




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(DDPMScheduler,)





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							Dict       =      {
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self  )			->		List[str]:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,[0.0_02, 0.02, 0.2, 2]  ):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE       ,beta_end=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE  )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_SCREAMING_SNAKE_CASE       ,prediction_type=_SCREAMING_SNAKE_CASE       ,sample_max_value=_SCREAMING_SNAKE_CASE       ,)





    def 							a__				(				self  )			->		Tuple:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Any       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							str       =      self.get_scheduler_config()
         UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_09_79  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1e-5





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[str]       =      self.get_scheduler_config()
         UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      len(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      self.dummy_model()
         UpperCAmelCase_      :							Dict       =      self.dummy_sample_deter
         UpperCAmelCase_      :							int       =      torch.manual_seed(0  )

         for t in reversed(range(_SCREAMING_SNAKE_CASE  )  ):
              # 1. predict noise residual
              UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_      :							int       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE  ).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase_      :							str       =      pred_prev_sample

         UpperCAmelCase_      :							Any       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							Any       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 2_58.96_06  ) < 1e-2
         assert abs(result_mean.item() - 0.33_72  ) < 1e-3





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Dict       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[Any]       =      self.get_scheduler_config(prediction_type='''v_prediction'''  )
         UpperCAmelCase_      :							int       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[str]       =      len(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      self.dummy_model()
         UpperCAmelCase_      :							Optional[int]       =      self.dummy_sample_deter
         UpperCAmelCase_      :							int       =      torch.manual_seed(0  )

         for t in reversed(range(_SCREAMING_SNAKE_CASE  )  ):
              # 1. predict noise residual
              UpperCAmelCase_      :							Optional[Any]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE  ).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase_      :							str       =      pred_prev_sample

         UpperCAmelCase_      :							Any       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							Dict       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 2_02.02_96  ) < 1e-2
         assert abs(result_mean.item() - 0.26_31  ) < 1e-3





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[str]       =      self.get_scheduler_config()
         UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      scheduler.timesteps

         for i, timestep in enumerate(_SCREAMING_SNAKE_CASE  ):
              if i == len(_SCREAMING_SNAKE_CASE  ) - 1:
                   UpperCAmelCase_      :							str       =      -1
              else:
                   UpperCAmelCase_      :							Tuple       =      timesteps[i + 1]

              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.previous_timestep(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[Any]       =      prev_t.item()

              self.assertEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Optional[Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							Tuple       =      self.get_scheduler_config()
         UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      [100, 87, 50, 51, 0]

         with self.assertRaises(_SCREAMING_SNAKE_CASE       ,msg='''`custom_timesteps` must be in descending order.'''  ):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							str       =      self.get_scheduler_config()
         UpperCAmelCase_      :							Dict       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      [100, 87, 50, 1, 0]
         UpperCAmelCase_      :							List[Any]       =      len(_SCREAMING_SNAKE_CASE  )

         with self.assertRaises(_SCREAMING_SNAKE_CASE       ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''  ):
              scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE       ,timesteps=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							Any       =      self.get_scheduler_config()
         UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             _SCREAMING_SNAKE_CASE       ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE  )





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )