

'''simple docstring'''

import argparse
import os
import shutil

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





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					args.pruning_method
  SCREAMING_SNAKE_CASE:List[Any]						      =					args.threshold

  SCREAMING_SNAKE_CASE:List[str]						      =					args.model_name_or_path.rstrip("/" )
  SCREAMING_SNAKE_CASE:str						      =					args.target_model_path

  print(F'''Load fine-pruned model from {model_name_or_path}''' )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.load(os.path.join(__lowerCAmelCase      ,       "pytorch_model.bin" ) )
  SCREAMING_SNAKE_CASE:List[Any]						      =					{}

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

  if target_model_path is None:
    SCREAMING_SNAKE_CASE:List[Any]						      =					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'''

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  for line in lines:
    SCREAMING_SNAKE_CASE:Any						      =					re.sub(r"#.*"      ,       ""      ,       snake_case )  # remove comments
    if line:
      filtered_lines.append(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					"\n".join(snake_case )

  # Make a hash from all this code
  SCREAMING_SNAKE_CASE:str						      =					full_str.encode("utf-8" )
  return shaaaa(snake_case ).hexdigest()


# get importable module names and hash for caching
A_       						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A_       						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_       						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A_       						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
    _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")






