






"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Any   			=					(DDPMParallelScheduler,)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					A__							 =       {
					    'num_train_timesteps': 1000,
					    'beta_start': 0.0001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					    'variance_type': 'fixed_small',
					    'clip_sample': True,
					}

					config.update(**__a						)
					return config


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__a						)


	def       _UpperCAmelCase						(      self						):


					"""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=__a  ,					beta_end=__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						) - 0.0_0979						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.02						)						) < 1E-5


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						)










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


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


