


'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						A__							(		lowerCAmelCase__  ):
						UpperCAmelCase             =						(DDPMScheduler,)




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    **_a					:      List[str]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''variance_type''': '''fixed_small''',
										    '''clip_sample''': True,
										}

										config.update(**_lowerCamelCase  )
										return config




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     int:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[int]:


										"""simple docstring"""





										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,    [0.0_02, 0.02, 0.2, 2]  ):
														self.check_over_configs(beta_start=_lowerCamelCase				,    beta_end=_lowerCamelCase  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Any:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Any  )							->     Any:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Any:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Union[str, Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Any  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

										assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_09_79  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

										_SCREAMING_SNAKE_CASE			  =len(_lowerCamelCase  )

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

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

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

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_lowerCamelCase  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_lowerCamelCase  )  )

										assert abs(result_sum.item() - 258.9606  ) < 1E-2
										assert abs(result_mean.item() - 0.33_72  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(prediction_type='''v_prediction'''  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

										_SCREAMING_SNAKE_CASE			  =len(_lowerCamelCase  )

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

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

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

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_lowerCamelCase  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_lowerCamelCase  )  )

										assert abs(result_sum.item() - 202.0296  ) < 1E-2
										assert abs(result_mean.item() - 0.26_31  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

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

										scheduler.set_timesteps(timesteps=_lowerCamelCase  )

										_SCREAMING_SNAKE_CASE			  =scheduler.timesteps

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

														_SCREAMING_SNAKE_CASE			  =scheduler.previous_timestep(_lowerCamelCase  )
														_SCREAMING_SNAKE_CASE			  =prev_t.item()

														self.assertEqual(_lowerCamelCase				,    _lowerCamelCase  )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

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

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 1, 0]
										_SCREAMING_SNAKE_CASE			  =len(_lowerCamelCase  )

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




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_lowerCamelCase  )

										_SCREAMING_SNAKE_CASE			  =[scheduler.config.num_train_timesteps]

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



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 						lowerCamelCase(   a__	,a__=()	,a__=None	,a__="no"	,a__="29500"):
				_SCREAMING_SNAKE_CASE			  =False
				_SCREAMING_SNAKE_CASE			  =False
				if any(key.startswith('''KAGGLE''') for key in os.environ.keys()):
								_SCREAMING_SNAKE_CASE			  =True
				elif "IPython" in sys.modules:
								_SCREAMING_SNAKE_CASE			  ='''google.colab''' in str(sys.modules['''IPython'''].get_ipython())

				try:
								_SCREAMING_SNAKE_CASE			  =PrecisionType(mixed_precision.lower())
				except ValueError:
								raise ValueError(
								    f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.")

				if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,a__) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state) > 0:
												raise ValueError(
												    '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
												    '''your training function. Restart your notebook and make sure no cells initializes an '''
												    '''`Accelerator`.''')
								if num_processes is None:
												_SCREAMING_SNAKE_CASE			  =8

								_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''TPU''')
								print(f"Launching a training on {num_processes} TPU cores.")
								xmp.spawn(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print('''Launching training on one GPU.''')
								else:
												print('''Launching training on one CPU.''')
								function(*a__)
				else:
								if num_processes is None:
												raise ValueError(
												    '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''')

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state) > 0:
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
																    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
																    '''`Accelerator`.''')

												if torch.cuda.is_initialized():
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
																    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
																    '''function.''')

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=a__	,master_addr='''127.0.01'''	,master_port=a__	,mixed_precision=a__):
																_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''MULTI_GPU''')
																print(f"Launching training on {num_processes} GPUs.")
																try:
																				start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
																								    '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
																								    '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
																								    '''which one is problematic.''') from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																_SCREAMING_SNAKE_CASE			  ='''1'''
																print('''Launching training on MPS.''')
												elif torch.cuda.is_available():
																print('''Launching training on one GPU.''')
												else:
																print('''Launching training on CPU.''')
												function(*a__)







def 						lowerCamelCase(   a__	,a__=()	,a__=2):
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=a__	,master_addr='''127.0.01'''	,master_port='''29500'''	,accelerate_mixed_precision='''no'''	,accelerate_debug_rdv_file=tmp_file.name	,accelerate_use_cpu='''yes'''	,):
												_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,debug=a__)
												start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')