

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


a_     :    List[str]     		=   logging.get_logger(__name__)


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


 SCREAMING_SNAKE_CASE		    =      os.getenv('SM_HP_MP_PARAMETERS'		,				'{}'						)
 try:
  # Parse it and check the field "partitions" is included, it is required for model parallel.
  SCREAMING_SNAKE_CASE		    =      json.loads(_UpperCamelCase						)
  if "partitions" not in smp_options:
   return False
 except json.JSONDecodeError:
  return False

 # Get the sagemaker specific framework parameters from mpi_options variable.
 SCREAMING_SNAKE_CASE		    =      os.getenv('SM_FRAMEWORK_PARAMS'		,				'{}'						)
 try:
  # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
  SCREAMING_SNAKE_CASE		    =      json.loads(_UpperCamelCase						)
  if not mpi_options.get('sagemaker_mpi_enabled'		,				_UpperCamelCase						):
   return False
 except json.JSONDecodeError:
  return False
 # Lastly, check if the `smdistributed` module is present.
 return importlib.util.find_spec('smdistributed'						) is not None


if is_sagemaker_model_parallel_available():
      import smdistributed.modelparallel.torch as smp

      smp.init()




@dataclass
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =field(
        default=""						,      metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}						,      )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     super().__post_init__()
     warnings.warn(
         '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
         '`TrainingArguments` instead.'				,     snake_case__				,     )

    @cached_property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     logger.info('PyTorch: setting up devices'     )
     if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
      logger.warning(
          'torch.distributed process group is initialized, but local_rank == -1. '
          'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'     )
     if self.no_cuda:
      SCREAMING_SNAKE_CASE		    =      torch.device('cpu'     )
      SCREAMING_SNAKE_CASE		    =      0
     elif is_sagemaker_model_parallel_available():
      SCREAMING_SNAKE_CASE		    =      smp.local_rank()
      SCREAMING_SNAKE_CASE		    =      torch.device('cuda'				,     snake_case__     )
      SCREAMING_SNAKE_CASE		    =      1
     elif is_sagemaker_dp_enabled():
      import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

      torch.distributed.init_process_group(backend='smddp'				,     timeout=self.ddp_timeout_delta     )
      SCREAMING_SNAKE_CASE		    =      int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'     )     )
      SCREAMING_SNAKE_CASE		    =      torch.device('cuda'				,     self.local_rank     )
      SCREAMING_SNAKE_CASE		    =      1
     elif self.local_rank == -1:
      # if n_gpu is > 1 we'll use nn.DataParallel.
      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
      # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
      # trigger an error that a device index is missing. Index 0 takes into account the
      # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
      # will use the first GPU in that env, i.e. GPU#1
      SCREAMING_SNAKE_CASE		    =      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
      # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
      # the default value.
      SCREAMING_SNAKE_CASE		    =      torch.cuda.device_count()
     else:
      # Here, we'll use torch.distributed.
      # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
      if not torch.distributed.is_initialized():
       torch.distributed.init_process_group(backend='nccl'				,     timeout=self.ddp_timeout_delta     )
      SCREAMING_SNAKE_CASE		    =      torch.device('cuda'				,     self.local_rank     )
      SCREAMING_SNAKE_CASE		    =      1

     if device.type == "cuda":
      torch.cuda.set_device(snake_case__     )

     return device

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     if is_sagemaker_model_parallel_available():
      return smp.dp_size()

     return super().world_size

    @property
    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     return not is_sagemaker_model_parallel_available()




    @property
    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     return False




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)

