
"""simple docstring"""
# 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      (_lowerCAmelCase				,  _lowerCAmelCase		):
						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(_lowerCAmelCase		)
																		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(_lowerCAmelCase		)

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



def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = None		):
						with open(_lowerCAmelCase				,  """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(_lowerCAmelCase				,  """r"""		) as f:
												__lowerCAmelCase        =       f.readlines()
						if secondary_filename is not None:
												with open(_lowerCAmelCase				,  """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(_lowerCAmelCase				,  """main"""		)		)
												__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """main"""		)		)
												__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """main"""		)		)
												if secondary_filename is not None:
																		__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """main"""		)		)
						else:
												__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """training_function"""		)		)
												__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """training_function"""		)		)
												__lowerCAmelCase        =       clean_lines(get_function_contents_by_name(_lowerCAmelCase				,  """training_function"""		)		)
												if secondary_filename is not None:
																		__lowerCAmelCase        =       clean_lines(
																		    get_function_contents_by_name(_lowerCAmelCase				,  """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(_lowerCAmelCase		)
						for i in range(len(_lowerCAmelCase		) - 1		):
												if i not in passed_idxs:
																		__lowerCAmelCase        =       next(_lowerCAmelCase		)
																		if (line not in full_file_func) and (line.lstrip() != _dl_line):
																								if "TESTING_MOCKED_DATALOADERS" not in line:
																														new_feature_code.append(_lowerCAmelCase		)
																														passed_idxs.append(_lowerCAmelCase		)
																								else:
																														# Skip over the `config['num_epochs'] = 2` statement
																														__lowerCAmelCase        =       next(_lowerCAmelCase		)

    # 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(_lowerCAmelCase		):
												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(_lowerCAmelCase		)
																														passed_idxs.append(_lowerCAmelCase		)

    # 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


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

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """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":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					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''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
