



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Optional[int]				=	(DDPMParallelScheduler,)



	def 			__A							(       self			,	**UpperCAmelCase__		):
							A__							=       {
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.0_001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "variance_type": "fixed_small",
							    "clip_sample": True,
							}

							config.update(**_a		)
							return config



	def 			__A							(       self		):
							for timesteps in [1, 5, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=_a		)



	def 			__A							(       self		):
							for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,	[0.002, 0.02, 0.2, 2]		):
													self.check_over_configs(beta_start=_a			,	beta_end=_a		)



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



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



	def 			__A							(       self		):
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_a		)



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



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



	def 			__A							(       self		):
							for t in [0, 500, 999]:
													self.check_over_forward(time_step=_a		)



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00_979		)		) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       len(_a		)

							A__							=       self.dummy_model()
							A__							=       self.dummy_sample_deter
							A__							=       self.dummy_sample_deter + 0.1
							A__							=       self.dummy_sample_deter - 0.1

							A__							=       samplea.shape[0]
							A__							=       torch.stack([samplea, samplea, samplea]			,	dim=0		)
							A__							=       torch.arange(_a		)[0:3, None].repeat(1			,	_a		)

							A__							=       model(samples.flatten(0			,	1		)			,	timesteps.flatten(0			,	1		)		)
							A__							=       scheduler.batch_step_no_noise(_a			,	timesteps.flatten(0			,	1		)			,	samples.flatten(0			,	1		)		)

							A__							=       torch.sum(torch.abs(_a		)		)
							A__							=       torch.mean(torch.abs(_a		)		)

							assert abs(result_sum.item() - 1_153.1_833		) < 1e-2
							assert abs(result_mean.item() - 0.5_005		) < 1e-3



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       len(_a		)

							A__							=       self.dummy_model()
							A__							=       self.dummy_sample_deter
							A__							=       torch.manual_seed(0		)

							for t in reversed(range(_a		)		):
													# 1. predict noise residual
													A__							=       model(_a			,	_a		)

													# 2. predict previous mean of sample x_t-1
													A__							=       scheduler.step(_a			,	_a			,	_a			,	generator=_a		).prev_sample

													A__							=       pred_prev_sample

							A__							=       torch.sum(torch.abs(_a		)		)
							A__							=       torch.mean(torch.abs(_a		)		)

							assert abs(result_sum.item() - 258.9_606		) < 1e-2
							assert abs(result_mean.item() - 0.3_372		) < 1e-3



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config(prediction_type="v_prediction"		)
							A__							=       scheduler_class(**_a		)

							A__							=       len(_a		)

							A__							=       self.dummy_model()
							A__							=       self.dummy_sample_deter
							A__							=       torch.manual_seed(0		)

							for t in reversed(range(_a		)		):
													# 1. predict noise residual
													A__							=       model(_a			,	_a		)

													# 2. predict previous mean of sample x_t-1
													A__							=       scheduler.step(_a			,	_a			,	_a			,	generator=_a		).prev_sample

													A__							=       pred_prev_sample

							A__							=       torch.sum(torch.abs(_a		)		)
							A__							=       torch.mean(torch.abs(_a		)		)

							assert abs(result_sum.item() - 202.0_296		) < 1e-2
							assert abs(result_mean.item() - 0.2_631		) < 1e-3



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=_a		)

							A__							=       scheduler.timesteps

							for i, timestep in enumerate(_a		):
													if i == len(_a		) - 1:
																			A__							=       -1
													else:
																			A__							=       timesteps[i + 1]

													A__							=       scheduler.previous_timestep(_a		)
													A__							=       prev_t.item()

													self.assertEqual(_a			,	_a		)



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       [100, 87, 50, 51, 0]

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



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       [100, 87, 50, 1, 0]
							A__							=       len(_a		)

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



	def 			__A							(       self		):
							A__							=       self.scheduler_classes[0]
							A__							=       self.get_scheduler_config()
							A__							=       scheduler_class(**_a		)

							A__							=       [scheduler.config.num_train_timesteps]

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










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





