






import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
   from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm

def     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  if is_torch_version('<'      ,       '2.0.0'    ) or not hasattr(UpperCAmelCase__      ,       '_dynamo'    ):
    return False
  return isinstance(UpperCAmelCase__      ,       torch._dynamo.eval_frame.OptimizedModule    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = True    )			->							Any:
  UpperCamelCase_:   Optional[Any]        =							(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  UpperCamelCase_:   int        =							is_compiled_module(UpperCAmelCase__    )
  if is_compiled:
    UpperCamelCase_:   List[str]        =							model
    UpperCamelCase_:   Dict        =							model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ):
    UpperCamelCase_:   Dict        =							model.module

  if not keep_fpaa_wrapper:
    UpperCamelCase_:   int        =							getattr(UpperCAmelCase__      ,       'forward'    )
    UpperCamelCase_:   List[str]        =							model.__dict__.pop('_original_forward'      ,       UpperCAmelCase__    )
    if original_forward is not None:
      while hasattr(UpperCAmelCase__      ,       '__wrapped__'    ):
        UpperCamelCase_:   Any        =							forward.__wrapped__
        if forward == original_forward:
          break
      UpperCamelCase_:   Optional[int]        =							forward
    if getattr(UpperCAmelCase__      ,       '_converted_to_transformer_engine'      ,       UpperCAmelCase__    ):
      convert_model(UpperCAmelCase__      ,       to_transformer_engine=UpperCAmelCase__    )

  if is_compiled:
    UpperCamelCase_:   Union[str, Any]        =							model
    UpperCamelCase_:   Tuple        =							compiled_model

  return model

def     snake_case   ()			->							List[str]:
  PartialState().wait_for_everyone()

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Dict:
  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(UpperCAmelCase__      ,       UpperCAmelCase__    )
  elif PartialState().local_process_index == 0:
    torch.save(UpperCAmelCase__      ,       UpperCAmelCase__    )

@contextmanager
def     snake_case   (**UpperCAmelCase__    )			->							Any:
  for key, value in kwargs.items():
    UpperCamelCase_:   int        =							str(UpperCAmelCase__    )

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]

def     snake_case   (UpperCAmelCase__    )			->							str:
  if not hasattr(UpperCAmelCase__      ,       '__qualname__'    ) and not hasattr(UpperCAmelCase__      ,       '__name__'    ):
    UpperCamelCase_:   List[Any]        =							getattr(UpperCAmelCase__      ,       '__class__'      ,       UpperCAmelCase__    )
  if hasattr(UpperCAmelCase__      ,       '__qualname__'    ):
    return obj.__qualname__
  if hasattr(UpperCAmelCase__      ,       '__name__'    ):
    return obj.__name__
  return str(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  for key, value in source.items():
    if isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ):
      UpperCamelCase_:   Any        =							destination.setdefault(UpperCAmelCase__      ,       {}    )
      merge_dicts(UpperCAmelCase__      ,       UpperCAmelCase__    )
    else:
      UpperCamelCase_:   str        =							value

  return destination

def     snake_case   (UpperCAmelCase__ = None    )			->							bool:
  if port is None:
    UpperCamelCase_:   List[str]        =							2_9_5_0_0
  with socket.socket(socket.AF_INET      ,       socket.SOCK_STREAM    ) as s:
    return s.connect_ex(('localhost', port)    ) == 0


import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

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

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

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

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      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_     :    Union[str, 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_     :    Dict     		=   parser.parse_args()

      main(args)

