

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[Any]						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    args.serialization_dir
	_UpperCamelCase :		Tuple								=    args.pruning_method
	_UpperCamelCase :		List[str]								=    args.threshold

	_UpperCamelCase :		Union[str, Any]								=    torch.load(os.path.join(UpperCAmelCase__				,							'pytorch_model.bin'						)				,							map_location='cpu'						)

	_UpperCamelCase :		Dict								=    0  # Number of remaining (not pruned) params in the encoder
	_UpperCamelCase :		Optional[int]								=    0  # Number of params in the encoder

	print('name'.ljust(6_0				,							' '						)				,							'Remaining Weights %'				,							'Remaining Weight'						)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				_UpperCamelCase :		Any								=    TopKBinarizer.apply(UpperCAmelCase__				,							UpperCAmelCase__						).sum().item()
			elif pruning_method == "sigmoied_threshold":
				_UpperCamelCase :		str								=    ThresholdBinarizer.apply(UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__						).sum().item()
			elif pruning_method == "l0":
				_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    -0.1, 1.1
				_UpperCamelCase :		Tuple								=    torch.sigmoid(UpperCAmelCase__						)
				_UpperCamelCase :		List[str]								=    s * (r - l) + l
				_UpperCamelCase :		Optional[Any]								=    s_bar.clamp(min=0.0				,							max=1.0						)
				_UpperCamelCase :		Optional[Any]								=    (mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'						)
			remaining_count += mask_ones
			print(name.ljust(6_0				,							' '						)				,							str(round(1_0_0 * mask_ones / param.numel()				,							3						)						).ljust(2_0				,							' '						)				,							str(UpperCAmelCase__						)						)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''						)
	print('Remaining Weights (global) %: '				,							1_0_0 * remaining_count / encoder_count						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()

							parser.add_argument(
							    """--pruning_method""",
							    choices=["""l0""", """topK""", """sigmoied_threshold"""],
							    type=str,
							    required=True,
							    help=(
							        """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
							        """ pruning)"""
							    ),
							)
							parser.add_argument(
							    """--threshold""",
							    type=float,
							    required=False,
							    help=(
							        """For `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(
							    """--serialization_dir""",
							    type=str,
							    required=True,
							    help="""Folder containing the model that was previously fine-pruned""",
							)

							lowerCAmelCase__             =		parser.parse_args()

							main(args)







# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if name != "training_function" and name != "main":
										raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""   )
					__lowerCAmelCase,						__lowerCAmelCase       =				[], False
					for line in lines:
										if not found_start and F"""def {name}""" in line:
															__lowerCAmelCase       =				True
															good_lines.append(UpperCAmelCase__   )
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					return [line for line in lines if not line.lstrip().startswith('#'   ) and line != "\n"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__ = None   ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(os.path.abspath(os.path.join('examples'		,       'nlp_example.py'   )   )		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					if secondary_filename is not None:
										with open(UpperCAmelCase__		,       'r'   ) as f:
															__lowerCAmelCase       =				f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
					else:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(
															    get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )

					__lowerCAmelCase       =				'train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'

					# Specific code in our script that differs from the full version, aka what is new
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					__lowerCAmelCase       =				iter(UpperCAmelCase__   )
					for i in range(len(UpperCAmelCase__   ) - 1   ):
										if i not in passed_idxs:
															__lowerCAmelCase       =				next(UpperCAmelCase__   )
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									__lowerCAmelCase       =				next(UpperCAmelCase__   )

    # Extract out just the new parts from the full_file_training_func
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(UpperCAmelCase__   ):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )

    # Finally, get the overall diff
					__lowerCAmelCase       =				[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										__lowerCAmelCase       =				[line for line in full_file_contents if line not in secondary_file_func]
										__lowerCAmelCase       =				[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example






