


"""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








"""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      )




