



"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	(EulerAncestralDiscreteScheduler,)
					_A						:     Union[str, Any]		=	1_0






					def   lowerCAmelCase_							(			self:				Dict		,	**snake_case:				Union[str, Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     {
											    """num_train_timesteps""": 1_100,
											    """beta_start""": 0.0_0_0_1,
											    """beta_end""": 0.0_2,
											    """beta_schedule""": """linear""",
											}

											config.update(**snake_case		)
											return config






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Union[str, Any]:
											for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case		)






					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[int]:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
																	self.check_over_configs(beta_start=snake_case		,	beta_end=snake_case		)






					def   lowerCAmelCase_							(			self:				int		)  ->      int:
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=snake_case		)






					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      List[Any]:
											snake_case_					:str		=     self.scheduler_classes[0]
											snake_case_					:Optional[Any]		=     self.get_scheduler_config()
											snake_case_					:List[str]		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:Optional[Any]		=     self.dummy_model()
											snake_case_					:Optional[int]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Optional[int]		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Optional[int]		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     model(snake_case		,	snake_case		)

																	snake_case_					:Optional[int]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Optional[Any]		=     output.prev_sample

											snake_case_					:Union[str, Any]		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Optional[Any]		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Tuple:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config(prediction_type="""v_prediction"""		)
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:List[Any]		=     self.dummy_model()
											snake_case_					:int		=     self.dummy_sample_deter * scheduler.init_noise_sigma
											snake_case_					:Dict		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:int		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Dict		=     output.prev_sample

											snake_case_					:Any		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Union[str, Any]		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_0_8.4_4_3_9		) < 1E-2
											assert abs(result_mean.item() - 0.1_4_1_2		) < 1E-3






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config()
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		,	device=snake_case		)
											snake_case_					:Any		=     torch.manual_seed(0		)

											snake_case_					:Dict		=     self.dummy_model()
											snake_case_					:List[Any]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Any		=     sample.to(snake_case		)

											for t in scheduler.timesteps:
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:Optional[Any]		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:int		=     output.prev_sample

											snake_case_					:List[Any]		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Dict		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3





from collections import OrderedDict
from typing import Mapping

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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						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       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						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),
						    ]						)
