





"""simple docstring"""





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def  __A   (_SCREAMING_SNAKE_CASE			)					->int:





  """simple docstring"""


  return 1.0 / (1.0 + np.exp(-_outputs			))
def  __A   (_SCREAMING_SNAKE_CASE			)					->Tuple:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							np.max(_outputs     ,   axis=-1     ,   keepdims=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							np.exp(_outputs - maxes			)
  return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_SCREAMING_SNAKE_CASE			)






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :Any		  =					"""sigmoid"""
       __magic_name__     :Optional[Any]		  =					"""softmax"""
       __magic_name__     :Optional[Any]		  =					"""none"""






@add_end_docstrings(
    a  ,   r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """  ,   )
class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :Union[str, Any]		  =					False
       __magic_name__     :Dict		  =					ClassificationFunction.NONE






       def __init__(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase      )

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == 'tf'
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )






       def      snake_case						(    self    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , __UpperCAmelCase=""    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							tokenizer_kwargs

         lowerCAmelCase__			:List[Any]											=							{}
         if hasattr(self.model.config    , 'return_all_scores'      ) and return_all_scores is None:
           lowerCAmelCase__			:List[Any]											=							self.model.config.return_all_scores

         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) or top_k is None:
           lowerCAmelCase__			:int											=							top_k
           lowerCAmelCase__			:Dict											=							False
         elif return_all_scores is not None:
           warnings.warn(
               '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
               ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'    , __UpperCAmelCase    , )
           if return_all_scores:
             lowerCAmelCase__			:List[Any]											=							None
           else:
             lowerCAmelCase__			:Union[str, Any]											=							1

         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Union[str, Any]											=							ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
           lowerCAmelCase__			:List[Any]											=							function_to_apply
         return preprocess_params, {}, postprocess_params






       def __call__(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							super().__call__(*__UpperCAmelCase    , **__UpperCAmelCase      )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         lowerCAmelCase__			:Optional[Any]											=							'top_k' not in kwargs
         if isinstance(args[0]    , __UpperCAmelCase      ) and _legacy:
           # This pipeline is odd, and return a list when single item is run
           return [result]
         else:
           return result






       def      snake_case						(    self    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							self.framework
         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           return self.tokenizer(**__UpperCAmelCase    , return_tensors=__UpperCAmelCase    , **__UpperCAmelCase      )
         elif isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) and len(__UpperCAmelCase      ) == 1 and isinstance(inputs[0]    , __UpperCAmelCase      ) and len(inputs[0]      ) == 2:
           # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
           return self.tokenizer(
               text=inputs[0][0]    , text_pair=inputs[0][1]    , return_tensors=__UpperCAmelCase    , **__UpperCAmelCase      )
         elif isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           # This is likely an invalid usage of the pipeline attempting to pass text pairs.
           raise ValueError(
               'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
               ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'      )
         return self.tokenizer(__UpperCAmelCase    , return_tensors=__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return self.model(**__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=1    , __UpperCAmelCase=True      ):



         '''simple docstring'''

         if function_to_apply is None:
           if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
             lowerCAmelCase__			:str											=							ClassificationFunction.SIGMOID
           elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
             lowerCAmelCase__			:int											=							ClassificationFunction.SOFTMAX
           elif hasattr(self.model.config    , 'function_to_apply'      ) and function_to_apply is None:
             lowerCAmelCase__			:Optional[Any]											=							self.model.config.function_to_apply
           else:
             lowerCAmelCase__			:Dict											=							ClassificationFunction.NONE

         lowerCAmelCase__			:int											=							model_outputs['logits'][0]
         lowerCAmelCase__			:Union[str, Any]											=							outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
           lowerCAmelCase__			:Dict											=							sigmoid(__UpperCAmelCase      )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
           lowerCAmelCase__			:int											=							softmax(__UpperCAmelCase      )
         elif function_to_apply == ClassificationFunction.NONE:
           lowerCAmelCase__			:Tuple											=							outputs
         else:
           raise ValueError(F"Unrecognized `function_to_apply` argument: {function_to_apply}"      )

         if top_k == 1 and _legacy:
           return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         lowerCAmelCase__			:Any											=							[
             {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(__UpperCAmelCase      )
         ]
         if not _legacy:
           dict_scores.sort(key=lambda __UpperCAmelCase      : x["score"]    , reverse=__UpperCAmelCase      )
           if top_k is not None:
             lowerCAmelCase__			:List[str]											=							dict_scores[:top_k]
         return dict_scores







