





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, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCamelCase       :				int           =			logging.getLogger(__name__)
def  __UpperCamelCase    (						snake_case      )    ->		List[str]:


   '''simple docstring'''







   __A    =  p * torch.log(snake_case      )
   __A    =  0
   return -plogp.sum(dim=-1      )
def  __UpperCamelCase    (						snake_case      )    ->		Union[str, Any]:


   '''simple docstring'''







   logger.info('''lv, h >\t''' + '''\t'''.join(F"{x + 1}" for x in range(len(snake_case      )      )      )      )
   for row in range(len(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  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case							,			snake_case=True							,			snake_case=True							,			snake_case=None							,			snake_case=False      )    ->		str:


   '''simple docstring'''







   __A   ,		__A    =  model.config.num_hidden_layers, model.config.num_attention_heads
   __A    =  torch.zeros(snake_case							,			snake_case      ).to(args.device      )
   __A    =  torch.zeros(snake_case							,			snake_case      ).to(args.device      )

   if head_mask is None:
      __A    =  torch.ones(snake_case							,			snake_case      ).to(args.device      )

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

   __A    =  None
   __A    =  None
   __A    =  0.0

   for step, inputs in enumerate(tqdm(snake_case							,			desc='''Iteration'''							,			disable=args.local_rank not in [-1, 0]      )      ):
      for k, v in inputs.items():
         __A    =  v.to(args.device      )

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      __A    =  model(**snake_case							,			head_mask=snake_case      )
      __A   ,		__A   ,		__A    =  (
          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

      if compute_entropy:
         for layer, attn in enumerate(snake_case      ):
            __A    =  entropy(attn.detach()      ) * inputs['''attention_mask'''].float().unsqueeze(1      )
            attn_entropy[layer] += masked_entropy.sum(-1      ).sum(0      ).detach()

      if compute_importance:
         head_importance += head_mask.grad.abs().detach()

      # Also store our logits/labels if we want to compute metrics afterwards
      if preds is None:
         __A    =  logits.detach().cpu().numpy()
         __A    =  inputs['''labels'''].detach().cpu().numpy()
      else:
         __A    =  np.append(snake_case							,			logits.detach().cpu().numpy()							,			axis=0      )
         __A    =  np.append(snake_case							,			inputs['''labels'''].detach().cpu().numpy()							,			axis=0      )

      tot_tokens += inputs["attention_mask"].float().detach().sum().data

   # Normalize
   attn_entropy /= tot_tokens
   head_importance /= tot_tokens
   # Layerwise importance normalization
   if not args.dont_normalize_importance_by_layer:
      __A    =  2
      __A    =  torch.pow(torch.pow(snake_case							,			snake_case      ).sum(-1      )							,			1 / exponent      )
      head_importance /= norm_by_layer.unsqueeze(-1      ) + 1E-20

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

   # Print/save matrices
   np.save(os.path.join(args.output_dir							,			'''attn_entropy.npy'''      )							,			attn_entropy.detach().cpu().numpy()      )
   np.save(os.path.join(args.output_dir							,			'''head_importance.npy'''      )							,			head_importance.detach().cpu().numpy()      )

   logger.info('''Attention entropies'''      )
   print_ad_tensor(snake_case      )
   logger.info('''Head importance scores'''      )
   print_ad_tensor(snake_case      )
   logger.info('''Head ranked by importance scores'''      )
   __A    =  torch.zeros(head_importance.numel()							,			dtype=torch.long							,			device=args.device      )
   __A    =  torch.arange(
       head_importance.numel()							,			device=args.device      )
   __A    =  head_ranks.view_as(snake_case      )
   print_ad_tensor(snake_case      )

   return attn_entropy, head_importance, preds, labels
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case      )    ->		List[Any]:


   '''simple docstring'''







   __A   ,		__A   ,		__A   ,		__A    =  compute_heads_importance(snake_case							,			snake_case							,			snake_case							,			compute_entropy=snake_case      )
   __A    =  np.argmax(snake_case							,			axis=1      ) if args.output_mode == '''classification''' else np.squeeze(snake_case      )
   __A    =  glue_compute_metrics(args.task_name							,			snake_case							,			snake_case      )[args.metric_name]
   logger.info('''Pruning: original score: %f, threshold: %f'''							,			snake_case							,			original_score * args.masking_threshold      )

