
"""simple docstring"""





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    args.pruning_method
   UpperCAmelCase_					:					int			     =    args.threshold

   UpperCAmelCase_					:					Optional[int]			     =    args.model_name_or_path.rstrip("/"  )
   UpperCAmelCase_					:					List[str]			     =    args.target_model_path

   print(f"""Load fine-pruned model from {model_name_or_path}"""  )
   UpperCAmelCase_					:					Tuple			     =    torch.load(os.path.join(__lowerCamelCase,		"pytorch_model.bin"  )  )
   UpperCAmelCase_					:					Tuple			     =    {}

   for name, tensor in model.items():
      if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
         UpperCAmelCase_					:					Optional[Any]			     =    tensor
         print(f"""Copied layer {name}"""  )
      elif "classifier" in name or "qa_output" in name:
         UpperCAmelCase_					:					Optional[Any]			     =    tensor
         print(f"""Copied layer {name}"""  )
      elif "bias" in name:
         UpperCAmelCase_					:					Optional[int]			     =    tensor
         print(f"""Copied layer {name}"""  )
      else:
         if pruning_method == "magnitude":
            UpperCAmelCase_					:					str			     =    MagnitudeBinarizer.apply(inputs=__lowerCamelCase,		threshold=__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[int]			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Optional[Any]			     =    name[:-6]
            UpperCAmelCase_					:					int			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_					:					List[Any]			     =    TopKBinarizer.apply(__lowerCamelCase,		__lowerCamelCase  )
            UpperCAmelCase_					:					int			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Tuple			     =    name[:-6]
            UpperCAmelCase_					:					Optional[Any]			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_					:					Any			     =    ThresholdBinarizer.apply(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
            UpperCAmelCase_					:					Tuple			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Optional[int]			     =    name[:-6]
            UpperCAmelCase_					:					List[str]			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    -0.1, 1.1
            UpperCAmelCase_					:					str			     =    torch.sigmoid(__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[Any]			     =    s * (r - l) + l
            UpperCAmelCase_					:					Any			     =    s_bar.clamp(min=0.0,		max=1.0  )
            UpperCAmelCase_					:					Optional[Any]			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         else:
            raise ValueError("Unknown pruning method"  )

   if target_model_path is None:
      UpperCAmelCase_					:					int			     =    os.path.join(
          os.path.dirname(__lowerCamelCase  ),		f"""bertarized_{os.path.basename(__lowerCamelCase  )}"""  )

   if not os.path.isdir(__lowerCamelCase  ):
      shutil.copytree(__lowerCamelCase,		__lowerCamelCase  )
      print(f"""\nCreated folder {target_model_path}"""  )

   torch.save(__lowerCamelCase,		os.path.join(__lowerCamelCase,		"pytorch_model.bin"  )  )
   print("\nPruned model saved! See you later!"  )


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

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
            ' sigmoied_threshold = Soft movement pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `magnitude` and `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(
        '--model_name_or_path',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )
    parser.add_argument(
        '--target_model_path',
        default=None,
        type=str,
        required=False,
        help='Folder containing the model that was previously fine-pruned',
    )

    _a		       =							parser.parse_args()

    main(args)




"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    {
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


