






'''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


SCREAMING_SNAKE_CASE						=	logging.getLogger(__name__)


def 							lowercase_				(    __A :       str					,      __A :       Any			)       ->      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 							lowercase_				(    __A :       int					,      __A :       Any=False			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Union[str, Any]				  =2
 if unlogit:
  lowercase					:   str				  =torch.pow(__A					,      __A			)
 lowercase					:   str				  =p * torch.log(__A			)
 lowercase					:   List[str]				  =0
 return -plogp.sum(dim=-1			)


def 							lowercase_				(    __A :       List[str]			)       ->      int:



 """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 							lowercase_				(    __A :       Tuple					,      __A :       Union[str, Any]					,      __A :       List[str]					,      __A :       Optional[Any]=True					,      __A :       int=True					,      __A :       List[Any]=None					,      __A :       int=False			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase  ,		lowercase					:   Dict				  =model.config.num_hidden_layers, model.config.num_attention_heads
 lowercase					:   Optional[int]				  =torch.zeros(__A					,      __A			).to(args.device			)
 lowercase					:   Dict				  =torch.zeros(__A					,      __A			).to(args.device			)

 if head_mask is None:
  lowercase					:   List[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:
  lowercase					:   int				  =None

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

  # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
  lowercase					:   Union[str, Any]				  =model(__A					,      labels=__A					,      head_mask=__A			)
  #  (loss), lm_logits, presents, (all hidden_states), (attentions)
  lowercase  ,		lowercase  ,		lowercase					:   int				  =(
      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			):
    lowercase					:   Any				  =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:
  lowercase					:   Tuple				  =2
  lowercase					:   Any				  =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:
  lowercase					:   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'''			)
 lowercase					:   Any				  =torch.zeros(head_importance.numel()					,      dtype=torch.long					,      device=args.device			)
 lowercase					:   Union[str, Any]				  =torch.arange(
     head_importance.numel()					,      device=args.device			)
 lowercase					:   int				  =head_ranks.view_as(__A			)
 print_ad_tensor(__A			)
 return attn_entropy, head_importance, total_loss


def 							lowercase_				(    __A :       Any					,      __A :       Dict					,      __A :       Optional[Any]			)       ->      Tuple:



 """simple docstring"""




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

 lowercase					:   Tuple				  =torch.ones_like(__A			)
 lowercase					:   str				  =max(1					,      int(new_head_mask.numel() * args.masking_amount			)			)

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

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

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

  # Compute metric and head importance again
  lowercase  ,		lowercase  ,		lowercase					:   List[Any]				  =compute_heads_importance(
      __A					,      __A					,      __A					,      compute_entropy=__A					,      head_mask=__A			)
  lowercase					:   Optional[int]				  =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_0_0					,      )

 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 							lowercase_				(    __A :       Dict					,      __A :       int					,      __A :       Dict					,      __A :       int			)       ->      Optional[int]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =datetime.now()
 lowercase  ,		lowercase  ,		lowercase					:   int				  =compute_heads_importance(
     __A					,      __A					,      __A					,      compute_entropy=__A					,      compute_importance=__A					,      head_mask=__A			)
 lowercase					:   Tuple				  =1 / loss
 lowercase					:   Union[str, Any]				  =datetime.now() - before_time

 lowercase					:   Dict				  =sum(p.numel() for p in model.parameters()			)
 lowercase					:   Tuple				  ={
     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			):
   lowercase					:   Optional[int]				  =[
       v,
   ]

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

 lowercase					:   Optional[Any]				  =datetime.now()
 lowercase  ,		lowercase  ,		lowercase					:   List[str]				  =compute_heads_importance(
     __A					,      __A					,      __A					,      compute_entropy=__A					,      compute_importance=__A					,      head_mask=__A					,      actually_pruned=__A					,      )

 lowercase					:   Any				  =1 / loss
 lowercase					:   List[str]				  =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_0_0					,      )
 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_0_0			)
 save_model(__A					,      args.output_dir			)


def 							lowercase_				(    )       ->      List[str]:



