



'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					(UnCLIPScheduler,)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							{
							    """num_train_timesteps""": 1000,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**_lowerCAmelCase						)
							return config







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							for clip_sample_range in [1, 5, 10, 20]:
													self.check_over_configs(clip_sample_range=_lowerCAmelCase						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Dict:
							'''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   _a   (    self						)    ->    Optional[Any]:
							'''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						) - 1.00_00E-10						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.054_9625						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.999_4987						)						) < 1E-5







	def   _a   (    self						)    ->    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						) - -10.171_2790 < 1E-5
							assert scheduler._get_variance(487 ,				predicted_variance=_lowerCAmelCase						) - -5.799_8052 < 1E-5
							assert scheduler._get_variance(999 ,				predicted_variance=_lowerCAmelCase						) - -0.001_0011 < 1E-5







	def   _a   (    self						)    ->    Dict:
							'''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() - 252.268_2495						) < 1E-2
							assert abs(result_mean.item() - 0.328_4743						) < 1E-3







	def   _a   (    self						)    ->    Union[str, Any]:
							'''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() - 258.204_4983						) < 1E-2
							assert abs(result_mean.item() - 0.336_2038						) < 1E-3







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							pass







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


