
'''simple docstring'''


import argparse
import os
import shutil

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







def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					lowerCamelCase_					:      List[str]     =		args.pruning_method
					lowerCamelCase_					:      Tuple     =		args.threshold

					lowerCamelCase_					:      List[str]     =		args.model_name_or_path.rstrip('''/'''				)
					lowerCamelCase_					:      Optional[int]     =		args.target_model_path

					print(F"""Load fine-pruned model from {model_name_or_path}"""				)
					lowerCamelCase_					:      Optional[int]     =		torch.load(os.path.join(a_			,       '''pytorch_model.bin'''				)				)
					lowerCamelCase_					:      List[str]     =		{}

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

					__lowercase							:   Optional[int]					=     parser.parse_args()

					main(args)




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


