





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


snake_case_           =     logging.getLogger(__name__)






def lowerCamelCase__       (				snake_case_      :    str			, snake_case_      :    List[str]			) ->							Union[str, Any]:
   # save results
   if os.path.exists(UpperCAmelCase_			):
      if os.path.exists(os.path.join(UpperCAmelCase_			, '''config.json'''			)			) and os.path.isfile(
          os.path.join(UpperCAmelCase_			, '''config.json'''			)			):
         os.remove(os.path.join(UpperCAmelCase_			, '''config.json'''			)			)
      if os.path.exists(os.path.join(UpperCAmelCase_			, '''pytorch_model.bin'''			)			) and os.path.isfile(
          os.path.join(UpperCAmelCase_			, '''pytorch_model.bin'''			)			):
         os.remove(os.path.join(UpperCAmelCase_			, '''pytorch_model.bin'''			)			)
   else:
      os.makedirs(UpperCAmelCase_			)
   model.save_pretrained(UpperCAmelCase_			)






def lowerCamelCase__       (				snake_case_      :    Any			, snake_case_      :    List[str]=False			) ->							Tuple:
   __snake_case							=  2
   if unlogit:
      __snake_case							=  torch.pow(UpperCAmelCase_			, UpperCAmelCase_			)
   __snake_case							=  p * torch.log(UpperCAmelCase_			)
   __snake_case							=  0
   return -plogp.sum(dim=-1			)






def lowerCamelCase__       (				snake_case_      :    Optional[int]			) ->							List[Any]:
   logger.info('''lv, h >\t''' + '''\t'''.join(f"""{x + 1}""" for x in range(len(UpperCAmelCase_			)			)			)			)
   for row in range(len(UpperCAmelCase_			)			):
      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 lowerCamelCase__       (				snake_case_      :    Optional[int]			, snake_case_      :    str			, snake_case_      :    Dict			, snake_case_      :    Any=True			, snake_case_      :    Any=True			, snake_case_      :    Optional[int]=None			, snake_case_      :    Optional[Any]=False			) ->							List[Any]:
   __snake_case							=  model.config.num_hidden_layers, model.config.num_attention_heads
   __snake_case							=  torch.zeros(UpperCAmelCase_			, UpperCAmelCase_			).to(args.device			)
   __snake_case							=  torch.zeros(UpperCAmelCase_			, UpperCAmelCase_			).to(args.device			)

   if head_mask is None:
      __snake_case							=  torch.ones(UpperCAmelCase_			, UpperCAmelCase_			).to(args.device			)

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

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

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      __snake_case							=  model(UpperCAmelCase_			, labels=UpperCAmelCase_			, head_mask=UpperCAmelCase_			)
      #  (loss), lm_logits, presents, (all hidden_states), (attentions)
      __snake_case							=  (
          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(UpperCAmelCase_			):
            __snake_case							=  entropy(attn.detach()			, UpperCAmelCase_			)
            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(UpperCAmelCase_			).float().detach().sum().data

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

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

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






def lowerCamelCase__       (				snake_case_      :    Any			, snake_case_      :    List[Any]			, snake_case_      :    Optional[int]			) ->							Any:
   __snake_case							=  compute_heads_importance(UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			, compute_entropy=UpperCAmelCase_			)
   __snake_case							=  1 / loss  # instead of downsteam score use the LM loss
   logger.info('''Pruning: original score: %f, threshold: %f'''			, UpperCAmelCase_			, original_score * args.masking_threshold			)

   __snake_case							=  torch.ones_like(UpperCAmelCase_			)
   __snake_case							=  max(1			, int(new_head_mask.numel() * args.masking_amount			)			)

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

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

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

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

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

   return head_mask






def lowerCamelCase__       (				snake_case_      :    Dict			, snake_case_      :    List[str]			, snake_case_      :    Optional[Any]			, snake_case_      :    List[Any]			) ->							Optional[Any]:
   __snake_case							=  datetime.now()
   __snake_case							=  compute_heads_importance(
       UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			, compute_entropy=UpperCAmelCase_			, compute_importance=UpperCAmelCase_			, head_mask=UpperCAmelCase_			)
   __snake_case							=  1 / loss
   __snake_case							=  datetime.now() - before_time

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

   for k, v in heads_to_prune.items():
      if isinstance(UpperCAmelCase_			, UpperCAmelCase_			):
         __snake_case							=  [
             v,
         ]

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

   __snake_case							=  datetime.now()
   __snake_case							=  compute_heads_importance(
       UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			, compute_entropy=UpperCAmelCase_			, compute_importance=UpperCAmelCase_			, head_mask=UpperCAmelCase_			, actually_pruned=UpperCAmelCase_			, )

   __snake_case							=  1 / loss
   __snake_case							=  datetime.now() - before_time

   logger.info(
       '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''			, UpperCAmelCase_			, UpperCAmelCase_			, pruned_num_params / original_num_params * 100			, )
   logger.info('''Pruning: score with masking: %f score with pruning: %f'''			, UpperCAmelCase_			, UpperCAmelCase_			)
   logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''			, original_time / new_time * 100			)
   save_model(UpperCAmelCase_			, args.output_dir			)






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

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

   parser.add_argument(
       '''--max_seq_length'''			, default=128			, type=UpperCAmelCase_			, 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=UpperCAmelCase_			, help='''Batch size.'''			)

   parser.add_argument('''--seed'''			, type=UpperCAmelCase_			, default=42			)
   parser.add_argument('''--local_rank'''			, type=UpperCAmelCase_			, 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=UpperCAmelCase_			, default=''''''			, help='''Can be used for distant debugging.'''			)
   parser.add_argument('''--server_port'''			, type=UpperCAmelCase_			, default=''''''			, help='''Can be used for distant debugging.'''			)
   __snake_case							=  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=UpperCAmelCase_			)
      ptvsd.wait_for_attach()

   # Setup devices and distributed training
   if args.local_rank == -1 or args.no_cuda:
      __snake_case							=  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
      __snake_case							=  0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank			)
      __snake_case							=  torch.device('''cuda'''			, args.local_rank			)
      __snake_case							=  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			)			)			)

   __snake_case							=  GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

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

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

   # Prepare dataset
   __snake_case							=  np.concatenate(
       [
           np.loadtxt(args.data_dir			, dtype=np.intaa			),
       ]			)
   __snake_case							=  (torch.from_numpy(UpperCAmelCase_			),)
   __snake_case							=  TensorDataset(*UpperCAmelCase_			)
   __snake_case							=  RandomSampler(UpperCAmelCase_			)
   __snake_case							=  DataLoader(UpperCAmelCase_			, sampler=UpperCAmelCase_			, batch_size=args.batch_size			)

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

   # 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:
      __snake_case							=  mask_heads(UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			)
      prune_heads(UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			, UpperCAmelCase_			)


if __name__ == "__main__":
    main()





'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


if __name__ == "__main__":
      print(f'''{solution() = }''')






