"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.generation import DisjunctiveConstraint



@require_torch
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													_snake_case						:					int    =   [[1, 2, 4], [1, 2, 3, 4]]
													_snake_case						:					int    =   DisjunctiveConstraint(__snake_case    )
													self.assertTrue(isinstance(dc.token_ids				,    __snake_case    )    )

													with self.assertRaises(__snake_case    ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]    )    )

													with self.assertRaises(__snake_case    ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]    ), torch.LongTensor([1, 2, 3, 4, 5]    )]    )



							def 	UpperCamelCase	(						self    ):
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													_snake_case						:					Optional[int]    =   [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__snake_case    ):
																			DisjunctiveConstraint(__snake_case    )  # fails here



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   [[1, 2, 3], [1, 2, 4]]

													_snake_case						:					Optional[Any]    =   DisjunctiveConstraint(__snake_case    )

													_snake_case    ,_snake_case    ,_snake_case						:					Dict    =   dc.update(1    )
													_snake_case						:					str    =   stepped is True and completed is False and reset is False
													self.assertTrue(__snake_case    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Optional[Any]    =   dc.update(2    )
													_snake_case						:					Union[str, Any]    =   stepped is True and completed is False and reset is False
													self.assertTrue(__snake_case    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					int    =   dc.update(3    )
													_snake_case						:					List[Any]    =   stepped is True and completed is True and reset is False
													self.assertTrue(__snake_case    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													_snake_case						:					Any    =   DisjunctiveConstraint(__snake_case    )

													_snake_case    ,_snake_case    ,_snake_case						:					Union[str, Any]    =   dc.update(1    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					List[Any]    =   dc.update(2    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Union[str, Any]    =   dc.update(4    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2, 4]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Any    =   dc.update(5    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]    )

													dc.reset()

													_snake_case    ,_snake_case    ,_snake_case						:					str    =   dc.update(1    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.remaining() == 3    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Union[str, Any]    =   dc.update(2    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.remaining() == 2    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					str    =   dc.update(5    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.remaining() == 0    )
													self.assertTrue(dc.current_seq == [1, 2, 5]    )
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

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

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

						if not os.path.isdir(__lowercase						):
												shutil.copytree(__lowercase ,      __lowercase						)
												print(F"""\nCreated folder {target_model_path}"""						)

						torch.save(__lowercase ,      os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			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',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)