


'''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					:  int											=      logging.getLogger(__name__)







def 				UpperCamelCase_			(    A__					:   Dict							,    A__					:   Union[str, Any]					):
  '''simple docstring'''





  if os.path.exists(A__					):
    if os.path.exists(os.path.join(A__							,    """config.json"""					)					) and os.path.isfile(
        os.path.join(A__							,    """config.json"""					)					):
      os.remove(os.path.join(A__							,    """config.json"""					)					)
    if os.path.exists(os.path.join(A__							,    """pytorch_model.bin"""					)					) and os.path.isfile(
        os.path.join(A__							,    """pytorch_model.bin"""					)					):
      os.remove(os.path.join(A__							,    """pytorch_model.bin"""					)					)
  else:
    os.makedirs(A__					)
  model.save_pretrained(A__					)







def 				UpperCamelCase_			(    A__					:   Tuple							,    A__					:   List[str]=False					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   2
  if unlogit:
    lowerCAmelCase_			:      List[Any]			=   torch.pow(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   p * torch.log(A__					)
  lowerCAmelCase_			:      Union[str, Any]			=   0
  return -plogp.sum(dim=-1					)







def 				UpperCamelCase_			(    A__					:   List[Any]					):
  '''simple docstring'''





  logger.info("""lv, h >\t""" + """\t""".join(f'{x + 1}' for x in range(len(A__					)					)					)					)
  for row in range(len(A__					)					):
    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 				UpperCamelCase_			(    A__					:   Any							,    A__					:   Any							,    A__					:   Tuple							,    A__					:   Optional[int]=True							,    A__					:   Any=True							,    A__					:   List[Any]=None							,    A__					:   Dict=False					):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   model.config.num_hidden_layers, model.config.num_attention_heads
  lowerCAmelCase_			:      str			=   torch.zeros(A__							,    A__					).to(args.device					)
  lowerCAmelCase_			:      Optional[Any]			=   torch.zeros(A__							,    A__					).to(args.device					)

  if head_mask is None:
    lowerCAmelCase_			:      Any			=   torch.ones(A__							,    A__					).to(args.device					)

  head_mask.requires_grad_(requires_grad=A__					)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    lowerCAmelCase_			:      str			=   None

  lowerCAmelCase_			:      Tuple			=   0.0
  lowerCAmelCase_			:      Optional[Any]			=   0.0
  for step, inputs in enumerate(tqdm(A__							,    desc="""Iteration"""							,    disable=args.local_rank not in [-1, 0]					)					):
    lowerCAmelCase_			:      Union[str, Any]			=   tuple(t.to(args.device					) for t in inputs					)
    ((lowerCAmelCase_),							)			:      List[Any]			=   inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    lowerCAmelCase_			:      Any			=   model(A__							,    labels=A__							,    head_mask=A__					)
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   (
        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(A__					):
        lowerCAmelCase_			:      str			=   entropy(attn.detach()							,    A__					)
        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(A__					).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_			:      List[Any]			=   2
    lowerCAmelCase_			:      Tuple			=   torch.pow(torch.pow(A__							,    A__					).sum(-1					)							,    1 / exponent					)
    head_importance /= norm_by_layer.unsqueeze(-1					) + 1E-20

  if not args.dont_normalize_global_importance:
    lowerCAmelCase_			:      List[str]			=   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print matrices
  if compute_entropy:
    logger.info("""Attention entropies"""					)
    print_ad_tensor(A__					)
  if compute_importance:
    logger.info("""Head importance scores"""					)
    print_ad_tensor(A__					)
  logger.info("""Head ranked by importance scores"""					)
  lowerCAmelCase_			:      Tuple			=   torch.zeros(head_importance.numel()							,    dtype=torch.long							,    device=args.device					)
  lowerCAmelCase_			:      int			=   torch.arange(
      head_importance.numel()							,    device=args.device					)
  lowerCAmelCase_			:      Dict			=   head_ranks.view_as(A__					)
  print_ad_tensor(A__					)
  return attn_entropy, head_importance, total_loss







