


'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowercase:		Dict    						=  logging.get_logger(__name__)

__lowercase:		List[str]    						=  {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Union[str, Any]            =       'electra'

 def __init__(			self			:						Union[str, Any],       a_			:						Union[str, Any]=3_0522,       a_			:						Optional[int]=128,       a_			:						Tuple=256,       a_			:						int=12,       a_			:						Union[str, Any]=4,       a_			:						List[str]=1024,       a_			:						Optional[Any]="gelu",       a_			:						int=0.1,       a_			:						List[str]=0.1,       a_			:						Dict=512,       a_			:						int=2,       a_			:						str=0.02,       a_			:						Optional[Any]=1e-1_2,       a_			:						Dict="first",       a_			:						List[Any]=True,       a_			:						Optional[int]="gelu",       a_			:						Optional[int]=0.1,       a_			:						List[Any]=0,       a_			:						Any="absolute",       a_			:						str=True,       a_			:						List[str]=None,       **a_			:						Optional[int],       ):





    """simple docstring"""



    super().__init__(pad_token_id=a_,       **a_       )

    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    embedding_size
    UpperCamelCase__			    =    hidden_size
    UpperCamelCase__			    =    num_hidden_layers
    UpperCamelCase__			    =    num_attention_heads
    UpperCamelCase__			    =    intermediate_size
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    type_vocab_size
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    layer_norm_eps

    UpperCamelCase__			    =    summary_type
    UpperCamelCase__			    =    summary_use_proj
    UpperCamelCase__			    =    summary_activation
    UpperCamelCase__			    =    summary_last_dropout
    UpperCamelCase__			    =    position_embedding_type
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    classifier_dropout







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    if self.task == "multiple-choice":
       UpperCamelCase__			    =    {0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCamelCase__			    =    {0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("input_ids", dynamic_axis),
            ("attention_mask", dynamic_axis),
            ("token_type_ids", dynamic_axis),
        ]       )






"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






       def 				__A			( self  ):
              for timesteps in [10, 50, 1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__  )






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






       def 				__A			( self  ):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__  )






       def 				__A			( self  ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__  )






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






