






import argparse
import os
import shutil

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


def 				snake_case__							(							UpperCAmelCase							:  Optional[int]       ):
    lowerCAmelCase__   :List[Any]      							=				args.pruning_method
    lowerCAmelCase__   :Any      							=				args.threshold

    lowerCAmelCase__   :List[Any]      							=				args.model_name_or_path.rstrip("/"       )
    lowerCAmelCase__   :int      							=				args.target_model_path

    print(F'''Load fine-pruned model from {model_name_or_path}'''       )
    lowerCAmelCase__   :str      							=				torch.load(os.path.join(UpperCAmelCase    ,			"pytorch_model.bin"       )       )
    lowerCAmelCase__   :str      							=				{}

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

    if target_model_path is None:
        lowerCAmelCase__   :Tuple      							=				os.path.join(
            os.path.dirname(UpperCAmelCase       )    ,			F'''bertarized_{os.path.basename(UpperCAmelCase       )}'''       )

    if not os.path.isdir(UpperCAmelCase       ):
        shutil.copytree(UpperCAmelCase    ,			UpperCAmelCase       )
        print(F'''\nCreated folder {target_model_path}'''       )

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


if __name__ == "__main__":
 _a    :   List[Any]        =			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    :   List[Any]        =			parser.parse_args()

 main(args)











from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_a    :   Optional[Any]        =			[
    """python""",
    """tqdm""",
    """regex""",
    """requests""",
    """packaging""",
    """filelock""",
    """numpy""",
    """tokenizers""",
    """huggingface-hub""",
    """safetensors""",
    """accelerate""",
    """pyyaml""",
]

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed
  elif pkg == "accelerate":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_accelerate_available

   # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
   # Transformers with PyTorch
   if not is_accelerate_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def 				snake_case__							(							UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  Union[str, Any]=None       ):
    require_version(deps[pkg]    ,			UpperCAmelCase       )



