
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCamelCase        =abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)





def    _a (						lowerCamelCase							):
 from transformers.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(lowerCamelCase							)





def    _a (						lowerCamelCase							):
 from transformers.testing_utils import pytest_terminal_summary_main

 lowerCamelCase		:     Dict          =				terminalreporter.config.getoption("""--make-reports"""							)
 if make_reports:
  pytest_terminal_summary_main(lowerCamelCase,     id=lowerCamelCase							)






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `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(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




