
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Optional[Any]				       =(UnCLIPScheduler,)
	def      A_				(			self							:   Union[str, Any]       ,   **UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= {
						    'num_train_timesteps': 1000,
						    'variance_type': 'fixed_small_log',
						    'clip_sample': True,
						    'clip_sample_range': 1.0,
						    'prediction_type': 'epsilon',
						}

						config.update(**UpperCAmelCase_							)
						return config
	def      A_				(			self							:   Tuple							):
						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]							):
						for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=UpperCAmelCase_							)
	def      A_				(			self							:   int							):
						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=UpperCAmelCase_       ,   prev_timestep=UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config(variance_type='fixed_small_log'							)
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						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							) - 0.0_549_625							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.9_994_987							)							) < 1e-5
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config(variance_type='learned_range'							)
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= 0.5

						assert scheduler._get_variance(1       ,   predicted_variance=UpperCAmelCase_							) - -10.1_712_790 < 1e-5
						assert scheduler._get_variance(487       ,   predicted_variance=UpperCAmelCase_							) - -5.7_998_052 < 1e-5
						assert scheduler._get_variance(999       ,   predicted_variance=UpperCAmelCase_							) - -0.0_010_011 < 1e-5
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= scheduler.timesteps

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

						for i, t in enumerate(UpperCAmelCase_							):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   UpperCAmelCase_							)

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

											SCREAMING_SNAKE_CASE__													= pred_prev_sample

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

						assert abs(result_sum.item() - 252.2_682_495							) < 1e-2
						assert abs(result_mean.item() - 0.3_284_743							) < 1e-3
	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__													= self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__													= scheduler_class(**UpperCAmelCase_							)

						scheduler.set_timesteps(25							)

						SCREAMING_SNAKE_CASE__													= scheduler.timesteps

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

						for i, t in enumerate(UpperCAmelCase_							):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE__													= model(UpperCAmelCase_       ,   UpperCAmelCase_							)

											if i + 1 == timesteps.shape[0]:
																SCREAMING_SNAKE_CASE__													= None
											else:
																SCREAMING_SNAKE_CASE__													= timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE__													= scheduler.step(
											    UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_       ,   prev_timestep=UpperCAmelCase_       ,   generator=UpperCAmelCase_							).prev_sample

											SCREAMING_SNAKE_CASE__													= pred_prev_sample

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

						assert abs(result_sum.item() - 258.2_044_983							) < 1e-2
						assert abs(result_mean.item() - 0.3_362_038							) < 1e-3
	def      A_				(			self							:   Optional[int]							):
						pass





	def      A_				(			self							:   Optional[int]							):
						pass







import os
from math import logaa






def 							_lowercase							(     UpperCamelCase_ = "base_exp.txt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_		)	,	UpperCamelCase_		)		)		):
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= list(map(UpperCamelCase_	,	line.split(','		)		)		)
										if x * logaa(UpperCamelCase_		) > largest:
															SCREAMING_SNAKE_CASE__													= x * logaa(UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= i + 1
					return result


if __name__ == "__main__":
				print(solution())





