






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def        lowerCamelCase__   (					__lowerCamelCase    :  Any	):
  __UpperCAmelCase							:			Any     =						args.serialization_dir
  __UpperCAmelCase							:			str     =						args.pruning_method
  __UpperCAmelCase							:			Any     =						args.threshold

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

  __UpperCAmelCase							:			int     =						0  # Number of remaining (not pruned) params in the encoder
  __UpperCAmelCase							:			Optional[int]     =						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":
        __UpperCAmelCase							:			Optional[int]     =						TopKBinarizer.apply(__lowerCamelCase						,	__lowerCamelCase	).sum().item()
      elif pruning_method == "sigmoied_threshold":
        __UpperCAmelCase							:			Optional[Any]     =						ThresholdBinarizer.apply(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	).sum().item()
      elif pruning_method == "l0":
        __UpperCAmelCase							:			Optional[int]     =						-0.1, 1.1
        __UpperCAmelCase							:			List[str]     =						torch.sigmoid(__lowerCamelCase	)
        __UpperCAmelCase							:			Optional[int]     =						s * (r - l) + l
        __UpperCAmelCase							:			Union[str, Any]     =						s_bar.clamp(min=0.0						,	max=1.0	)
        __UpperCAmelCase							:			List[str]     =						(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__":
       a	: Union[str, Any]      =				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",
       )

       a	: str      =				parser.parse_args()

       main(args)










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


a	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						load_config_from_file(__lowerCamelCase	)

  if config_file.endswith(""".json"""	):
    config.to_json_file(__lowerCamelCase	)
  else:
    config.to_yaml_file(__lowerCamelCase	)
  return config_file






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						parser.add_parser("""update"""						,	parents=__lowerCamelCase						,	help=__lowerCamelCase						,	formatter_class=__lowerCamelCase	)
  parser.add_argument(
      """--config_file"""						,	default=__lowerCamelCase						,	help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )						,	)

  parser.set_defaults(func=__lowerCamelCase	)
  return parser







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


