
"""simple docstring"""






import argparse
import os
import shutil

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






def 						_snake_case       (	lowercase__		:  Tuple		)  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  args.pruning_method
			lowerCAmelCase_    :Tuple			     =  args.threshold

			lowerCAmelCase_    :int			     =  args.model_name_or_path.rstrip("""/"""		)
			lowerCAmelCase_    :Dict			     =  args.target_model_path

			print(f"""Load fine-pruned model from {model_name_or_path}"""		)
			lowerCAmelCase_    :Optional[int]			     =  torch.load(os.path.join(lowercase__      ,     """pytorch_model.bin"""		)		)
			lowerCAmelCase_    :Union[str, Any]			     =  {}

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

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

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

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


if __name__ == "__main__":
				__UpperCAmelCase       =  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',
				)

				__UpperCAmelCase       =  parser.parse_args()

				main(args)







from math import isqrt, loga






def  snake_case  (       snake_case__			:int)						->   list[int]:
       _A        =  [True] * max_number
       for i in range(2   ,	isqrt(max_number - 1) + 1):
              if is_prime[i]:
                     for j in range(i**2   ,	snake_case__   ,	snake_case__):
                            _A        =  False

       return [i for i in range(2   ,	snake_case__) if is_prime[i]]






def  snake_case  (       snake_case__			:int = 800_800   ,	snake_case__			:int = 800_800)						->   int:
       _A        =  degree * loga(snake_case__)
       _A        =  int(snake_case__)
       _A        =  calculate_prime_numbers(snake_case__)

       _A        =  0
       _A        =  0
       _A        =  len(snake_case__) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left])
                  + prime_numbers[left] * loga(prime_numbers[right])
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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




