

from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Any						)		->				Union[str, Any]:
 '''simple docstring'''


 for param, grad_param in zip(model_a.parameters()		,				model_b.parameters()						):
  if not param.requires_grad:
   continue
  if not did_step:
   # Grads should not be in sync
   assert (
       torch.allclose(param.grad		,				grad_param.grad						) is False
   ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
  else:
   # Grads should be in sync
   assert (
       torch.allclose(param.grad		,				grad_param.grad						) is True
   ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]=True						)		->				Tuple:
 '''simple docstring'''


 model.train()
 SCREAMING_SNAKE_CASE		    =      model(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      F.mse_loss(_UpperCamelCase		,				target.to(output.device						)						)
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Dict=False						)		->				Union[str, Any]:
 '''simple docstring'''


 set_seed(42						)
 SCREAMING_SNAKE_CASE		    =      RegressionModel()
 SCREAMING_SNAKE_CASE		    =      deepcopy(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      RegressionDataset(length=80						)
 SCREAMING_SNAKE_CASE		    =      DataLoader(_UpperCamelCase		,				batch_size=16						)
 model.to(accelerator.device						)
 if sched:
  SCREAMING_SNAKE_CASE		    =      AdamW(params=model.parameters()		,				lr=1e-3						)
  SCREAMING_SNAKE_CASE		    =      AdamW(params=ddp_model.parameters()		,				lr=1e-3						)
  SCREAMING_SNAKE_CASE		    =      LambdaLR(_UpperCamelCase		,				lr_lambda=lambda _UpperCamelCase						: epoch**0.65						)
  SCREAMING_SNAKE_CASE		    =      LambdaLR(_UpperCamelCase		,				lr_lambda=lambda _UpperCamelCase						: epoch**0.65						)
 # Make a copy of `model`
 if sched:
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.prepare(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.prepare(_UpperCamelCase		,				_UpperCamelCase						)
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_training_setup(_UpperCamelCase						)
 # Use a single batch
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      next(iter(_UpperCamelCase						)						).values()
 for iteration in range(3						):
  # Gather the distributed inputs and targs for the base model
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.gather((ddp_input, ddp_target)						)
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      input.to(accelerator.device						), target.to(accelerator.device						)
  # Perform our initial ground truth step in non "DDP"
  step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(_UpperCamelCase						):
    step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  else:
   # Sync grads
   step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  for param, ddp_param in zip(model.parameters()		,				ddp_model.parameters()						):
   if not param.requires_grad:
    continue
   assert torch.allclose(
       param.grad		,				ddp_param.grad						), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

  # Shuffle ddp_input on each iteration
  torch.manual_seed(13_37 + iteration						)
  SCREAMING_SNAKE_CASE		    =      ddp_input[torch.randperm(len(_UpperCamelCase						)						)]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_training_setup(_UpperCamelCase						)
 # Use a single batch
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      next(iter(_UpperCamelCase						)						).values()
 for iteration in range(3						):
  # Gather the distributed inputs and targs for the base model
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.gather((ddp_input, ddp_target)						)
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      input.to(accelerator.device						), target.to(accelerator.device						)
  # Perform our initial ground truth step in non "DDP"
  step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  # Do "gradient accumulation" (noop)
  if iteration % 2 == 0:
   # Accumulate grads locally
   with accelerator.no_sync(_UpperCamelCase						):
    step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  else:
   # Sync grads
   step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()		,				ddp_model.parameters()						):
   if not param.requires_grad:
    continue
   if iteration % 2 == 0:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad		,				ddp_param.grad						) is False
    ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
   else:
    # Grads should be in sync
    assert (
        torch.allclose(param.grad		,				ddp_param.grad						) is True
    ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
  torch.manual_seed(13_37 + iteration						)
  SCREAMING_SNAKE_CASE		    =      ddp_input[torch.randperm(len(_UpperCamelCase						)						)]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]=False		,				_UpperCamelCase		:	Optional[int]=False						)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      Accelerator(
     split_batches=_UpperCamelCase		,				dispatch_batches=_UpperCamelCase		,				gradient_accumulation_steps=2						)
 # Test that context manager behaves properly
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_training_setup(_UpperCamelCase						)
 for iteration, batch in enumerate(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      batch.values()
  # Gather the distributed inputs and targs for the base model
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.gather((ddp_input, ddp_target)						)
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      input.to(accelerator.device						), target.to(accelerator.device						)
  # Perform our initial ground truth step in non "DDP"
  step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  # Do "gradient accumulation" (noop)
  with accelerator.accumulate(_UpperCamelCase						):
   step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)

