




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class       a__   ( snake_case__				):
						_a							:   Optional[int] =   (PNDMScheduler,)
						_a							:   Optional[int] =   (("""num_inference_steps""", 5_0),)






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{
											    "num_train_timesteps": 1_0_0_0,
											    "beta_start": 0.00_01,
											    "beta_end": 0.02,
											    "beta_schedule": "linear",
											}

											config.update(**_A      )
											return config






						def     __SCREAMING_SNAKE_CASE(       self		,      _A=0		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )
											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )
											__lowerCAmelCase           =	self.dummy_sample
											__lowerCAmelCase           =	0.1 * sample
											__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config(**_A      )
																__lowerCAmelCase           =	scheduler_class(**_A      )
																scheduler.set_timesteps(_A      )
																# copy over dummy past residuals
																__lowerCAmelCase           =	dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_A      )
																					__lowerCAmelCase           =	scheduler_class.from_pretrained(_A      )
																					new_scheduler.set_timesteps(_A      )
																					# copy over dummy past residuals
																					__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self		,      _A=0		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )
											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )
											__lowerCAmelCase           =	self.dummy_sample
											__lowerCAmelCase           =	0.1 * sample
											__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )
																scheduler.set_timesteps(_A      )

																# copy over dummy past residuals (must be after setting timesteps)
																__lowerCAmelCase           =	dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_A      )
																					__lowerCAmelCase           =	scheduler_class.from_pretrained(_A      )
																					# copy over dummy past residuals
																					new_scheduler.set_timesteps(_A      )

																					# copy over dummy past residual (must be after setting timesteps)
																					__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_prk(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	new_scheduler.step_plms(_A		,      _A		,      _A		,      **_A      ).prev_sample

																assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.scheduler_classes[0]
											__lowerCAmelCase           =	self.get_scheduler_config(**_A      )
											__lowerCAmelCase           =	scheduler_class(**_A      )

											__lowerCAmelCase           =	1_0
											__lowerCAmelCase           =	self.dummy_model()
											__lowerCAmelCase           =	self.dummy_sample_deter
											scheduler.set_timesteps(_A      )

											for i, t in enumerate(scheduler.prk_timesteps      ):
																__lowerCAmelCase           =	model(_A		,      _A      )
																__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A      ).prev_sample

											for i, t in enumerate(scheduler.plms_timesteps      ):
																__lowerCAmelCase           =	model(_A		,      _A      )
																__lowerCAmelCase           =	scheduler.step_plms(_A		,      _A		,      _A      ).prev_sample

											return sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	dict(self.forward_default_kwargs      )

											__lowerCAmelCase           =	kwargs.pop("num_inference_steps"		,      _A      )

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																__lowerCAmelCase           =	self.dummy_sample
																__lowerCAmelCase           =	0.1 * sample

																if num_inference_steps is not None and hasattr(_A		,      "set_timesteps"      ):
																					scheduler.set_timesteps(_A      )
																elif num_inference_steps is not None and not hasattr(_A		,      "set_timesteps"      ):
																					__lowerCAmelCase           =	num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																__lowerCAmelCase           =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																__lowerCAmelCase           =	dummy_past_residuals[:]

																__lowerCAmelCase           =	scheduler.step_prk(_A		,      0		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	scheduler.step_prk(_A		,      1		,      _A		,      **_A      ).prev_sample

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

																__lowerCAmelCase           =	scheduler.step_plms(_A		,      0		,      _A		,      **_A      ).prev_sample
																__lowerCAmelCase           =	scheduler.step_plms(_A		,      1		,      _A		,      **_A      ).prev_sample

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for timesteps in [1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=_A      )

											__lowerCAmelCase           =	self.scheduler_classes[0]
											__lowerCAmelCase           =	self.get_scheduler_config(steps_offset=1      )
											__lowerCAmelCase           =	scheduler_class(**_A      )
											scheduler.set_timesteps(1_0      )
											assert torch.equal(
											    scheduler.timesteps		,      torch.LongTensor(
											        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]      )		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for t in [1, 5, 1_0]:
																self.check_over_forward(time_step=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for t, num_inference_steps in zip([1, 5, 1_0]		,      [1_0, 5_0, 1_0_0]      ):
																self.check_over_forward(num_inference_steps=_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	2_7

											for scheduler_class in self.scheduler_classes:
																__lowerCAmelCase           =	self.dummy_sample
																__lowerCAmelCase           =	0.1 * sample

																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																scheduler.set_timesteps(_A      )

																# before power of 3 fix, would error on first step, so we only need to do two
																for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																					__lowerCAmelCase           =	scheduler.step_prk(_A		,      _A		,      _A      ).prev_sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaises(_A      ):
																__lowerCAmelCase           =	self.scheduler_classes[0]
																__lowerCAmelCase           =	self.get_scheduler_config()
																__lowerCAmelCase           =	scheduler_class(**_A      )

																scheduler.step_plms(self.dummy_sample		,      1		,      self.dummy_sample      ).prev_sample






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop()
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 1_98.13_18      ) < 1E-2
											assert abs(result_mean.item() - 0.25_80      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(prediction_type="v_prediction"      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 67.39_86      ) < 1E-2
											assert abs(result_mean.item() - 0.08_78      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(set_alpha_to_one=_A		,      beta_start=0.01      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 2_30.03_99      ) < 1E-2
											assert abs(result_mean.item() - 0.29_95      ) < 1E-3






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.full_loop(set_alpha_to_one=_A		,      beta_start=0.01      )
											__lowerCAmelCase           =	torch.sum(torch.abs(_A      )      )
											__lowerCAmelCase           =	torch.mean(torch.abs(_A      )      )

											assert abs(result_sum.item() - 1_86.94_82      ) < 1E-2
											assert abs(result_mean.item() - 0.24_34      ) < 1E-3










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					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
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	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]					):
															__lowerCAmelCase           =	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)




