'''simple docstring'''
import argparse
import os
import shutil

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







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



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

    __UpperCamelCase	:	Union[str, Any]		    =  args.model_name_or_path.rstrip('/'			)
    __UpperCamelCase	:	Union[str, Any]		    =  args.target_model_path

    print(f"""Load fine-pruned model from {model_name_or_path}"""			)
    __UpperCamelCase	:	int		    =  torch.load(os.path.join(_a				,     'pytorch_model.bin'			)			)
    __UpperCamelCase	:	Tuple		    =  {}

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

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

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

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


if __name__ == "__main__":
  a=  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=  parser.parse_args()

  main(args)




'''simple docstring'''
a=  '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
  from .utils import rich



