
"""simple docstring"""





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    args.pruning_method
   UpperCAmelCase_					:					int			     =    args.threshold

   UpperCAmelCase_					:					Optional[int]			     =    args.model_name_or_path.rstrip("/"  )
   UpperCAmelCase_					:					List[str]			     =    args.target_model_path

   print(f"""Load fine-pruned model from {model_name_or_path}"""  )
   UpperCAmelCase_					:					Tuple			     =    torch.load(os.path.join(__lowerCamelCase,		"pytorch_model.bin"  )  )
   UpperCAmelCase_					:					Tuple			     =    {}

   for name, tensor in model.items():
      if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
         UpperCAmelCase_					:					Optional[Any]			     =    tensor
         print(f"""Copied layer {name}"""  )
      elif "classifier" in name or "qa_output" in name:
         UpperCAmelCase_					:					Optional[Any]			     =    tensor
         print(f"""Copied layer {name}"""  )
      elif "bias" in name:
         UpperCAmelCase_					:					Optional[int]			     =    tensor
         print(f"""Copied layer {name}"""  )
      else:
         if pruning_method == "magnitude":
            UpperCAmelCase_					:					str			     =    MagnitudeBinarizer.apply(inputs=__lowerCamelCase,		threshold=__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[int]			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Optional[Any]			     =    name[:-6]
            UpperCAmelCase_					:					int			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_					:					List[Any]			     =    TopKBinarizer.apply(__lowerCamelCase,		__lowerCamelCase  )
            UpperCAmelCase_					:					int			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Tuple			     =    name[:-6]
            UpperCAmelCase_					:					Optional[Any]			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_					:					Any			     =    ThresholdBinarizer.apply(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
            UpperCAmelCase_					:					Tuple			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            UpperCAmelCase_					:					Optional[int]			     =    name[:-6]
            UpperCAmelCase_					:					List[str]			     =    model[f"""{prefix_}mask_scores"""]
            UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    -0.1, 1.1
            UpperCAmelCase_					:					str			     =    torch.sigmoid(__lowerCamelCase  )
            UpperCAmelCase_					:					Optional[Any]			     =    s * (r - l) + l
            UpperCAmelCase_					:					Any			     =    s_bar.clamp(min=0.0,		max=1.0  )
            UpperCAmelCase_					:					Optional[Any]			     =    tensor * mask
            print(f"""Pruned layer {name}"""  )
         else:
            raise ValueError("Unknown pruning method"  )

   if target_model_path is None:
      UpperCAmelCase_					:					int			     =    os.path.join(
          os.path.dirname(__lowerCamelCase  ),		f"""bertarized_{os.path.basename(__lowerCamelCase  )}"""  )

   if not os.path.isdir(__lowerCamelCase  ):
      shutil.copytree(__lowerCamelCase,		__lowerCamelCase  )
      print(f"""\nCreated folder {target_model_path}"""  )

   torch.save(__lowerCamelCase,		os.path.join(__lowerCamelCase,		"pytorch_model.bin"  )  )
   print("\nPruned model saved! See you later!"  )


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

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
            ' sigmoied_threshold = Soft movement pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `magnitude` and `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(
        '--model_name_or_path',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )
    parser.add_argument(
        '--target_model_path',
        default=None,
        type=str,
        required=False,
        help='Folder containing the model that was previously fine-pruned',
    )

    _a		       =							parser.parse_args()

    main(args)







'''simple docstring'''

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str | Literal[False]:
		_a     :      Optional[int]        =							list(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count += 1
						_a     :      Optional[int]        =							'_'
		if count > 1:
				return False
		else:
				return "".join(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      list[str]:
		_a     :      Optional[int]        =							[]
		while True:
				_a     :      Any        =							['$'] * len(lowerCAmelCase_  )
				_a     :      List[str]        =							[]
				for i in range(len(lowerCAmelCase_  )  ):
						for j in range(i + 1						,			len(lowerCAmelCase_  )  ):
								_a     :      Optional[int]        =							compare_string(binary[i]						,			binary[j]  )
								if k is False:
										_a     :      Optional[Any]        =							'*'
										_a     :      Optional[Any]        =							'*'
										temp.append('X'  )
				for i in range(len(lowerCAmelCase_  )  ):
						if checka[i] == "$":
								pi.append(binary[i]  )
				if len(lowerCAmelCase_  ) == 0:
						return pi
				_a     :      Any        =							list(set(lowerCAmelCase_  )  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      int        =							[]
		for minterm in minterms:
				_a     :      Optional[int]        =							''
				for _ in range(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							str(minterm % 2  ) + string
						minterm //= 2
				temp.append(lowerCAmelCase_  )
		return temp



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      int        =							list(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							list(lowerCAmelCase_  )
		_a     :      str        =							0
		for i in range(len(lowerCAmelCase_  )  ):
				if lista[i] != lista[i]:
						count_n += 1
		return count_n == count



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							[]
		_a     :      Optional[Any]        =							[0] * len(lowerCAmelCase_  )
		for i in range(len(chart[0]  )  ):
				_a     :      Union[str, Any]        =							0
				_a     :      int        =							-1
				for j in range(len(lowerCAmelCase_  )  ):
						if chart[j][i] == 1:
								count += 1
								_a     :      int        =							j
				if count == 1:
						_a     :      List[Any]        =							1
		for i in range(len(lowerCAmelCase_  )  ):
				if select[i] == 1:
						for j in range(len(chart[0]  )  ):
								if chart[i][j] == 1:
										for k in range(len(lowerCAmelCase_  )  ):
												_a     :      Any        =							0
						temp.append(prime_implicants[i]  )
		while True:
				_a     :      Union[str, Any]        =							0
				_a     :      List[Any]        =							-1
				_a     :      str        =							0
				for i in range(len(lowerCAmelCase_  )  ):
						_a     :      Union[str, Any]        =							chart[i].count(1  )
						if count_n > max_n:
								_a     :      Any        =							count_n
								_a     :      int        =							i

				if max_n == 0:
						return temp

				temp.append(prime_implicants[rem]  )

				for i in range(len(chart[0]  )  ):
						if chart[rem][i] == 1:
								for j in range(len(lowerCAmelCase_  )  ):
										_a     :      List[str]        =							0



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[list[int]]:
		_a     :      int        =							[[0 for x in range(len(lowerCAmelCase_  )  )] for x in range(len(lowerCAmelCase_  )  )]
		for i in range(len(lowerCAmelCase_  )  ):
				_a     :      str        =							prime_implicants[i].count('_'  )
				for j in range(len(lowerCAmelCase_  )  ):
						if is_for_table(prime_implicants[i]						,			binary[j]						,			lowerCAmelCase_  ):
								_a     :      Optional[Any]        =							1

		return chart



def      __lowerCamelCase     (      )			->      None:
		_a     :      Optional[int]        =							int(input('Enter the no. of variables\n'  )  )
		_a     :      List[Any]        =							[
		    float(lowerCAmelCase_  )
		    for x in input(
		        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'  ).split()
		]
		_a     :      List[str]        =							decimal_to_binary(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      Dict        =							check(lowerCAmelCase_  )
		print('Prime Implicants are:'  )
		print(lowerCAmelCase_  )
		_a     :      List[Any]        =							prime_implicant_chart(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      int        =							selection(lowerCAmelCase_						,			lowerCAmelCase_  )
		print('Essential Prime Implicants are:'  )
		print(lowerCAmelCase_  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