"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__A											=		logging.getLogger(__name__)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  if os.path.exists(_SCREAMING_SNAKE_CASE			):
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			)
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			)
  else:
    os.makedirs(_SCREAMING_SNAKE_CASE			)
  model.save_pretrained(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=False			)					->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__			:Dict											=							2
  if unlogit:
    lowerCAmelCase__			:List[str]											=							torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:str											=							p * torch.log(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							0
  return -plogp.sum(dim=-1			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  logger.info('lv, h >\t' + '\t'.join(F"{x + 1}" for x in range(len(_SCREAMING_SNAKE_CASE			)			)			)			)
  for row in range(len(_SCREAMING_SNAKE_CASE			)			):
    if tensor.dtype != torch.long:
      logger.info(F"layer {row + 1}:\t" + '\t'.join(F"{x:.5f}" for x in tensor[row].cpu().data			)			)
    else:
      logger.info(F"layer {row + 1}:\t" + '\t'.join(F"{x:d}" for x in tensor[row].cpu().data			)			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=None     ,   _SCREAMING_SNAKE_CASE=False			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							model.config.num_hidden_layers, model.config.num_attention_heads
  lowerCAmelCase__			:Any											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)
  lowerCAmelCase__			:Tuple											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  if head_mask is None:
    lowerCAmelCase__			:Optional[int]											=							torch.ones(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  head_mask.requires_grad_(requires_grad=_SCREAMING_SNAKE_CASE			)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    lowerCAmelCase__			:List[str]											=							None

  lowerCAmelCase__			:Any											=							0.0
  lowerCAmelCase__			:Any											=							0.0
  for step, inputs in enumerate(tqdm(_SCREAMING_SNAKE_CASE     ,   desc='Iteration'     ,   disable=args.local_rank not in [-1, 0]			)			):
    lowerCAmelCase__			:str											=							tuple(t.to(args.device			) for t in inputs			)
    ((lowerCAmelCase__)							,				)			:Dict											=							inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    lowerCAmelCase__			:str											=							model(_SCREAMING_SNAKE_CASE     ,   labels=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							(
        outputs[0],
        outputs[1],
        outputs[-1],
    )  # Loss and logits are the first, attention the last
    loss.backward()  # Backpropagate to populate the gradients in the head mask
    total_loss += loss.detach().cpu().numpy()
    if compute_entropy:
      for layer, attn in enumerate(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:Optional[Any]											=							entropy(attn.detach()     ,   _SCREAMING_SNAKE_CASE			)
        attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

    if compute_importance:
      head_importance += head_mask.grad.abs().detach()
    tot_tokens += torch.ones_like(_SCREAMING_SNAKE_CASE			).float().detach().sum().data

  # Normalize
  attn_entropy /= tot_tokens
  head_importance /= tot_tokens
  # Layerwise importance normalization
  if not args.dont_normalize_importance_by_layer:
    lowerCAmelCase__			:Union[str, Any]											=							2
    lowerCAmelCase__			:Tuple											=							torch.pow(torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).sum(-1			)     ,   1 / exponent			)
    head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

  if not args.dont_normalize_global_importance:
    lowerCAmelCase__			:str											=							(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print matrices
  if compute_entropy:
    logger.info('Attention entropies'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  if compute_importance:
    logger.info('Head importance scores'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  logger.info('Head ranked by importance scores'			)
  lowerCAmelCase__			:List[Any]											=							torch.zeros(head_importance.numel()     ,   dtype=torch.long     ,   device=args.device			)
  lowerCAmelCase__			:List[Any]											=							torch.arange(
      head_importance.numel()     ,   device=args.device			)
  lowerCAmelCase__			:int											=							head_ranks.view_as(_SCREAMING_SNAKE_CASE			)
  print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  return attn_entropy, head_importance, total_loss
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							1 / loss  # instead of downsteam score use the LM loss
  logger.info('Pruning: original score: %f, threshold: %f'     ,   _SCREAMING_SNAKE_CASE     ,   original_score * args.masking_threshold			)

