


'''simple docstring'''


import argparse
import os
import shutil

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






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Dict								=      args.pruning_method
		_UpperCAmelCase		:       List[Any]								=      args.threshold

		_UpperCAmelCase		:       List[str]								=      args.model_name_or_path.rstrip("/"   )
		_UpperCAmelCase		:       str								=      args.target_model_path

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

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


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase__    	=     abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_addoption_shared

		pytest_addoption_shared(__lowerCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_terminal_summary_main

		_UpperCAmelCase		:       Optional[int]								=      terminalreporter.config.getoption("--make-reports"   )
		if make_reports:
				pytest_terminal_summary_main(__lowerCAmelCase      , id=__lowerCAmelCase   )



