code: 
  
  
  """simple docstring"""
  
  
  
  
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  							__a				   =					(EulerAncestralDiscreteScheduler,)
  							__a				   =					10
  
  
  
  
  							def      __a      (    self:						List[str]    ,       **A_:						List[str]      ):
  										__lowerCamelCase        =			{
  										    """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:						Dict      ):
  										for timesteps in [10, 50, 1_00, 10_00]:
  													self.check_over_configs(num_train_timesteps=A_      )
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										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:						int      ):
  										for schedule in ["linear", "scaled_linear"]:
  													self.check_over_configs(beta_schedule=A_      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										for prediction_type in ["epsilon", "v_prediction"]:
  													self.check_over_configs(prediction_type=A_      )
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			self.scheduler_classes[0]
  										__lowerCamelCase        =			self.get_scheduler_config()
  										__lowerCamelCase        =			scheduler_class(**A_      )
  
  										scheduler.set_timesteps(self.num_inference_steps      )
  
  										__lowerCamelCase        =			torch.manual_seed(0      )
  
  										__lowerCamelCase        =			self.dummy_model()
  										__lowerCamelCase        =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  										__lowerCamelCase        =			sample.to(A_      )
  
  										for i, t in enumerate(scheduler.timesteps      ):
  													__lowerCamelCase        =			scheduler.scale_model_input(A_    ,       A_      )
  
  													__lowerCamelCase        =			model(A_    ,       A_      )
  
  													__lowerCamelCase        =			scheduler.step(A_    ,       A_    ,       A_    ,       generator=A_      )
  													__lowerCamelCase        =			output.prev_sample
  
  										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
  										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )
  
  										assert abs(result_sum.item() - 152.3_192      ) < 1E-2
  										assert abs(result_mean.item() - 0.1_983      ) < 1E-3
  
  
  
  
  							def      __a      (    self:						str      ):
  										__lowerCamelCase        =			self.scheduler_classes[0]
  										__lowerCamelCase        =			self.get_scheduler_config(prediction_type="""v_prediction"""      )
  										__lowerCamelCase        =			scheduler_class(**A_      )
  
  										scheduler.set_timesteps(self.num_inference_steps      )
  
  										__lowerCamelCase        =			torch.manual_seed(0      )
  
  										__lowerCamelCase        =			self.dummy_model()
  										__lowerCamelCase        =			self.dummy_sample_deter * scheduler.init_noise_sigma
  										__lowerCamelCase        =			sample.to(A_      )
  
  										for i, t in enumerate(scheduler.timesteps      ):
  													__lowerCamelCase        =			scheduler.scale_model_input(A_    ,       A_      )
  
  													__lowerCamelCase        =			model(A_    ,       A_      )
  
  													__lowerCamelCase        =			scheduler.step(A_    ,       A_    ,       A_    ,       generator=A_      )
  													__lowerCamelCase        =			output.prev_sample
  
  										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
  										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )
  
  										assert abs(result_sum.item() - 108.4_439      ) < 1E-2
  										assert abs(result_mean.item() - 0.1_412      ) < 1E-3
  
  
  
  
  							def      __a      (    self:						str      ):
  										__lowerCamelCase        =			self.scheduler_classes[0]
  										__lowerCamelCase        =			self.get_scheduler_config()
  										__lowerCamelCase        =			scheduler_class(**A_      )
  
  										scheduler.set_timesteps(self.num_inference_steps    ,       device=A_      )
  										__lowerCamelCase        =			torch.manual_seed(0      )
  
  										__lowerCamelCase        =			self.dummy_model()
  										__lowerCamelCase        =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  										__lowerCamelCase        =			sample.to(A_      )
  
  										for t in scheduler.timesteps:
  													__lowerCamelCase        =			scheduler.scale_model_input(A_    ,       A_      )
  
  													__lowerCamelCase        =			model(A_    ,       A_      )
  
  													__lowerCamelCase        =			scheduler.step(A_    ,       A_    ,       A_    ,       generator=A_      )
  													__lowerCamelCase        =			output.prev_sample
  
  										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
  										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )
  
  										assert abs(result_sum.item() - 152.3_192      ) < 1E-2
  										assert abs(result_mean.item() - 0.1_983      ) < 1E-3
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  