  lowerCAmelCase__			:Optional[int]											=							torch.ones_like(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							max(1     ,   int(new_head_mask.numel() * args.masking_amount			)			)

  lowerCAmelCase__			:List[str]											=							original_score
  while current_score >= original_score * args.masking_threshold:
    lowerCAmelCase__			:List[str]											=							new_head_mask.clone().detach()  # save current head mask
    # heads from least important to most - keep only not-masked heads
    lowerCAmelCase__			:str											=							float('Inf'			)
    lowerCAmelCase__			:List[str]											=							head_importance.view(-1			).sort()[1]

    if len(_SCREAMING_SNAKE_CASE			) <= num_to_mask:
      print('BREAK BY num_to_mask'			)
      break

    # mask heads
    lowerCAmelCase__			:int											=							current_heads_to_mask[:num_to_mask]
    logger.info('Heads to mask: %s'     ,   str(current_heads_to_mask.tolist()			)			)
    lowerCAmelCase__			:Dict											=							new_head_mask.view(-1			)
    lowerCAmelCase__			:Any											=							0.0
    lowerCAmelCase__			:Tuple											=							new_head_mask.view_as(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Optional[int]											=							new_head_mask.clone().detach()
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)

    # Compute metric and head importance again
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							compute_heads_importance(
        _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Any											=							1 / loss
    logger.info(
        'Masking: current score: %f, remaining heads %d (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   new_head_mask.sum()     ,   new_head_mask.sum() / new_head_mask.numel() * 100     ,   )

  logger.info('Final head mask'			)
  print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  np.save(os.path.join(args.output_dir     ,   'head_mask.npy'			)     ,   head_mask.detach().cpu().numpy()			)

  return head_mask
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Union[str, Any]											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  lowerCAmelCase__			:List[str]											=							sum(p.numel() for p in model.parameters()			)
  lowerCAmelCase__			:List[Any]											=							{
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_SCREAMING_SNAKE_CASE			)			)
  }

  for k, v in heads_to_prune.items():
    if isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
      lowerCAmelCase__			:Union[str, Any]											=							[
          v,
      ]

  assert sum(len(_SCREAMING_SNAKE_CASE			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
  model.prune_heads(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							sum(p.numel() for p in model.parameters()			)

  lowerCAmelCase__			:int											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE     ,   actually_pruned=_SCREAMING_SNAKE_CASE     ,   )

  lowerCAmelCase__			:int											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  logger.info(
      'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   pruned_num_params / original_num_params * 100     ,   )
  logger.info('Pruning: score with masking: %f score with pruning: %f'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  logger.info('Pruning: speed ratio (original timing / new timing): %f percents'     ,   original_time / new_time * 100			)
  save_model(_SCREAMING_SNAKE_CASE     ,   args.output_dir			)
def  __A   ()					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--data_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='The input data dir. Should contain the .tsv files (or other data files) for the task.'     ,   )
  parser.add_argument(
      '--model_name_or_path'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  parser.add_argument(
      '--output_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='The output directory where the model predictions and checkpoints will be written.'     ,   )

  # Other parameters
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained config name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--tokenizer_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained tokenizer name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Where do you want to store the pre-trained models downloaded from s3'     ,   )
  parser.add_argument(
      '--data_subset'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=-1     ,   help='If > 0: limit the data to a subset of data_subset instances.'			)
  parser.add_argument(
      '--overwrite_output_dir'     ,   action='store_true'     ,   help='Whether to overwrite data in output directory'			)
  parser.add_argument(
      '--overwrite_cache'     ,   action='store_true'     ,   help='Overwrite the cached training and evaluation sets'			)

