12, 1, 0]
							snake_case   :				str				 =   len(UpperCamelCase__						)

							with self.assertRaises(UpperCamelCase__						,     msg="Can only pass one of `num_inference_steps` or `timesteps`."						):
											scheduler.set_timesteps(num_inference_steps=UpperCamelCase__						,     timesteps=UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				Optional[int]				 =   self.scheduler_classes[0]
							snake_case   :				int				 =   self.get_scheduler_config()
							snake_case   :				Any				 =   scheduler_class(**UpperCamelCase__						)

							snake_case   :				List[str]				 =   [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    UpperCamelCase__						,     msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"						,     ):
											scheduler.set_timesteps(timesteps=UpperCamelCase__						)









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





