

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =(DDIMScheduler,)
			lowerCamelCase__          =(('eta', 0.0), ('num_inference_steps', 50))
			def 							__UpperCamelCase	(     self							:	int , **a							:	Dict		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    {
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "clip_sample": True,
				}

				config.update(**a		)
				return config
			def 							__UpperCamelCase	(     self							:	int , **a							:	Any		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.get_scheduler_config(**a		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    scheduler_class(**a		)

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[Any]			       =    10, 0.0

				SCREAMING_SNAKE_CASE				: str			       =    self.dummy_model()
				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.dummy_sample_deter

				scheduler.set_timesteps(a		)

				for t in scheduler.timesteps:
					SCREAMING_SNAKE_CASE				: List[str]			       =    model(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    scheduler.step(a , a , a , a		).prev_sample

				return sample
			def 							__UpperCamelCase	(     self							:	int		)							->    Optional[int]:

				"""simple docstring"""

				for timesteps in [100, 500, 1000]:
					self.check_over_configs(num_train_timesteps=a		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Optional[Any]:

				"""simple docstring"""

				for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=a		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.get_scheduler_config(steps_offset=1		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    scheduler_class(**a		)
				scheduler.set_timesteps(5		)
				assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1]		)		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    int:

				"""simple docstring"""

				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=a , beta_end=a		)
			def 							__UpperCamelCase	(     self							:	str		)							->    List[Any]:

				"""simple docstring"""

				for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    int:

				"""simple docstring"""

				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=a		)
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=a		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Any:

				"""simple docstring"""

				for timestep_spacing in ["trailing", "leading"]:
					self.check_over_configs(timestep_spacing=a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    List[str]:

				"""simple docstring"""

				for rescale_betas_zero_snr in [True, False]:
					self.check_over_configs(rescale_betas_zero_snr=a		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				for t in [1, 10, 49]:
					self.check_over_forward(time_step=a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Any:

				"""simple docstring"""

				for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500]		):
					self.check_over_forward(time_step=a , num_inference_steps=a		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    Any:

				"""simple docstring"""

				for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0]		):
					self.check_over_forward(time_step=a , eta=a		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE				: int			       =    self.get_scheduler_config()
				SCREAMING_SNAKE_CASE				: int			       =    scheduler_class(**a		)

				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 							__UpperCamelCase	(     self							:	List[str]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    self.full_loop()

				SCREAMING_SNAKE_CASE				: Dict			       =    torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.mean(torch.abs(a		)		)

				assert abs(result_sum.item() - 172.0067		) < 1e-2
				assert abs(result_mean.item() - 0.22_3967		) < 1e-3
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.full_loop(prediction_type="v_prediction"		)

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE				: Dict			       =    torch.mean(torch.abs(a		)		)

				assert abs(result_sum.item() - 52.5302		) < 1e-2
				assert abs(result_mean.item() - 0.0684		) < 1e-3
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    self.full_loop(set_alpha_to_one=a , beta_start=0.01		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.mean(torch.abs(a		)		)

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






			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.full_loop(set_alpha_to_one=a , beta_start=0.01		)
				SCREAMING_SNAKE_CASE				: int			       =    torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.mean(torch.abs(a		)		)

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


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


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets