



'''simple docstring'''




import argparse
import os
import shutil

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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

			A_     :						str     =					args.model_name_or_path.rstrip("""/"""   )
			A_     :						Union[str, Any]     =					args.target_model_path

			print(f'Load fine-pruned model from {model_name_or_path}'   )
			A_     :						Dict     =					torch.load(os.path.join(lowerCamelCase__						,				"""pytorch_model.bin"""   )   )
			A_     :						int     =					{}

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

			if target_model_path is None:
						A_     :						str     =					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__":
						lowerCamelCase							:List[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''',
						)

						lowerCamelCase							:List[str]       = parser.parse_args()

						main(args)




'''simple docstring'''




def      a      (			):
			'''simple docstring'''





			A_     :						Optional[int]     =					[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

			A_     :						Dict     =					6
			A_     :						List[Any]     =					1
			A_     :						Optional[Any]     =					19_01

			A_     :						Tuple     =					0

			while year < 20_01:
						day += 7

						if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
									if day > days_per_month[month - 1] and month != 2:
												month += 1
												A_     :						Optional[Any]     =					day - days_per_month[month - 2]
									elif day > 29 and month == 2:
												month += 1
												A_     :						str     =					day - 29
						else:
									if day > days_per_month[month - 1]:
												month += 1
												A_     :						List[str]     =					day - days_per_month[month - 2]

						if month > 12:
									year += 1
									A_     :						Tuple     =					1

						if year < 20_01 and day == 1:
									sundays += 1
			return sundays


if __name__ == "__main__":
						print(solution())