




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (DDPMParallelScheduler,)

						def      A__    (		self :Any     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """variance_type""": """fixed_small""",
										    """clip_sample""": True,
										}

										config.update(**__snake_case			)
										return config

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for timesteps in [1, 5, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,			[0.002, 0.02, 0.2, 2]			):
														self.check_over_configs(beta_start=__snake_case     ,			beta_end=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=__snake_case			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										self.check_over_configs(thresholding=__snake_case			)
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=__snake_case     ,			prediction_type=__snake_case     ,			sample_max_value=__snake_case     ,			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

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

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for t in [0, 5_00, 9_99]:
														self.check_over_forward(time_step=__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.scheduler_classes[0]
										__magic_name__						:						Any										=self.get_scheduler_config()
										__magic_name__						:						str										=scheduler_class(**__snake_case			)

										assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.00979			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.02			)			) < 1E-5

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						str										=self.scheduler_classes[0]
										__magic_name__						:						Optional[int]										=self.get_scheduler_config()
										__magic_name__						:						int										=scheduler_class(**__snake_case			)

										__magic_name__						:						Union[str, Any]										=len(__snake_case			)

										__magic_name__						:						str										=self.dummy_model()
										__magic_name__						:						Optional[Any]										=self.dummy_sample_deter
										__magic_name__						:						Any										=self.dummy_sample_deter + 0.1
										__magic_name__						:						Union[str, Any]										=self.dummy_sample_deter - 0.1

										__magic_name__						:						str										=samplea.shape[0]
										__magic_name__						:						Any										=torch.stack([samplea, samplea, samplea]     ,			dim=0			)
										__magic_name__						:						Optional[int]										=torch.arange(__snake_case			)[0:3, None].repeat(1     ,			__snake_case			)

										__magic_name__						:						int										=model(samples.flatten(0     ,			1			)     ,			timesteps.flatten(0     ,			1			)			)
										__magic_name__						:						Optional[int]										=scheduler.batch_step_no_noise(__snake_case     ,			timesteps.flatten(0     ,			1			)     ,			samples.flatten(0     ,			1			)			)

										__magic_name__						:						Tuple										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Union[str, Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 1153.1833			) < 1E-2
										assert abs(result_mean.item() - 0.5005			) < 1E-3

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.scheduler_classes[0]
										__magic_name__						:						List[Any]										=self.get_scheduler_config()
										__magic_name__						:						Union[str, Any]										=scheduler_class(**__snake_case			)

										__magic_name__						:						List[Any]										=len(__snake_case			)

										__magic_name__						:						int										=self.dummy_model()
										__magic_name__						:						Tuple										=self.dummy_sample_deter
										__magic_name__						:						Optional[Any]										=torch.manual_seed(0			)

										for t in reversed(range(__snake_case			)			):
														# 1. predict noise residual
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)

														# 2. predict previous mean of sample x_t-1
														__magic_name__						:						Dict										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			generator=__snake_case			).prev_sample

														__magic_name__						:						Optional[Any]										=pred_prev_sample

										__magic_name__						:						str										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 258.9606			) < 1E-2
										assert abs(result_mean.item() - 0.3372			) < 1E-3

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.scheduler_classes[0]
										__magic_name__						:						Any										=self.get_scheduler_config(prediction_type="""v_prediction"""			)
										__magic_name__						:						int										=scheduler_class(**__snake_case			)

										__magic_name__						:						Optional[Any]										=len(__snake_case			)

										__magic_name__						:						str										=self.dummy_model()
										__magic_name__						:						str										=self.dummy_sample_deter
										__magic_name__						:						List[str]										=torch.manual_seed(0			)

										for t in reversed(range(__snake_case			)			):
														# 1. predict noise residual
														__magic_name__						:						Optional[int]										=model(__snake_case     ,			__snake_case			)

														# 2. predict previous mean of sample x_t-1
														__magic_name__						:						str										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case     ,			generator=__snake_case			).prev_sample

														__magic_name__						:						List[Any]										=pred_prev_sample

										__magic_name__						:						Optional[int]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 202.0296			) < 1E-2
										assert abs(result_mean.item() - 0.2631			) < 1E-3

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.scheduler_classes[0]
										__magic_name__						:						str										=self.get_scheduler_config()
										__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

										__magic_name__						:						Dict										=[1_00, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=__snake_case			)

										__magic_name__						:						int										=scheduler.timesteps

										for i, timestep in enumerate(__snake_case			):
														if i == len(__snake_case			) - 1:
																		__magic_name__						:						Any										=-1
														else:
																		__magic_name__						:						List[str]										=timesteps[i + 1]

														__magic_name__						:						Tuple										=scheduler.previous_timestep(__snake_case			)
														__magic_name__						:						Any										=prev_t.item()

														self.assertEqual(__snake_case     ,			__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.scheduler_classes[0]
										__magic_name__						:						List[Any]										=self.get_scheduler_config()
										__magic_name__						:						Union[str, Any]										=scheduler_class(**__snake_case			)

										__magic_name__						:						Tuple										=[1_00, 87, 50, 51, 0]

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

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config()
										__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

										__magic_name__						:						int										=[1_00, 87, 50, 1, 0]
										__magic_name__						:						Optional[Any]										=len(__snake_case			)

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


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config()
										__magic_name__						:						Union[str, Any]										=scheduler_class(**__snake_case			)

										__magic_name__						:						Union[str, Any]										=[scheduler.config.num_train_timesteps]

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










from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				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
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=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] ):
												__magic_name__						:						str										=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)