  parser.add_argument(
      '--dont_normalize_importance_by_layer'     ,   action='store_true'     ,   help='Don\'t normalize importance score by layers'			)
  parser.add_argument(
      '--dont_normalize_global_importance'     ,   action='store_true'     ,   help='Don\'t normalize all importance scores between 0 and 1'     ,   )

  parser.add_argument(
      '--try_masking'     ,   action='store_true'     ,   help='Whether to try to mask head until a threshold of accuracy.'			)
  parser.add_argument(
      '--masking_threshold'     ,   default=0.9     ,   type=_SCREAMING_SNAKE_CASE     ,   help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'     ,   )
  parser.add_argument(
      '--masking_amount'     ,   default=0.1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Amount to heads to masking at each masking step.'			)
  parser.add_argument('--metric_name'     ,   default='acc'     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Metric to use for head masking.'			)

  parser.add_argument(
      '--max_seq_length'     ,   default=128     ,   type=_SCREAMING_SNAKE_CASE     ,   help=(
          'The maximum total input sequence length after WordPiece tokenization. \n'
          'Sequences longer than this will be truncated, sequences shorter padded.'
      )     ,   )
  parser.add_argument('--batch_size'     ,   default=1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Batch size.'			)

  parser.add_argument('--seed'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=42			)
  parser.add_argument('--local_rank'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=-1     ,   help='local_rank for distributed training on gpus'			)
  parser.add_argument('--no_cuda'     ,   action='store_true'     ,   help='Whether not to use CUDA when available'			)
  parser.add_argument('--server_ip'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  parser.add_argument('--server_port'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  lowerCAmelCase__			:Any											=							parser.parse_args()

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('Waiting for debugger attach'			)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,   redirect_output=_SCREAMING_SNAKE_CASE			)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    lowerCAmelCase__			:List[Any]											=							torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
    lowerCAmelCase__			:Optional[int]											=							0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank			)
    lowerCAmelCase__			:Dict											=							torch.device('cuda'     ,   args.local_rank			)
    lowerCAmelCase__			:Tuple											=							1
    torch.distributed.init_process_group(backend='nccl'			)  # Initializes the distributed backend

  # Setup logging
  logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
  logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device     ,   args.n_gpu     ,   bool(args.local_rank != -1			)			)			)

  lowerCAmelCase__			:int											=							GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

  # Distributed and parallel training
  model.to(args.device			)
  if args.local_rank != -1:
    lowerCAmelCase__			:Optional[Any]											=							nn.parallel.DistributedDataParallel(
        _SCREAMING_SNAKE_CASE     ,   device_ids=[args.local_rank]     ,   output_device=args.local_rank     ,   find_unused_parameters=_SCREAMING_SNAKE_CASE			)
  elif args.n_gpu > 1:
    lowerCAmelCase__			:Union[str, Any]											=							nn.DataParallel(_SCREAMING_SNAKE_CASE			)

  # Print/save training arguments
  os.makedirs(args.output_dir     ,   exist_ok=_SCREAMING_SNAKE_CASE			)
  torch.save(_SCREAMING_SNAKE_CASE     ,   os.path.join(args.output_dir     ,   'run_args.bin'			)			)
  logger.info('Training/evaluation parameters %s'     ,   _SCREAMING_SNAKE_CASE			)

  # Prepare dataset
  lowerCAmelCase__			:Optional[int]											=							np.concatenate(
      [
          np.loadtxt(args.data_dir     ,   dtype=np.intaa			),
      ]			)
  lowerCAmelCase__			:Union[str, Any]											=							(torch.from_numpy(_SCREAMING_SNAKE_CASE			),)
  lowerCAmelCase__			:Optional[int]											=							TensorDataset(*_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							RandomSampler(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							DataLoader(_SCREAMING_SNAKE_CASE     ,   sampler=_SCREAMING_SNAKE_CASE     ,   batch_size=args.batch_size			)

  # Compute head entropy and importance score
  compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Try head masking (set heads to zero until the score goes under a threshole)
  # and head pruning (remove masked heads and see the effect on the network)
  if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
    lowerCAmelCase__			:Optional[Any]											=							mask_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    prune_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  main()
