



'''simple docstring'''
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    Adafactor,
					    AdamW,
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_inverse_sqrt_schedule,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					)


def      lowercase_		(      lowerCAmelCase__		:     List[Any]	,							lowerCAmelCase__		:     List[str]=10       ):

			"""simple docstring"""





			__UpperCAmelCase      :					List[Any]   =   []
			for _ in range(lowercase_       ):
						lrs.append(scheduler.get_lr()[0]       )
						scheduler.step()
			return lrs


def      lowercase_		(      lowerCAmelCase__		:     str	,							lowerCAmelCase__		:     Any=10       ):

			"""simple docstring"""





			__UpperCAmelCase      :					List[str]   =   []
			for step in range(lowercase_       ):
						lrs.append(scheduler.get_lr()[0]       )
						scheduler.step()
						if step == num_steps // 2:
									with tempfile.TemporaryDirectory() as tmpdirname:
												__UpperCAmelCase      :					Any   =   os.path.join(lowercase_	,							"""schedule.bin"""       )
												torch.save(scheduler.state_dict()	,							lowercase_       )

												__UpperCAmelCase      :					List[Any]   =   torch.load(lowercase_       )
												scheduler.load_state_dict(lowercase_       )
			return lrs



@require_torch
class _A     (			unittest.TestCase			):





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   Optional[int]:

					'''simple docstring'''
					self.assertEqual(len(UpperCAmelCase__ )       ,				len(UpperCAmelCase__ ) )
					for a, b in zip(UpperCAmelCase__       ,				UpperCAmelCase__ ):
								self.assertAlmostEqual(UpperCAmelCase__       ,				UpperCAmelCase__       ,				delta=UpperCAmelCase__ )





		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=UpperCAmelCase__ )
					__UpperCAmelCase      :					Union[str, Any]   =   torch.tensor([0.4, 0.2, -0.5] )
					__UpperCAmelCase      :					int   =   nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__UpperCAmelCase      :					Any   =   AdamW(params=[w]       ,				lr=2E-1       ,				weight_decay=0.0 )
					for _ in range(100 ):
								__UpperCAmelCase      :					List[Any]   =   criterion(UpperCAmelCase__       ,				UpperCAmelCase__ )
								loss.backward()
								optimizer.step()
								w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
								w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()       ,				[0.4, 0.2, -0.5]       ,				tol=1E-2 )





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=UpperCAmelCase__ )
					__UpperCAmelCase      :					int   =   torch.tensor([0.4, 0.2, -0.5] )
					__UpperCAmelCase      :					int   =   nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__UpperCAmelCase      :					List[str]   =   Adafactor(
					    params=[w]       ,				lr=1E-2       ,				eps=(1E-30, 1E-3)       ,				clip_threshold=1.0       ,				decay_rate=-0.8       ,				betaa=UpperCAmelCase__       ,				weight_decay=0.0       ,				relative_step=UpperCAmelCase__       ,				scale_parameter=UpperCAmelCase__       ,				warmup_init=UpperCAmelCase__       ,				)
					for _ in range(1_000 ):
								__UpperCAmelCase      :					int   =   criterion(UpperCAmelCase__       ,				UpperCAmelCase__ )
								loss.backward()
								optimizer.step()
								w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
								w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()       ,				[0.4, 0.2, -0.5]       ,				tol=1E-2 )



@require_torch
class _A     (			unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			List[Any]  					=	nn.Linear(50		, 50			) if is_torch_available() else None
		_SCREAMING_SNAKE_CASE    :			Tuple  					=	AdamW(m.parameters()		, lr=10.0			) if is_torch_available() else None
		_SCREAMING_SNAKE_CASE    :			List[Any]  					=	10





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase=None )				->   Any:

					'''simple docstring'''
					self.assertEqual(len(UpperCAmelCase__ )       ,				len(UpperCAmelCase__ ) )
					for a, b in zip(UpperCAmelCase__       ,				UpperCAmelCase__ ):
								self.assertAlmostEqual(UpperCAmelCase__       ,				UpperCAmelCase__       ,				delta=UpperCAmelCase__       ,				msg=UpperCAmelCase__ )





		def 							__A				(   self )				->   Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   {"""num_warmup_steps""": 2, """num_training_steps""": 10}
					# schedulers doct format
					# function: (sched_args_dict, expected_learning_rates)
					__UpperCAmelCase      :					List[str]   =   {
					    get_constant_schedule: ({}, [10.0] * self.num_steps),
					    get_constant_schedule_with_warmup: (
					        {"""num_warmup_steps""": 4},
					        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
					    ),
					    get_linear_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
					    ),
					    get_cosine_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
					    ),
					    get_cosine_with_hard_restarts_schedule_with_warmup: (
					        {**common_kwargs, """num_cycles""": 2},
					        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
					    ),
					    get_polynomial_decay_schedule_with_warmup: (
					        {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
					        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
					    ),
					    get_inverse_sqrt_schedule: (
					        {"""num_warmup_steps""": 2},
					        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
					    ),
					}

					for scheduler_func, data in scheds.items():
								__UpperCAmelCase ,     __UpperCAmelCase      :					str   =   data

								__UpperCAmelCase      :					Union[str, Any]   =   scheduler_func(self.optimizer       ,				**UpperCAmelCase__ )
								self.assertEqual(len([scheduler.get_lr()[0]] )       ,				1 )
								__UpperCAmelCase      :					List[str]   =   unwrap_schedule(UpperCAmelCase__       ,				self.num_steps )
								self.assertListAlmostEqual(
								    UpperCAmelCase__       ,				UpperCAmelCase__       ,				tol=1E-2       ,				msg=f'failed for {scheduler_func} in normal scheduler'       ,				)

								__UpperCAmelCase      :					Any   =   scheduler_func(self.optimizer       ,				**UpperCAmelCase__ )
								if scheduler_func.__name__ != "get_constant_schedule":
											LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase__ )  # wrap to test picklability of the schedule
								__UpperCAmelCase      :					List[str]   =   unwrap_and_save_reload_schedule(UpperCAmelCase__       ,				self.num_steps )
								self.assertListEqual(UpperCAmelCase__       ,				UpperCAmelCase__       ,				msg=f'failed for {scheduler_func} in save and reload' )



class _A     :





		def __init__(   self       ,				__UpperCAmelCase )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   fn





		def __call__(   self       ,				*__UpperCAmelCase       ,				**__UpperCAmelCase )				->   List[str]:

					'''simple docstring'''
					return self.fn(*UpperCAmelCase__       ,				**UpperCAmelCase__ )





		@classmethod
		def 							__A				(   self       ,				__UpperCAmelCase )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   list(map(self       ,				scheduler.lr_lambdas ) )










import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

       A__       =    args.model_name_or_path.rstrip('''/'''						)
       A__       =    args.target_model_path

       print(f"""Load fine-pruned model from {model_name_or_path}"""						)
       A__       =    torch.load(os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       A__       =    {}

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

       if target_model_path is None:
              A__       =    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__":
  _lowerCamelCase	:		Optional[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""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