def 				UpperCamelCase_			(    A__					:   str							,    A__					:   Union[str, Any]							,    A__					:   Tuple					):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   compute_heads_importance(A__							,    A__							,    A__							,    compute_entropy=A__					)
  lowerCAmelCase_			:      Dict			=   1 / loss  # instead of downsteam score use the LM loss
  logger.info("""Pruning: original score: %f, threshold: %f"""							,    A__							,    original_score * args.masking_threshold					)

  lowerCAmelCase_			:      Optional[Any]			=   torch.ones_like(A__					)
  lowerCAmelCase_			:      int			=   max(1							,    int(new_head_mask.numel() * args.masking_amount					)					)

  lowerCAmelCase_			:      Union[str, Any]			=   original_score
  while current_score >= original_score * args.masking_threshold:
    lowerCAmelCase_			:      Optional[int]			=   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_			:      int			=   head_importance.view(-1					).sort()[1]

    if len(A__					) <= 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_			:      Tuple			=   0.0
    lowerCAmelCase_			:      List[str]			=   new_head_mask.view_as(A__					)
    lowerCAmelCase_			:      Any			=   new_head_mask.clone().detach()
    print_ad_tensor(A__					)

    # Compute metric and head importance again
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   compute_heads_importance(
        A__							,    A__							,    A__							,    compute_entropy=A__							,    head_mask=A__					)
    lowerCAmelCase_			:      Any			=   1 / loss
    logger.info(
        """Masking: current score: %f, remaining heads %d (%.1f percents)"""							,    A__							,    new_head_mask.sum()							,    new_head_mask.sum() / new_head_mask.numel() * 1_00							,    )

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

  return head_mask







def 				UpperCamelCase_			(    A__					:   Dict							,    A__					:   Any							,    A__					:   List[Any]							,    A__					:   List[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[int]			=   datetime.now()
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   compute_heads_importance(
      A__							,    A__							,    A__							,    compute_entropy=A__							,    compute_importance=A__							,    head_mask=A__					)
  lowerCAmelCase_			:      str			=   1 / loss
  lowerCAmelCase_			:      Dict			=   datetime.now() - before_time

  lowerCAmelCase_			:      Tuple			=   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(A__					)					)
  }

  for k, v in heads_to_prune.items():
    if isinstance(A__							,    A__					):
      lowerCAmelCase_			:      int			=   [
          v,
      ]

  assert sum(len(A__					) for h in heads_to_prune.values()					) == (1 - head_mask.long()).sum().item()
  model.prune_heads(A__					)
  lowerCAmelCase_			:      Optional[Any]			=   sum(p.numel() for p in model.parameters()					)

  lowerCAmelCase_			:      Optional[int]			=   datetime.now()
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Union[str, Any]			=   compute_heads_importance(
      A__							,    A__							,    A__							,    compute_entropy=A__							,    compute_importance=A__							,    head_mask=A__							,    actually_pruned=A__							,    )

  lowerCAmelCase_			:      int			=   1 / loss
  lowerCAmelCase_			:      Union[str, Any]			=   datetime.now() - before_time

  logger.info(
      """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""							,    A__							,    A__							,    pruned_num_params / original_num_params * 1_00							,    )
  logger.info("""Pruning: score with masking: %f score with pruning: %f"""							,    A__							,    A__					)
  logger.info("""Pruning: speed ratio (original timing / new timing): %f percents"""							,    original_time / new_time * 1_00					)
  save_model(A__							,    args.output_dir					)







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--data_dir"""							,    default=A__							,    type=A__							,    required=A__							,    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=A__							,    type=A__							,    required=A__							,    help="""Path to pretrained model or model identifier from huggingface.co/models"""							,    )
  parser.add_argument(
      """--output_dir"""							,    default=A__							,    type=A__							,    required=A__							,    help="""The output directory where the model predictions and checkpoints will be written."""							,    )

