
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=(UnCLIPScheduler,)




				def      __a						(					self				,				**_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	{
							    "num_train_timesteps": 1_000,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**_UpperCamelCase     )
							return config




				def      __a						(					self     )				->      List[Any]:
							for timesteps in [1, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							for variance in ["fixed_small_log", "learned_range"]:
										self.check_over_configs(variance_type=_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							for clip_sample_range in [1, 5, 10, 20]:
										self.check_over_configs(clip_sample_range=_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(prediction_type=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							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     )				->      Optional[int]:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config(variance_type="fixed_small_log"     )
							lowerCAmelCase_						=	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     ) - 0.0549625     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.9994987     )     ) < 1e-5




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config(variance_type="learned_range"     )
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							lowerCAmelCase_						=	0.5

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




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config()
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							lowerCAmelCase_						=	scheduler.timesteps

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

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

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

										lowerCAmelCase_						=	pred_prev_sample

							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

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




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config()
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							scheduler.set_timesteps(25     )

							lowerCAmelCase_						=	scheduler.timesteps

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

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

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

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

										lowerCAmelCase_						=	pred_prev_sample

							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

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




				def      __a						(					self     )				->      int:
							pass




				def      __a						(					self     )				->      int:
							pass





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''megatron-bert'''




				def __init__(					self				,				_UpperCamelCase=29_056				,				_UpperCamelCase=1_024				,				_UpperCamelCase=24				,				_UpperCamelCase=16				,				_UpperCamelCase=4_096				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-1_2				,				_UpperCamelCase=0				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      int:
							super().__init__(pad_token_id=_UpperCamelCase				,				**_UpperCamelCase     )

							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	position_embedding_type
							lowerCAmelCase_						=	use_cache



