



'''simple docstring'''


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Any	=   (EulerAncestralDiscreteScheduler,)
 __magic_name__       :			str	=   10




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   Tuple:



      """simple docstring"""







      a_									={
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**lowerCAmelCase_)
      return config




 def    lowercase_	( self)			->   Union[str, Any]:



      """simple docstring"""







      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=lowerCAmelCase_)




 def    lowercase_	( self)			->   Tuple:



      """simple docstring"""







      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,	[0.0_0_0_2, 0.0_0_2, 0.0_2]):
           self.check_over_configs(beta_start=lowerCAmelCase_				,	beta_end=lowerCAmelCase_)




 def    lowercase_	( self)			->   int:



      """simple docstring"""







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




 def    lowercase_	( self)			->   List[Any]:



      """simple docstring"""







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




 def    lowercase_	( self)			->   Union[str, Any]:



      """simple docstring"""







      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config()
      a_									=scheduler_class(**lowerCAmelCase_)

      scheduler.set_timesteps(self.num_inference_steps)

      a_									=torch.manual_seed(0)

      a_									=self.dummy_model()
      a_									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a_									=sample.to(lowerCAmelCase_)

      for i, t in enumerate(scheduler.timesteps):
           a_									=scheduler.scale_model_input(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=model(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=scheduler.step(lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	generator=lowerCAmelCase_)
           a_									=output.prev_sample

      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      assert abs(result_sum.item() - 1_5_2.3_1_9_2) < 1e-2
      assert abs(result_mean.item() - 0.1_9_8_3) < 1e-3




 def    lowercase_	( self)			->   List[str]:



      """simple docstring"""







      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config(prediction_type="v_prediction")
      a_									=scheduler_class(**lowerCAmelCase_)

      scheduler.set_timesteps(self.num_inference_steps)

      a_									=torch.manual_seed(0)

      a_									=self.dummy_model()
      a_									=self.dummy_sample_deter * scheduler.init_noise_sigma
      a_									=sample.to(lowerCAmelCase_)

      for i, t in enumerate(scheduler.timesteps):
           a_									=scheduler.scale_model_input(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=model(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=scheduler.step(lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	generator=lowerCAmelCase_)
           a_									=output.prev_sample

      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      assert abs(result_sum.item() - 1_0_8.4_4_3_9) < 1e-2
      assert abs(result_mean.item() - 0.1_4_1_2) < 1e-3




 def    lowercase_	( self)			->   int:



      """simple docstring"""







      a_									=self.scheduler_classes[0]
      a_									=self.get_scheduler_config()
      a_									=scheduler_class(**lowerCAmelCase_)

      scheduler.set_timesteps(self.num_inference_steps				,	device=lowerCAmelCase_)
      a_									=torch.manual_seed(0)

      a_									=self.dummy_model()
      a_									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a_									=sample.to(lowerCAmelCase_)

      for t in scheduler.timesteps:
           a_									=scheduler.scale_model_input(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=model(lowerCAmelCase_				,	lowerCAmelCase_)

           a_									=scheduler.step(lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	generator=lowerCAmelCase_)
           a_									=output.prev_sample

      a_									=torch.sum(torch.abs(lowerCAmelCase_))
      a_									=torch.mean(torch.abs(lowerCAmelCase_))

      assert abs(result_sum.item() - 1_5_2.3_1_9_2) < 1e-2
      assert abs(result_mean.item() - 0.1_9_8_3) < 1e-3







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