  # Other parameters
  parser.add_argument(
      """--config_name"""							,    default=""""""							,    type=A__							,    help="""Pretrained config name or path if not the same as model_name_or_path"""							,    )
  parser.add_argument(
      """--tokenizer_name"""							,    default=""""""							,    type=A__							,    help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""							,    )
  parser.add_argument(
      """--cache_dir"""							,    default=A__							,    type=A__							,    help="""Where do you want to store the pre-trained models downloaded from s3"""							,    )
  parser.add_argument(
      """--data_subset"""							,    type=A__							,    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=A__							,    help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""							,    )
  parser.add_argument(
      """--masking_amount"""							,    default=0.1							,    type=A__							,    help="""Amount to heads to masking at each masking step."""					)
  parser.add_argument("""--metric_name"""							,    default="""acc"""							,    type=A__							,    help="""Metric to use for head masking."""					)

  parser.add_argument(
      """--max_seq_length"""							,    default=1_28							,    type=A__							,    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=A__							,    help="""Batch size."""					)

  parser.add_argument("""--seed"""							,    type=A__							,    default=42					)
  parser.add_argument("""--local_rank"""							,    type=A__							,    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=A__							,    default=""""""							,    help="""Can be used for distant debugging."""					)
  parser.add_argument("""--server_port"""							,    type=A__							,    default=""""""							,    help="""Can be used for distant debugging."""					)
  lowerCAmelCase_			:      str			=   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=A__					)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    lowerCAmelCase_			:      str			=   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
    lowerCAmelCase_			:      Dict			=   0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank					)
    lowerCAmelCase_			:      Tuple			=   torch.device("""cuda"""							,    args.local_rank					)
    lowerCAmelCase_			:      Optional[int]			=   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_			:      List[str]			=   GPTaLMHeadModel.from_pretrained(args.model_name_or_path					)

  # Distributed and parallel training
  model.to(args.device					)
  if args.local_rank != -1:
    lowerCAmelCase_			:      Optional[int]			=   nn.parallel.DistributedDataParallel(
        A__							,    device_ids=[args.local_rank]							,    output_device=args.local_rank							,    find_unused_parameters=A__					)
  elif args.n_gpu > 1:
    lowerCAmelCase_			:      Tuple			=   nn.DataParallel(A__					)

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

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

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

  # 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_			:      Dict			=   mask_heads(A__							,    A__							,    A__					)
    prune_heads(A__							,    A__							,    A__							,    A__					)


if __name__ == "__main__":
  main()







'''simple docstring'''






from __future__ import annotations

from typing import Any






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    pass







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  None:
      lowerCAmelCase_			:      Any			=   data
      lowerCAmelCase_			:      Node | None			=   None

    def __iter__(   self    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Union[str, Any]			=   self
      lowerCAmelCase_			:      Any			=   []
      while node:
        if node in visited:
          raise ContainsLoopError
        visited.append(lowerCamelCase		)
        yield node.data
        lowerCAmelCase_			:      int			=   node.next_node





    @property
    def __lowercase   (   self    :  str		)   ->  bool:
      try:
        list(self		)
        return False
      except ContainsLoopError:
        return True


if __name__ == "__main__":
  __A					:  Dict											=      Node(1)
  __A					:  Optional[Any]											=      Node(2)
  __A					:  int											=      Node(3)
  __A					:  Optional[Any]											=      Node(4)
  print(root_node.has_loop)  # False
  __A					:  Any											=      root_node.next_node
  print(root_node.has_loop)  # True

  __A					:  List[Any]											=      Node(5)
  __A					:  Dict											=      Node(6)
  __A					:  str											=      Node(5)
  __A					:  Dict											=      Node(6)
  print(root_node.has_loop)  # False

  __A					:  Optional[int]											=      Node(1)
  print(root_node.has_loop)  # False



