'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def __a			(						__lowerCamelCase      :    Dict					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   args.serialization_dir
      lowercase_     			=   args.pruning_method
      lowercase_     			=   args.threshold

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

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

       lowerCAmelCase_			:			Dict    	=   parser.parse_args()

       main(args)







'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






