
"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[Any]	=       (HeunDiscreteScheduler,)
    __lowercase				:			Optional[Any]	=       1_0
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Union[str, Any]            = {
          "num_train_timesteps": 11_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self  ):
      for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,     [0.0002, 0.002, 0.02]  ):
        self.check_over_configs(beta_start=snake_case_    ,     beta_end=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for schedule in ["linear", "scaled_linear", "exp"]:
        self.check_over_configs(beta_schedule=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for clip_sample_range in [1.0, 2.0, 3.0]:
        self.check_over_configs(clip_sample_range=snake_case_    ,     clip_sample=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    str            = self.get_scheduler_config()
      _snake_case  :    Dict            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Dict            = sample.to(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    List[str]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

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

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    str            = output.prev_sample

      _snake_case  :    Optional[Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(snake_case_  )  )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    Dict            = self.get_scheduler_config(prediction_type="v_prediction"  )
      _snake_case  :    int            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    Tuple            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

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

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Optional[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Any            = output.prev_sample

      _snake_case  :    int            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Dict            = torch.mean(torch.abs(snake_case_  )  )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 4.69_34E-07  ) < 1E-2
        assert abs(result_mean.item() - 6.11_12E-10  ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.scheduler_classes[0]
      _snake_case  :    int            = self.get_scheduler_config()
      _snake_case  :    Tuple            = scheduler_class(**snake_case_  )

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

      _snake_case  :    Dict            = self.dummy_model()
      _snake_case  :    Dict            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        _snake_case  :    Optional[Any]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Union[str, Any]            = output.prev_sample

      _snake_case  :    Tuple            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    str            = torch.mean(torch.abs(snake_case_  )  )

      if str(snake_case_  ).startswith("cpu"  ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      elif str(snake_case_  ).startswith("mps"  ):
        # Larger tolerance on mps
        assert abs(result_mean.item() - 0.0002  ) < 1E-2
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.scheduler_classes[0]
      _snake_case  :    int            = self.get_scheduler_config()
      _snake_case  :    int            = scheduler_class(**snake_case_    ,     use_karras_sigmas=snake_case_  )

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

      _snake_case  :    int            = self.dummy_model()
      _snake_case  :    int            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

      for t in scheduler.timesteps:
        _snake_case  :    Any            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    List[str]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    List[str]            = output.prev_sample

      _snake_case  :    Dict            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    int            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 0.00015  ) < 1E-2
      assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07  ) < 1E-2



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)

__snake_case					:							List[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

									with patch.object(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





