"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class lowerCamelCase		(_UpperCamelCase      ):






					'''simple docstring'''


					a   	=		(DDIMParallelScheduler,)
					a   	=		(("eta", 0.0), ("num_inference_steps", 5_0))



					def 			lowerCAmelCase_					(						self			:				str   , **_snake_case			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				{
									    "num_train_timesteps": 1000,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									    "clip_sample": True,
									}

									config.update(**_snake_case		)
									return config



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , **_snake_case			:				Tuple		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__		  =				self.get_scheduler_config(**_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				scheduler_class(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				10, 0.0

									SCREAMING_SNAKE_CASE__		  =				self.dummy_model()
									SCREAMING_SNAKE_CASE__		  =				self.dummy_sample_deter

									scheduler.set_timesteps(_snake_case		)

									for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case		)
													SCREAMING_SNAKE_CASE__		  =				scheduler.step(_snake_case   , _snake_case   , _snake_case   , _snake_case		).prev_sample

									return sample



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Union[str, Any]:
									for timesteps in [100, 500, 1000]:
													self.check_over_configs(num_train_timesteps=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Union[str, Any]:
									for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__		  =				self.get_scheduler_config(steps_offset=1		)
									SCREAMING_SNAKE_CASE__		  =				scheduler_class(**_snake_case		)
									scheduler.set_timesteps(5		)
									assert torch.equal(scheduler.timesteps   , torch.LongTensor([801, 601, 401, 201, 1]		)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									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=_snake_case   , beta_end=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Union[str, Any]:
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       List[Any]:
									self.check_over_configs(thresholding=_snake_case		)
									for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_snake_case   , prediction_type=_snake_case   , sample_max_value=_snake_case   , )



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									for t in [1, 10, 49]:
													self.check_over_forward(time_step=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       int:
									for t, num_inference_steps in zip([1, 10, 50]   , [10, 50, 500]		):
													self.check_over_forward(time_step=_snake_case   , num_inference_steps=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Any:
									for t, eta in zip([1, 10, 49]   , [0.0, 0.5, 1.0]		):
													self.check_over_forward(time_step=_snake_case   , eta=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__		  =				self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__		  =				scheduler_class(**_snake_case		)

									assert torch.sum(torch.abs(scheduler._get_variance(0   , 0		) - 0.0		)		) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(420   , 400		) - 0.1_4771		)		) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(980   , 960		) - 0.3_2460		)		) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0   , 0		) - 0.0		)		) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487   , 486		) - 0.0_0979		)		) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999   , 998		) - 0.02		)		) < 1e-5



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__		  =				self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__		  =				scheduler_class(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				10, 0.0
									scheduler.set_timesteps(_snake_case		)

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

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

									SCREAMING_SNAKE_CASE__		  =				model(samples.flatten(0   , 1		)   , timesteps.flatten(0   , 1		)		)
									SCREAMING_SNAKE_CASE__		  =				scheduler.batch_step_no_noise(_snake_case   , timesteps.flatten(0   , 1		)   , samples.flatten(0   , 1		)   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.sum(torch.abs(_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 1147.7904		) < 1e-2
									assert abs(result_mean.item() - 0.4982		) < 1e-3



					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.full_loop()

									SCREAMING_SNAKE_CASE__		  =				torch.sum(torch.abs(_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 172.0067		) < 1e-2
									assert abs(result_mean.item() - 0.22_3967		) < 1e-3



					def 			lowerCAmelCase_					(						self			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.full_loop(prediction_type="v_prediction"		)

									SCREAMING_SNAKE_CASE__		  =				torch.sum(torch.abs(_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 52.5302		) < 1e-2
									assert abs(result_mean.item() - 0.0684		) < 1e-3



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									# We specify different beta, so that the first alpha is 0.99
									SCREAMING_SNAKE_CASE__		  =				self.full_loop(set_alpha_to_one=_snake_case   , beta_start=0.01		)
									SCREAMING_SNAKE_CASE__		  =				torch.sum(torch.abs(_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 149.8295		) < 1e-2
									assert abs(result_mean.item() - 0.1951		) < 1e-3



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Dict:
									# We specify different beta, so that the first alpha is 0.99
									SCREAMING_SNAKE_CASE__		  =				self.full_loop(set_alpha_to_one=_snake_case   , beta_start=0.01		)
									SCREAMING_SNAKE_CASE__		  =				torch.sum(torch.abs(_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_sum.item() - 149.0784		) < 1e-2
									assert abs(result_mean.item() - 0.1941		) < 1e-3





"""simple docstring"""






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_A   					=    HUGGINGFACE_HUB_CACHE


_A   					=    'config.json'
_A   					=    'diffusion_pytorch_model.bin'
_A   					=    'diffusion_flax_model.msgpack'
_A   					=    'model.onnx'
_A   					=    'diffusion_pytorch_model.safetensors'
_A   					=    'weights.pb'
_A   					=    'https://huggingface.co'
_A   					=    default_cache_path
_A   					=    'diffusers_modules'
_A   					=    os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A   					=    ['fp16', 'non-ema']
_A   					=    '.self_attn'




