






"""simple docstring"""





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     args.serialization_dir
    UpperCamelCase__								=     args.pruning_method
    UpperCamelCase__								=     args.threshold

    UpperCamelCase__								=     torch.load(os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )			,						map_location='cpu'       )

    UpperCamelCase__								=     0  # Number of remaining (not pruned) params in the encoder
    UpperCamelCase__								=     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__								=     TopKBinarizer.apply(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ).sum().item()
            elif pruning_method == "sigmoied_threshold":
                UpperCamelCase__								=     ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ).sum().item()
            elif pruning_method == "l0":
                UpperCamelCase__							,    UpperCamelCase__								=     -0.1, 1.1
                UpperCamelCase__								=     torch.sigmoid(SCREAMING_SNAKE_CASE       )
                UpperCamelCase__								=     s * (r - l) + l
                UpperCamelCase__								=     s_bar.clamp(min=0.0			,						max=1.0       )
                UpperCamelCase__								=     (mask > 0.0).sum().item()
            else:
                raise ValueError('Unknown pruning method'       )
            remaining_count += mask_ones
            print(name.ljust(60			,						' '       )			,						str(round(1_00 * mask_ones / param.numel()			,						3       )       ).ljust(20			,						' '       )			,						str(SCREAMING_SNAKE_CASE       )       )
        else:
            encoder_count += param.numel()
            if "bias" in name or "LayerNorm" in name:
                remaining_count += param.numel()

    print(''       )
    print('Remaining Weights (global) %: '			,						1_00 * remaining_count / encoder_count       )


if __name__ == "__main__":
  A__       :			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""",
  )

  A__       :			str=						parser.parse_args()

  main(args)












"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE = 10_00       )       ->						str:
    """simple docstring"""

    if (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (digit_position <= 0):
        raise ValueError('Digit position must be a positive integer'       )
    elif (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (precision < 0):
        raise ValueError('Precision must be a nonnegative integer'       )

    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    UpperCamelCase__								=     (
        4 * _subsum(SCREAMING_SNAKE_CASE			,						1			,						SCREAMING_SNAKE_CASE       )
        - 2 * _subsum(SCREAMING_SNAKE_CASE			,						4			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						5			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						6			,						SCREAMING_SNAKE_CASE       )
    )

    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16       )       )[2:]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						float:
    """simple docstring"""

    UpperCamelCase__								=     0.0
    for sum_index in range(digit_pos_to_extract + precision       ):
        UpperCamelCase__								=     8 * sum_index + denominator_addend
        if sum_index < digit_pos_to_extract:
            # if the exponential term is an integer and we mod it by the denominator
            # before dividing, only the integer part of the sum will change;
            # the fractional part will not
            UpperCamelCase__								=     pow(
                16			,						digit_pos_to_extract - 1 - sum_index			,						SCREAMING_SNAKE_CASE       )
        else:
            UpperCamelCase__								=     pow(16			,						digit_pos_to_extract - 1 - sum_index       )
        total += exponential_term / denominator
    return total


if __name__ == "__main__":
  import doctest

  doctest.testmod()




