'''simple docstring'''
import argparse
import os
import shutil

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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  args.pruning_method
      UpperCAmelCase_            =  args.threshold

      UpperCAmelCase_            =  args.model_name_or_path.rstrip('''/'''					)
      UpperCAmelCase_            =  args.target_model_path

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

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

      if target_model_path is None:
            UpperCAmelCase_            =  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__":
      lowercase__  :      Optional[int]        =					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",
      )

      lowercase__  :      Union[str, Any]        =					parser.parse_args()

      main(args)






'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

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





