


'''simple docstring'''





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							str							= (KDPMaAncestralDiscreteScheduler,)
			A	:							Union[str, Any]							= 10
			def 				lowerCAmelCase    (     self       : List[Any] ,						**lowerCAmelCase_       : List[str]  )			->		List[Any]:
						__A=  {
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**lowerCAmelCase_  )
						return config
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
						for timesteps in [10, 50, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		str:
						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,						[0.00_02, 0.0_02, 0.02]  ):
									self.check_over_configs(beta_start=lowerCAmelCase_ ,						beta_end=lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Any:
						for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Dict:
						if torch_device == "mps":
									return
						__A=  self.scheduler_classes[0]
						__A=  self.get_scheduler_config()
						__A=  scheduler_class(**lowerCAmelCase_  )

						scheduler.set_timesteps(self.num_inference_steps  )

						__A=  torch.manual_seed(0  )

						__A=  self.dummy_model()
						__A=  self.dummy_sample_deter * scheduler.init_noise_sigma
						__A=  sample.to(lowerCAmelCase_  )

						for i, t in enumerate(scheduler.timesteps  ):
									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
									__A=  output.prev_sample

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

						assert abs(result_sum.item() - 1_38_49.38_77  ) < 1E-2
						assert abs(result_mean.item() - 18.03_31  ) < 5E-3
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Any  )			->		Union[str, Any]:
						if torch_device == "mps":
									return
						__A=  self.scheduler_classes[0]
						__A=  self.get_scheduler_config(prediction_type='v_prediction'  )
						__A=  scheduler_class(**lowerCAmelCase_  )

						scheduler.set_timesteps(self.num_inference_steps  )

						__A=  self.dummy_model()
						__A=  self.dummy_sample_deter * scheduler.init_noise_sigma
						__A=  sample.to(lowerCAmelCase_  )

						__A=  torch.manual_seed(0  )

						for i, t in enumerate(scheduler.timesteps  ):
									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
									__A=  output.prev_sample

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

						assert abs(result_sum.item() - 3_28.99_70  ) < 1E-2
						assert abs(result_mean.item() - 0.42_84  ) < 1E-3

			def 				lowerCAmelCase    (     self       : List[Any]  )			->		Dict:
						if torch_device == "mps":
									return
						__A=  self.scheduler_classes[0]
						__A=  self.get_scheduler_config()
						__A=  scheduler_class(**lowerCAmelCase_  )

						scheduler.set_timesteps(self.num_inference_steps ,						device=lowerCAmelCase_  )
						__A=  torch.manual_seed(0  )

						__A=  self.dummy_model()
						__A=  self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
									__A=  scheduler.scale_model_input(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  model(lowerCAmelCase_ ,						lowerCAmelCase_  )

									__A=  scheduler.step(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_  )
									__A=  output.prev_sample

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

						assert abs(result_sum.item() - 1_38_49.38_18  ) < 1E-1
						assert abs(result_mean.item() - 18.03_31  ) < 1E-3







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



