
import argparse
import os
import shutil

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




def 	A_ (							snake_case							:   Optional[Any] )      ->    Any:



						'''simple docstring'''

						__UpperCamelCase       =			args.pruning_method
						__UpperCamelCase       =			args.threshold

						__UpperCamelCase       =			args.model_name_or_path.rstrip('''/''' )
						__UpperCamelCase       =			args.target_model_path

						print(f"Load fine-pruned model from {model_name_or_path}" )
						__UpperCamelCase       =			torch.load(os.path.join(_snake_case ,      '''pytorch_model.bin''' ) )
						__UpperCamelCase       =			{}

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

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

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

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


if __name__ == "__main__":
				lowercase__							:							Tuple									=			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",
				)

				lowercase__							:							Optional[Any]									=			parser.parse_args()

				main(args)






from collections import Counter
from timeit import timeit




def 	A_ (							snake_case							:   str = "" ,      )      ->    bool:



						'''simple docstring'''

						return sum(c % 2 for c in Counter(input_str.replace(''' ''' ,      '''''' ).lower() ).values() ) < 2




def 	A_ (							snake_case							:   str = "" )      ->    bool:



						'''simple docstring'''

						if len(snake_case ) == 0:
												return True
						__UpperCamelCase       =			input_str.replace(''' ''' ,      '''''' ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						__UpperCamelCase       =			{}

						for character in lower_case_input_str:
												__UpperCamelCase       =			character_freq_dict.get(snake_case ,      0 ) + 1
						__UpperCamelCase       =			0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True




def 	A_ (							snake_case							:   str = "" )      ->    None:



						'''simple docstring'''

						print('''\nFor string = ''' ,      snake_case ,      ''':''' )
						print(
						    '''> can_string_be_rearranged_as_palindrome_counter()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome_counter(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )
						print(
						    '''> can_string_be_rearranged_as_palindrome()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			input(
				    "Enter string to determine if it can be rearranged as a palindrome or not: "
				).strip()
				benchmark(check_str)
				lowercase__							:							Dict									=			can_string_be_rearranged_as_palindrome_counter(check_str)
				print(F"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")




