import argparse
import os
import shutil

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







def      _a					(   lowercase__	:      Optional[Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		args.pruning_method
     SCREAMING_SNAKE_CASE__					:					List[str]      =		args.threshold

     SCREAMING_SNAKE_CASE__					:					Tuple      =		args.model_name_or_path.rstrip('/'			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		args.target_model_path

     print(f'''Load fine-pruned model from {model_name_or_path}'''			)
     SCREAMING_SNAKE_CASE__					:					int      =		torch.load(os.path.join(SCREAMING_SNAKE_CASE_  ,	'pytorch_model.bin'			)			)
     SCREAMING_SNAKE_CASE__					:					Tuple      =		{}

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

     if target_model_path is None:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		os.path.join(
              os.path.dirname(SCREAMING_SNAKE_CASE_			)  ,	f'''bertarized_{os.path.basename(SCREAMING_SNAKE_CASE_			)}'''			)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       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",
      )

      SCREAMING_SNAKE_CASE__		:       Any       						=       parser.parse_args()

      main(args)





def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
		print(F'{solution() = }')