






"""simple docstring"""
import argparse
import os
import shutil

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

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Any     	=   args.pruning_method
			UpperCAmelCase_	:					List[str]     	=   args.threshold

			UpperCAmelCase_	:					Dict     	=   args.model_name_or_path.rstrip("/"		)
			UpperCAmelCase_	:					Optional[Any]     	=   args.target_model_path

			print(F"""Load fine-pruned model from {model_name_or_path}"""		)
			UpperCAmelCase_	:					Any     	=   torch.load(os.path.join(A__      ,"pytorch_model.bin"		)		)
			UpperCAmelCase_	:					List[str]     	=   {}

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

			if target_model_path is None:
						UpperCAmelCase_	:					Tuple     	=   os.path.join(
						    os.path.dirname(A__		)      ,F"""bertarized_{os.path.basename(A__		)}"""		)

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

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


if __name__ == "__main__":
			lowerCamelCase_							      = 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_							      = parser.parse_args()

			main(args)











"""simple docstring"""
def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   [0 for i in range(len(A__		)		)]

			# initialize interval's left pointer and right pointer
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   0, 0

			for i in range(1      ,len(A__		)		):
						# case when current index is inside the interval
						if i <= right_pointer:
									UpperCAmelCase_	:					Any     	=   min(right_pointer - i + 1      ,z_result[i - left_pointer]		)
									UpperCAmelCase_	:					Union[str, Any]     	=   min_edge

						while go_next(A__      ,A__      ,A__		):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   i, i + z_result[i] - 1

			return z_result

def 							snake_case (  A__      ,A__      ,A__		):
			return i + z_result[i] < len(A__		) and s[z_result[i]] == s[i + z_result[i]]

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			UpperCAmelCase_	:					Tuple     	=   z_function(pattern + input_str		)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(A__		):
									answer += 1

			return answer


if __name__ == "__main__":
			import doctest

			doctest.testmod()