   __A    =  torch.ones_like(snake_case      )
   __A    =  max(1							,			int(new_head_mask.numel() * args.masking_amount      )      )

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

      if len(snake_case      ) <= num_to_mask:
         break

      # mask heads
      __A    =  current_heads_to_mask[:num_to_mask]
      logger.info('''Heads to mask: %s'''							,			str(current_heads_to_mask.tolist()      )      )
      __A    =  new_head_mask.view(-1      )
      __A    =  0.0
      __A    =  new_head_mask.view_as(snake_case      )
      __A    =  new_head_mask.clone().detach()
      print_ad_tensor(snake_case      )

      # Compute metric and head importance again
      __A   ,		__A   ,		__A   ,		__A    =  compute_heads_importance(
          snake_case							,			snake_case							,			snake_case							,			compute_entropy=snake_case							,			head_mask=snake_case      )
      __A    =  np.argmax(snake_case							,			axis=1      ) if args.output_mode == '''classification''' else np.squeeze(snake_case      )
      __A    =  glue_compute_metrics(args.task_name							,			snake_case							,			snake_case      )[args.metric_name]
      logger.info(
          '''Masking: current score: %f, remaining heads %d (%.1f percents)'''							,			snake_case							,			new_head_mask.sum()							,			new_head_mask.sum() / new_head_mask.numel() * 1_0_0							,			)

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

   return head_mask
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case							,			snake_case      )    ->		Any:


   '''simple docstring'''







   __A    =  datetime.now()
   __A   ,		__A   ,		__A   ,		__A    =  compute_heads_importance(
       snake_case							,			snake_case							,			snake_case							,			compute_entropy=snake_case							,			compute_importance=snake_case							,			head_mask=snake_case      )
   __A    =  np.argmax(snake_case							,			axis=1      ) if args.output_mode == '''classification''' else np.squeeze(snake_case      )
   __A    =  glue_compute_metrics(args.task_name							,			snake_case							,			snake_case      )[args.metric_name]
   __A    =  datetime.now() - before_time

   __A    =  sum(p.numel() for p in model.parameters()      )
   __A    =  {
       layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(snake_case      )      )
   }

   assert sum(len(snake_case      ) for h in heads_to_prune.values()      ) == (1 - head_mask.long()).sum().item()
   model.prune_heads(snake_case      )
   __A    =  sum(p.numel() for p in model.parameters()      )

   __A    =  datetime.now()
   __A   ,		__A   ,		__A   ,		__A    =  compute_heads_importance(
       snake_case							,			snake_case							,			snake_case							,			compute_entropy=snake_case							,			compute_importance=snake_case							,			head_mask=snake_case							,			actually_pruned=snake_case							,			)
   __A    =  np.argmax(snake_case							,			axis=1      ) if args.output_mode == '''classification''' else np.squeeze(snake_case      )
   __A    =  glue_compute_metrics(args.task_name							,			snake_case							,			snake_case      )[args.metric_name]
   __A    =  datetime.now() - before_time

   logger.info(
       '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''							,			snake_case							,			snake_case							,			pruned_num_params / original_num_params * 1_0_0							,			)
   logger.info('''Pruning: score with masking: %f score with pruning: %f'''							,			snake_case							,			snake_case      )
   logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''							,			original_time / new_time * 1_0_0      )
def  __UpperCamelCase    (						)    ->		List[Any]:


   '''simple docstring'''







   __A    =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--data_dir'''							,			default=snake_case							,			type=snake_case							,			required=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=snake_case							,			type=snake_case							,			required=snake_case							,			help='''Path to pretrained model or model identifier from huggingface.co/models'''							,			)
   parser.add_argument(
       '''--task_name'''							,			default=snake_case							,			type=snake_case							,			required=snake_case							,			help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()      )							,			)
   parser.add_argument(
       '''--output_dir'''							,			default=snake_case							,			type=snake_case							,			required=snake_case							,			help='''The output directory where the model predictions and checkpoints will be written.'''							,			)

