


import argparse
import os
import shutil

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

def 	lowerCAmelCase__			(      a__:		List[str]	)  ->		int:


							'''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(__a      ,  '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=__a      ,  threshold=__a	)
																												_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(__a      ,  __a	)
																												_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(__a      ,  __a      ,  __a	)
																												_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					=      -0.1, 1.1
																												_UpperCAmelCase					=      torch.sigmoid(__a	)
																												_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(__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__  :Dict             =							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__  :str             =							parser.parse_args()

		main(args)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
