


import argparse
import os
import shutil

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



def 	A_       (  _lowerCAmelCase  )		->				List[str]:
 UpperCamelCase			:      List[str] 			=     args.pruning_method
 UpperCamelCase			:      Tuple 			=     args.threshold

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

 print(F"""Load fine-pruned model from {model_name_or_path}"""  )
 UpperCamelCase			:      Optional[int] 			=     torch.load(os.path.join(a_							,     "pytorch_model.bin"  )  )
 UpperCamelCase			:      List[str] 			=     {}

 for name, tensor in model.items():
  if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
   UpperCamelCase			:      int 			=     tensor
   print(F"""Copied layer {name}"""  )
  elif "classifier" in name or "qa_output" in name:
   UpperCamelCase			:      int 			=     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			:      List[Any] 			=     MagnitudeBinarizer.apply(inputs=a_							,     threshold=a_  )
    UpperCamelCase			:      Optional[Any] 			=     tensor * mask
    print(F"""Pruned layer {name}"""  )
   elif pruning_method == "topK":
    if "mask_scores" in name:
     continue
    UpperCamelCase			:      Any 			=     name[:-6]
    UpperCamelCase			:      int 			=     model[F"""{prefix_}mask_scores"""]
    UpperCamelCase			:      List[str] 			=     TopKBinarizer.apply(a_							,     a_  )
    UpperCamelCase			:      Optional[int] 			=     tensor * mask
    print(F"""Pruned layer {name}"""  )
   elif pruning_method == "sigmoied_threshold":
    if "mask_scores" in name:
     continue
    UpperCamelCase			:      List[Any] 			=     name[:-6]
    UpperCamelCase			:      Optional[Any] 			=     model[F"""{prefix_}mask_scores"""]
    UpperCamelCase			:      Any 			=     ThresholdBinarizer.apply(a_							,     a_							,     a_  )
    UpperCamelCase			:      Optional[int] 			=     tensor * mask
    print(F"""Pruned layer {name}"""  )
   elif pruning_method == "l0":
    if "mask_scores" in name:
     continue
    UpperCamelCase			:      List[str] 			=     name[:-6]
    UpperCamelCase			:      Optional[Any] 			=     model[F"""{prefix_}mask_scores"""]
    UpperCamelCase			:      Optional[Any] 			=     -0.1, 1.1
    UpperCamelCase			:      int 			=     torch.sigmoid(a_  )
    UpperCamelCase			:      Any 			=     s * (r - l) + l
    UpperCamelCase			:      List[str] 			=     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(a_  )							,     F"""bertarized_{os.path.basename(a_  )}"""  )

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

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


if __name__ == "__main__":
    __lowerCamelCase :  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""",
    )

    __lowerCamelCase :  Dict										=  parser.parse_args()

    main(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
