




'''simple docstring'''


from __future__ import annotations

from typing import Any




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      float = 0  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  row, column
								snake_case__						:       Dict       						=  [[default_value for c in range(snake_case_  )] for r in range(snake_case_  )]







						def __str__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

								# Make string identifier
								snake_case__						:       List[str]       						=  0
								for row_vector in self.array:
										for obj in row_vector:
												snake_case__						:       str       						=  max(snake_case_			,			len(str(snake_case_  )  )  )
								snake_case__						:       Dict       						=  F"""%{max_element_length}s"""

								# Make string and return
								def single_line(snake_case_					:      list[float]  ) -> str:
										nonlocal string_format_identifier
										snake_case__						:       str       						=  '''['''
										line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
										line += "]"
										return line

								s += "\n".join(single_line(snake_case_  ) for row_vector in self.array  )
								return s







						def __repr__(							self					:      Optional[int]  ):



								'''simple docstring'''



								return str(self  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								if not (isinstance(snake_case_			,			(list, tuple)  ) and len(snake_case_  ) == 2):
										return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
										return False
								else:
										return True







						def __getitem__(							self					:      List[str]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								return self.array[loc[0]][loc[1]]







						def __setitem__(							self					:      int			,			snake_case_					:      tuple[int, int]			,			snake_case_					:      float  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								snake_case__						:       Optional[Any]       						=  value







						def __add__(							self					:      Dict			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  )
								assert self.row == another.row and self.column == another.column

								# Add
								snake_case__						:       str       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       Optional[Any]       						=  self[r, c] + another[r, c]
								return result







						def __neg__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       str       						=  -self[r, c]
								return result







						def __sub__(							self					:      Optional[int]			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								return self + (-another)







						def __mul__(							self					:      int			,			snake_case_					:      int | float | Matrix  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			(int, float)  ):  # Scalar multiplication
										snake_case__						:       Dict       						=  Matrix(self.row			,			self.column  )
										for r in range(self.row  ):
												for c in range(self.column  ):
														snake_case__						:       Tuple       						=  self[r, c] * another
										return result
								elif isinstance(snake_case_			,			snake_case_  ):  # Matrix multiplication
										assert self.column == another.row
										snake_case__						:       List[Any]       						=  Matrix(self.row			,			another.column  )
										for r in range(self.row  ):
												for c in range(another.column  ):
														for i in range(self.column  ):
																result[r, c] += self[r, i] * another[i, c]
										return result
								else:
										snake_case__						:       Union[str, Any]       						=  F"""Unsupported type given for another ({type(snake_case_  )})"""
										raise TypeError(snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  Matrix(self.column			,			self.row  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       List[Any]       						=  self[r, c]
								return result







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Matrix			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  ) and isinstance(snake_case_			,			snake_case_  )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								snake_case__						:       Tuple       						=  v.transpose()
								snake_case__						:       Optional[Any]       						=  (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
										return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




					def 	_a       (				):

							"""simple docstring"""

							snake_case__						:       Tuple       						=  Matrix(3   ,							3   ,							0     )
							for i in range(3     ):
									snake_case__						:       Any       						=  1
							print(F"""a^(-1) is {ainv}"""     )
							# u, v
							snake_case__						:       int       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						:       List[Any]       						=  1, 2, -3
							snake_case__						:       Any       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						:       Optional[int]       						=  4, -2, 5
							print(F"""u is {u}"""     )
							print(F"""v is {v}"""     )
							print(F"""uv^T is {u * v.transpose()}"""     )
							# Sherman Morrison
							print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase   ,							__lowerCAmelCase     )}"""     )




					def 	_a       (				):

							"""simple docstring"""

							import doctest

							doctest.testmod()





					testa()






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

		print(F"""Load fine-pruned model from {model_name_or_path}"""     )
		snake_case__						:       Dict       						=  torch.load(os.path.join(__lowerCAmelCase   ,							'''pytorch_model.bin'''     )     )
		snake_case__						:       Any       						=  {}

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

		if target_model_path is None:
				snake_case__						:       Optional[int]       						=  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__						:       str										=						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__						:       Optional[Any]										=						parser.parse_args()

					main(args)



