





"""simple docstring"""





import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]=None   ):


							'''simple docstring'''







							if subparsers is not None:
														__lowerCamelCase      :      int         =subparsers.add_parser('''env'''   )
							else:
														__lowerCamelCase      :      Union[str, Any]         =argparse.ArgumentParser('''Accelerate env command'''   )

							parser.add_argument(
							    '''--config_file'''    ,						default=SCREAMING_SNAKE_CASE    ,						help='''The config file to use for the default values in the launching script.'''   )

							if subparsers is not None:
														parser.set_defaults(func=SCREAMING_SNAKE_CASE   )
							return parser



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =torch.__version__
							__lowerCamelCase      :      Union[str, Any]         =torch.cuda.is_available()
							__lowerCamelCase      :      Optional[int]         =is_xpu_available()
							__lowerCamelCase      :      int         =is_npu_available()

							__lowerCamelCase      :      Any         ='''Not found'''
							# Get the default from the config file.
							if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Dict         =load_config_from_file(args.config_file   ).to_dict()

							__lowerCamelCase      :      str         ={
							    '''`Accelerate` version''': version,
							    '''Platform''': platform.platform(),
							    '''Python version''': platform.python_version(),
							    '''Numpy version''': np.__version__,
							    '''PyTorch version (GPU?)''': F'{pt_version} ({pt_cuda_available})',
							    '''PyTorch XPU available''': str(SCREAMING_SNAKE_CASE   ),
							    '''PyTorch NPU available''': str(SCREAMING_SNAKE_CASE   ),
							    '''System RAM''': F'{psutil.virtual_memory().total / 1024 ** 3:.2f} GB',
							}
							if pt_cuda_available:
														__lowerCamelCase      :      Optional[int]         =torch.cuda.get_device_name()

							print('''\nCopy-and-paste the text below in your GitHub issue\n'''   )
							print('''\n'''.join([F'- {prop}: {val}' for prop, val in info.items()]   )   )

							print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:'''   )
							__lowerCamelCase      :      Any         =(
							    '''\n'''.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]   )
							    if isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    else F'\t{accelerate_config}'
							)
							print(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =accelerate_config

							return info



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =env_command_parser()
							__lowerCamelCase      :      Optional[int]         =parser.parse_args()
							env_command(SCREAMING_SNAKE_CASE   )
							return 0


if __name__ == "__main__":
							raise SystemExit(main())











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




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




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




				def 					__lowercase  (     self  :Any			):
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

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

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