   # Other parameters
   parser.add_argument(
       '''--config_name'''							,			default=''''''							,			type=snake_case							,			help='''Pretrained config name or path if not the same as model_name_or_path'''							,			)
   parser.add_argument(
       '''--tokenizer_name'''							,			default=''''''							,			type=snake_case							,			help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''							,			)
   parser.add_argument(
       '''--cache_dir'''							,			default=snake_case							,			type=snake_case							,			help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''							,			)
   parser.add_argument(
       '''--data_subset'''							,			type=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=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=snake_case							,			help='''Amount to heads to masking at each masking step.'''      )
   parser.add_argument('''--metric_name'''							,			default='''acc'''							,			type=snake_case							,			help='''Metric to use for head masking.'''      )

   parser.add_argument(
       '''--max_seq_length'''							,			default=1_2_8							,			type=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=snake_case							,			help='''Batch size.'''      )

   parser.add_argument('''--seed'''							,			type=snake_case							,			default=4_2      )
   parser.add_argument('''--local_rank'''							,			type=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=snake_case							,			default=''''''							,			help='''Can be used for distant debugging.'''      )
   parser.add_argument('''--server_port'''							,			type=snake_case							,			default=''''''							,			help='''Can be used for distant debugging.'''      )
   __A    =  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=snake_case      )
      ptvsd.wait_for_attach()

   # Setup devices and distributed training
   if args.local_rank == -1 or args.no_cuda:
      __A    =  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
      __A    =  0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank      )
      __A    =  torch.device('''cuda'''							,			args.local_rank      )
      __A    =  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      )      )      )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(args.local_rank      ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

   # Set seeds
   set_seed(args.seed      )

   # Prepare GLUE task
   __A    =  args.task_name.lower()
   if args.task_name not in glue_processors:
      raise ValueError('''Task not found: %s''' % (args.task_name)      )
   __A    =  glue_processors[args.task_name]()
   __A    =  glue_output_modes[args.task_name]
   __A    =  processor.get_labels()
   __A    =  len(snake_case      )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   __A    =  AutoConfig.from_pretrained(
       args.config_name if args.config_name else args.model_name_or_path							,			num_labels=snake_case							,			finetuning_task=args.task_name							,			output_attentions=snake_case							,			cache_dir=args.cache_dir							,			)
   __A    =  AutoTokenizer.from_pretrained(
       args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,			cache_dir=args.cache_dir							,			)
   __A    =  AutoModelForSequenceClassification.from_pretrained(
       args.model_name_or_path							,			from_tf=bool('''.ckpt''' in args.model_name_or_path      )							,			config=snake_case							,			cache_dir=args.cache_dir							,			)

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

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

   # Prepare dataset for the GLUE task
   __A    =  GlueDataset(snake_case							,			tokenizer=snake_case							,			mode='''dev'''      )
   if args.data_subset > 0:
      __A    =  Subset(snake_case							,			list(range(min(args.data_subset							,			len(snake_case      )      )      )      )      )
   __A    =  SequentialSampler(snake_case      ) if args.local_rank == -1 else DistributedSampler(snake_case      )
   __A    =  DataLoader(
       snake_case							,			sampler=snake_case							,			batch_size=args.batch_size							,			collate_fn=snake_case      )

   # Compute head entropy and importance score
   compute_heads_importance(snake_case							,			snake_case							,			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:
      __A    =  mask_heads(snake_case							,			snake_case							,			snake_case      )
      prune_heads(snake_case							,			snake_case							,			snake_case							,			snake_case      )


if __name__ == "__main__":
    main()








class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self      )->   None:
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A    =  {}
     # indexmap keys are an index and it's values are the number at that index
     __A    =  {}

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == item:
           array.pop(UpperCAmelCase      )
           return array
        elif array[mid] < item:
           __A    =  mid + 1
        else:
           __A    =  mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == index:
           # If the item already exists in the array,
           # insert it after the existing item
           array.insert(mid + 1    ,							UpperCAmelCase      )
           return array
        elif array[mid] < index:
           __A    =  mid + 1
        else:
           __A    =  mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(UpperCAmelCase    ,							UpperCAmelCase      )
     return array

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   None:
     # Remove previous index
     if index in self.indexmap:
        __A    =  self.indexmap[index]
        if len(self.numbermap[n]      ) == 1:
           del self.numbermap[n]
        else:
           __A    =  self.binary_search_delete(self.numbermap[n]    ,							UpperCAmelCase      )

        # Set new index
     __A    =  number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
        __A    =  [index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
        __A    =  self.binary_search_insert(
            self.numbermap[number]    ,							UpperCAmelCase      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:
     return self.numbermap.get(UpperCAmelCase    ,							[-1]      )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

