



"""simple docstring"""





from collections import namedtuple

lowerCamelCase          =						namedtuple("""from_to""", """from_ to""")

lowerCamelCase          =						{
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

lowerCamelCase          =						{
    'mm': from_to(0.001, 1_000),
    'cm': from_to(0.01, 100),
    'm': from_to(1, 1),
    'km': from_to(1_000, 0.001),
    'in': from_to(0.0_254, 39.3_701),
    'ft': from_to(0.3_048, 3.28_084),
    'yd': from_to(0.9_144, 1.09_361),
    'mi': from_to(1_609.34, 0.000_621_371),
}
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__      ):
      UpperCAmelCase_			 =							from_type.lower().rstrip("s"      )
      UpperCAmelCase_			 =							TYPE_CONVERSION.get(__lowercase	,			__lowercase      )
      UpperCAmelCase_			 =							to_type.lower().rstrip("s"      )
      UpperCAmelCase_			 =							TYPE_CONVERSION.get(__lowercase	,			__lowercase      )
      if new_from not in METRIC_CONVERSION:
            UpperCAmelCase_			 =							(
                f"""Invalid 'from_type' value: {from_type!r}.\n"""
                f"""Conversion abbreviations are: {', '.join(__lowercase      )}"""
            )
            raise ValueError(__lowercase      )
      if new_to not in METRIC_CONVERSION:
            UpperCAmelCase_			 =							(
                f"""Invalid 'to_type' value: {to_type!r}.\n"""
                f"""Conversion abbreviations are: {', '.join(__lowercase      )}"""
            )
            raise ValueError(__lowercase      )
      return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

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

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)