





import argparse
import os
import shutil

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple      )      ->	Union[str, Any]:
    UpperCamelCase__							:				Union[str, Any]          =      args.pruning_method
    UpperCamelCase__							:				Dict          =      args.threshold

    UpperCamelCase__							:				List[Any]          =      args.model_name_or_path.rstrip('''/'''      )
    UpperCamelCase__							:				Optional[Any]          =      args.target_model_path

    print(f"Load fine-pruned model from {model_name_or_path}"      )
    UpperCamelCase__							:				Optional[Any]          =      torch.load(os.path.join(lowerCAmelCase__		,   '''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__							:				Any          =      tensor
            print(f"Copied layer {name}"      )
        elif "classifier" in name or "qa_output" in name:
            UpperCamelCase__							:				Tuple          =      tensor
            print(f"Copied layer {name}"      )
        elif "bias" in name:
            UpperCamelCase__							:				List[str]          =      tensor
            print(f"Copied layer {name}"      )
        else:
            if pruning_method == "magnitude":
                UpperCamelCase__							:				int          =      MagnitudeBinarizer.apply(inputs=lowerCAmelCase__		,   threshold=lowerCAmelCase__      )
                UpperCamelCase__							:				Dict          =      tensor * mask
                print(f"Pruned layer {name}"      )
            elif pruning_method == "topK":
                if "mask_scores" in name:
                    continue
                UpperCamelCase__							:				Tuple          =      name[:-6]
                UpperCamelCase__							:				Tuple          =      model[f"{prefix_}mask_scores"]
                UpperCamelCase__							:				int          =      TopKBinarizer.apply(lowerCAmelCase__		,   lowerCAmelCase__      )
                UpperCamelCase__							:				str          =      tensor * mask
                print(f"Pruned layer {name}"      )
            elif pruning_method == "sigmoied_threshold":
                if "mask_scores" in name:
                    continue
                UpperCamelCase__							:				List[str]          =      name[:-6]
                UpperCamelCase__							:				List[str]          =      model[f"{prefix_}mask_scores"]
                UpperCamelCase__							:				List[str]          =      ThresholdBinarizer.apply(lowerCAmelCase__		,   lowerCAmelCase__		,   lowerCAmelCase__      )
                UpperCamelCase__							:				str          =      tensor * mask
                print(f"Pruned layer {name}"      )
            elif pruning_method == "l0":
                if "mask_scores" in name:
                    continue
                UpperCamelCase__							:				Optional[Any]          =      name[:-6]
                UpperCamelCase__							:				str          =      model[f"{prefix_}mask_scores"]
                UpperCamelCase__							:				Optional[Any]          =      -0.1, 1.1
                UpperCamelCase__							:				Dict          =      torch.sigmoid(lowerCAmelCase__      )
                UpperCamelCase__							:				Optional[int]          =      s * (r - l) + l
                UpperCamelCase__							:				str          =      s_bar.clamp(min=0.0		,   max=1.0      )
                UpperCamelCase__							:				int          =      tensor * mask
                print(f"Pruned layer {name}"      )
            else:
                raise ValueError('''Unknown pruning method'''      )

    if target_model_path is None:
        UpperCamelCase__							:				Optional[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__":
  UpperCAmelCase_     							= 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',
  )

  UpperCAmelCase_     							= parser.parse_args()

  main(args)






"""simple docstring"""







from typing import TYPE_CHECKING

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


lowercase__		:				int          =	{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[int]          =	["""MBartTokenizer"""]

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MBartForCausalLM""",
         """MBartForConditionalGeneration""",
         """MBartForQuestionAnswering""",
         """MBartForSequenceClassification""",
         """MBartModel""",
         """MBartPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """TFMBartForConditionalGeneration""",
         """TFMBartModel""",
         """TFMBartPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """FlaxMBartForConditionalGeneration""",
         """FlaxMBartForQuestionAnswering""",
         """FlaxMBartForSequenceClassification""",
         """FlaxMBartModel""",
         """FlaxMBartPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				int          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





