

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							List[str]									= (HeunDiscreteScheduler,)
						_A						:							Optional[int]									= 10





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   Any:
									a_	:	int          =   {
									    """num_train_timesteps""": 1_100,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**_lowercase						)
									return config





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     ,			[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
												self.check_over_configs(beta_start=_lowercase     ,			beta_end=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Union[str, Any]:
									for schedule in ["linear", "scaled_linear", "exp"]:
												self.check_over_configs(beta_schedule=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Dict:
									for clip_sample_range in [1.0, 2.0, 3.0]:
												self.check_over_configs(clip_sample_range=_lowercase     ,			clip_sample=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									for prediction_type in ["epsilon", "v_prediction", "sample"]:
												self.check_over_configs(prediction_type=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Any:
									a_	:	List[str]          =   self.scheduler_classes[0]
									a_	:	List[str]          =   self.get_scheduler_config()
									a_	:	List[str]          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps						)

									a_	:	List[str]          =   self.dummy_model()
									a_	:	Tuple          =   self.dummy_sample_deter * scheduler.init_noise_sigma
									a_	:	Union[str, Any]          =   sample.to(_lowercase						)

									for i, t in enumerate(scheduler.timesteps						):
												a_	:	str          =   scheduler.scale_model_input(_lowercase     ,			_lowercase						)

												a_	:	Tuple          =   model(_lowercase     ,			_lowercase						)

												a_	:	Any          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase						)
												a_	:	str          =   output.prev_sample

									a_	:	Union[str, Any]          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	int          =   torch.mean(torch.abs(_lowercase						)						)

									if torch_device in ["cpu", "mps"]:
												assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
									else:
												# CUDA
												assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3





						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									a_	:	Union[str, Any]          =   self.scheduler_classes[0]
									a_	:	Any          =   self.get_scheduler_config(prediction_type="""v_prediction"""						)
									a_	:	Any          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps						)

									a_	:	Any          =   self.dummy_model()
									a_	:	Dict          =   self.dummy_sample_deter * scheduler.init_noise_sigma
									a_	:	List[str]          =   sample.to(_lowercase						)

									for i, t in enumerate(scheduler.timesteps						):
												a_	:	List[Any]          =   scheduler.scale_model_input(_lowercase     ,			_lowercase						)

												a_	:	Any          =   model(_lowercase     ,			_lowercase						)

												a_	:	Optional[Any]          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase						)
												a_	:	Any          =   output.prev_sample

									a_	:	Union[str, Any]          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	List[Any]          =   torch.mean(torch.abs(_lowercase						)						)

									if torch_device in ["cpu", "mps"]:
												assert abs(result_sum.item() - 4.6934e-07						) < 1e-2
												assert abs(result_mean.item() - 6.1112e-10						) < 1e-3
									else:
												# CUDA
												assert abs(result_sum.item() - 4.693428650170972e-07						) < 1e-2
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3





						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									a_	:	Optional[Any]          =   self.scheduler_classes[0]
									a_	:	Dict          =   self.get_scheduler_config()
									a_	:	Tuple          =   scheduler_class(**_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps     ,			device=_lowercase						)

									a_	:	Optional[int]          =   self.dummy_model()
									a_	:	List[str]          =   self.dummy_sample_deter.to(_lowercase						) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												a_	:	Union[str, Any]          =   scheduler.scale_model_input(_lowercase     ,			_lowercase						)

												a_	:	Optional[Any]          =   model(_lowercase     ,			_lowercase						)

												a_	:	Dict          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase						)
												a_	:	Union[str, Any]          =   output.prev_sample

									a_	:	List[str]          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	Any          =   torch.mean(torch.abs(_lowercase						)						)

									if str(_lowercase						).startswith("""cpu"""						):
												# The following sum varies between 148 and 156 on mps. Why?
												assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
									elif str(_lowercase						).startswith("""mps"""						):
												# Larger tolerance on mps
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
									else:
												# CUDA
												assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
												assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									a_	:	List[Any]          =   self.scheduler_classes[0]
									a_	:	Optional[Any]          =   self.get_scheduler_config()
									a_	:	Any          =   scheduler_class(**_lowercase     ,			use_karras_sigmas=_lowercase						)

									scheduler.set_timesteps(self.num_inference_steps     ,			device=_lowercase						)

									a_	:	Tuple          =   self.dummy_model()
									a_	:	List[str]          =   self.dummy_sample_deter.to(_lowercase						) * scheduler.init_noise_sigma
									a_	:	Any          =   sample.to(_lowercase						)

									for t in scheduler.timesteps:
												a_	:	List[Any]          =   scheduler.scale_model_input(_lowercase     ,			_lowercase						)

												a_	:	int          =   model(_lowercase     ,			_lowercase						)

												a_	:	int          =   scheduler.step(_lowercase     ,			_lowercase     ,			_lowercase						)
												a_	:	List[Any]          =   output.prev_sample

									a_	:	List[Any]          =   torch.sum(torch.abs(_lowercase						)						)
									a_	:	List[Any]          =   torch.mean(torch.abs(_lowercase						)						)

									assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
									assert abs(result_mean.item() - 1.9869554535034695e-07						) < 1e-2





import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			return EnvironmentCommand()



class 					A__(a_       ):
						"""simple docstring"""






						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Optional[Any]:
									a_	:	List[str]          =   parser.add_parser("""env"""     ,			help="""Print relevant system environment info."""						)
									download_parser.set_defaults(func=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	List[str]          =   {
									    """`datasets` version""": version,
									    """Platform""": platform.platform(),
									    """Python version""": platform.python_version(),
									    """Huggingface_hub version""": huggingface_hub.__version__,
									    """PyArrow version""": pyarrow.__version__,
									    """Pandas version""": pandas.__version__,
									}

									print("""\nCopy-and-paste the text below in your GitHub issue.\n"""						)
									print(self.format_dict(_lowercase						)						)

									return info





						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Dict:
									return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"