 """simple docstring"""




 lowercase					:   str				  =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_2_8					,      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=4_2			)
 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.'''			)
 lowercase					:   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:
  lowercase					:   Optional[Any]				  =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
  lowercase					:   Dict				  =0 if args.no_cuda else torch.cuda.device_count()
 else:
  torch.cuda.set_device(args.local_rank			)
  lowercase					:   Dict				  =torch.device('''cuda'''					,      args.local_rank			)
  lowercase					:   Dict				  =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			)			)			)

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

 # Distributed and parallel training
 model.to(args.device			)
 if args.local_rank != -1:
  lowercase					:   Union[str, Any]				  =nn.parallel.DistributedDataParallel(
      __A					,      device_ids=[args.local_rank]					,      output_device=args.local_rank					,      find_unused_parameters=__A			)
 elif args.n_gpu > 1:
  lowercase					:   Any				  =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
 lowercase					:   Union[str, Any]				  =np.concatenate(
     [
         np.loadtxt(args.data_dir					,      dtype=np.intaa			),
     ]			)
 lowercase					:   Dict				  =(torch.from_numpy(__A			),)
 lowercase					:   Dict				  =TensorDataset(*__A			)
 lowercase					:   Optional[Any]				  =RandomSampler(__A			)
 lowercase					:   List[Any]				  =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:
  lowercase					:   Tuple				  =mask_heads(__A					,      __A					,      __A			)
  prune_heads(__A					,      __A					,      __A					,      __A			)


if __name__ == "__main__":
      main()











'''simple docstring'''



import mpmath  # for roots of unity
import numpy as np
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Optional[int]=None		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =list(poly_a or [0]		)[:]
       lowercase					:   Dict				  =list(poly_b or [0]		)[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
        self.polyA.pop()
       lowercase					:   int				  =len(self.polyA		)

       while self.polyB[-1] == 0:
        self.polyB.pop()
       lowercase					:   List[str]				  =len(self.polyB		)

       # Add 0 to make lengths equal a power of 2
       lowercase					:   Tuple				  =int(
           2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

       while len(self.polyA		) < self.c_max_length:
        self.polyA.append(0		)
       while len(self.polyB		) < self.c_max_length:
        self.polyB.append(0		)
       # A complex root used for the fourier transform
       lowercase					:   Optional[int]				  =complex(mpmath.root(x=1     ,      n=self.c_max_length     ,      k=1		)		)

       # The product
       lowercase					:   str				  =self.__multiply()






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
       # Corner case
       if len(UpperCAmelCase		) <= 1:
        return dft[0]
       #
       lowercase					:   List[Any]				  =self.c_max_length // 2
       while next_ncol > 0:
        lowercase					:   str				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   List[str]				  =self.root**next_ncol

        # First half of next step
        lowercase					:   Union[str, Any]				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Second half of next step
        lowercase					:   Any				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Update
        lowercase					:   Tuple				  =new_dft
        lowercase					:   List[Any]				  =next_ncol // 2
       return dft[0]






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.__dft('''A'''		)
       lowercase					:   Union[str, Any]				  =self.__dft('''B'''		)
       lowercase					:   Any				  =[[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]		) <= 1:
        return inverce_c[0]
       # Inverse DFT
       lowercase					:   Tuple				  =2
       while next_ncol <= self.c_max_length:
        lowercase					:   Tuple				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   Tuple				  =self.root ** (next_ncol // 2)
        lowercase					:   Optional[int]				  =1
        # First half of next step
        for j in range(self.c_max_length // next_ncol		):
         for i in range(next_ncol // 2		):
          # Even positions
          new_inverse_c[i].append(
              (
                  inverce_c[i][j]
                  + inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / 2		)
          # Odd positions
          new_inverse_c[i + next_ncol // 2].append(
              (
                  inverce_c[i][j]
                  - inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / (2 * current_root)		)
         current_root *= root
        # Update
        lowercase					:   List[Any]				  =new_inverse_c
        next_ncol *= 2
       # Unpack
       lowercase					:   List[str]				  =[round(x[0].real     ,      8		) + round(x[0].imag     ,      8		) * 1J for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
        inverce_c.pop()
       return inverce_c






      def __str__( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''A = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]		)		)
       lowercase					:   List[str]				  ='''B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]		)		)
       lowercase					:   Optional[Any]				  ='''A*B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.product		)		)

       return f'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()