  # DDP model and model should only be in sync when not (iteration % 2 == 0)
  for param, ddp_param in zip(model.parameters()		,				ddp_model.parameters()						):
   if not param.requires_grad:
    continue
   if ((iteration + 1) % 2 == 0) or (iteration == len(_UpperCamelCase						) - 1):
    # Grads should be in sync
    assert (
        torch.allclose(param.grad		,				ddp_param.grad						) is True
    ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
   else:
    # Grads should not be in sync
    assert (
        torch.allclose(param.grad		,				ddp_param.grad						) is False
    ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
  torch.manual_seed(13_37 + iteration						)
  SCREAMING_SNAKE_CASE		    =      ddp_input[torch.randperm(len(_UpperCamelCase						)						)]
 GradientState._reset_state()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]=False		,				_UpperCamelCase		:	Union[str, Any]=False						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      Accelerator(
     split_batches=_UpperCamelCase		,				dispatch_batches=_UpperCamelCase		,				gradient_accumulation_steps=2						)
 # Test that context manager behaves properly
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_training_setup(_UpperCamelCase		,				_UpperCamelCase						)
 for iteration, batch in enumerate(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      batch.values()
  # Gather the distributed inputs and targs for the base model
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.gather((ddp_input, ddp_target)						)
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      input.to(accelerator.device						), target.to(accelerator.device						)
  # Perform our initial ground truth step in non "DDP"
  model.train()
  ddp_model.train()
  step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  opt.step()

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_UpperCamelCase						)):
   if split_batches:
    sched.step()
   else:
    for _ in range(accelerator.num_processes						):
     sched.step()
  opt.zero_grad()
  # Perform gradient accumulation under wrapper
  with accelerator.accumulate(_UpperCamelCase						):
   step_model(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   ddp_opt.step()
   ddp_sched.step()
   ddp_opt.zero_grad()

  # Learning rates should be the same
  assert (
      opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
  ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
  SCREAMING_SNAKE_CASE		    =      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_UpperCamelCase						))
  if accelerator.num_processes > 1:
   check_model_parameters(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
  # Shuffle ddp_input on each iteration
  torch.manual_seed(13_37 + iteration						)
 GradientState._reset_state()


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      Accelerator()

 SCREAMING_SNAKE_CASE		    =      RegressionDataset(length=80						)
 SCREAMING_SNAKE_CASE		    =      DataLoader(_UpperCamelCase		,				batch_size=16						)
 SCREAMING_SNAKE_CASE		    =      RegressionDataset(length=96						)
 SCREAMING_SNAKE_CASE		    =      DataLoader(_UpperCamelCase		,				batch_size=16						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.prepare(_UpperCamelCase		,				_UpperCamelCase						)
 assert accelerator.gradient_state.active_dataloader is None
 for iteration, _ in enumerate(_UpperCamelCase						):
  assert id(accelerator.gradient_state.active_dataloader						) == id(_UpperCamelCase						)
  if iteration < len(_UpperCamelCase						) - 1:
   assert not accelerator.gradient_state.end_of_dataloader
   if iteration == 1:
    for batch_num, _ in enumerate(_UpperCamelCase						):
     assert id(accelerator.gradient_state.active_dataloader						) == id(_UpperCamelCase						)
     if batch_num < len(_UpperCamelCase						) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
     else:
      assert accelerator.gradient_state.end_of_dataloader
  else:
   assert accelerator.gradient_state.end_of_dataloader
 assert accelerator.gradient_state.active_dataloader is None


def 							__lowerCAmelCase     (							)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      Accelerator()
 SCREAMING_SNAKE_CASE		    =      accelerator.state
 if state.local_process_index == 0:
  print('**Test `accumulate` gradient accumulation with dataloader break**'						)
 test_dataloader_break()
 if state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print('**Test NOOP `no_sync` context manager**'						)
  test_noop_sync(_UpperCamelCase						)
 if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
  if state.local_process_index == 0:
   print('**Test Distributed `no_sync` context manager**'						)
  test_distributed_sync(_UpperCamelCase						)
 if state.distributed_type == DistributedType.MULTI_GPU:
  for split_batch in [True, False]:
   for dispatch_batches in [True, False]:
    if state.local_process_index == 0:
     print(
         '**Test `accumulate` gradient accumulation, '		,				f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""		,				)
    test_gradient_accumulation(_UpperCamelCase		,				_UpperCamelCase						)

    # Currently will break on torch 2.0 +, need to investigate why
 if is_torch_version('<'		,				'2.0'						) or state.distributed_type == DistributedType.NO:
  if state.local_process_index == 0:
   print(
       '**Test `accumulate` gradient accumulation with optimizer and scheduler, '		,				'`split_batches=False`, `dispatch_batches=False`**'		,				)
  test_gradient_accumulation_with_opt_and_scheduler()
  if state.distributed_type == DistributedType.MULTI_GPU:
   for split_batch in [True, False]:
    for dispatch_batches in [True, False]:
     if not split_batch and not dispatch_batches:
      continue
     if state.local_process_index == 0:
      print(
          '**Test `accumulate` gradient accumulation with optimizer and scheduler, '		,				f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""		,				)
     test_gradient_accumulation_with_opt_and_scheduler(_UpperCamelCase		,				_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				Optional[Any]:
 '''simple docstring'''


 main()


if __name__ == "__main__":
      main()




import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

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

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

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

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

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

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

