






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(UnCLIPScheduler,)




				def 							lowercase__  (	self		:       str,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     {
								    '''num_train_timesteps''': 1_000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for timesteps in [1, 5, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for variance in ["fixed_small_log", "learned_range"]:
												self.check_over_configs(variance_type=lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=lowerCamelCase    )




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								for clip_sample_range in [1, 5, 10, 20]:
												self.check_over_configs(clip_sample_range=lowerCamelCase    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(prediction_type=lowerCamelCase    )




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								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=lowerCamelCase,    prev_timestep=lowerCamelCase    )




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(variance_type='''fixed_small_log'''    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0000E-10    )    ) < 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		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(variance_type='''learned_range'''    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     0.5

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config()
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     scheduler.timesteps

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

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

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

												lowercase__  					=     pred_prev_sample

								lowercase__  					=     torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config()
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								scheduler.set_timesteps(25    )

								lowercase__  					=     scheduler.timesteps

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

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

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

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

												lowercase__  					=     pred_prev_sample

								lowercase__  					=     torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

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




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								pass












from typing import Dict

from .base import GenericTensor, Pipeline
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       Union[str, Any]=None,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								if tokenize_kwargs is None:
												lowercase__  					=     {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''    )
												lowercase__  					=     truncation

								lowercase__  					=     tokenize_kwargs

								lowercase__  					=     {}
								if return_tensors is not None:
												lowercase__  					=     return_tensors

								return preprocess_params, {}, postprocess_params




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.framework
								lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )
								return model_inputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model(**lowerCamelCase    )
								return model_outputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Tuple=False    ):

								'''simple docstring'''
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(	self		:       List[str],    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								return super().__call__(*lowerCamelCase,    **lowerCamelCase    )




