


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


__lowerCamelCase   	=  logging.getLogger(__name__)



def 			_a      (    __UpperCamelCase			):
     a_     :  str				      =     p * torch.log(__UpperCamelCase			)
     a_     :  Optional[Any]				      =     0
     return -plogp.sum(dim=-1			)



def 			_a      (    __UpperCamelCase			):
     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=True	,	__UpperCamelCase=True	,	__UpperCamelCase=None	,	__UpperCamelCase=False			):
     a_      ,      a_     :  Optional[Any]				      =     model.config.num_hidden_layers, model.config.num_attention_heads
     a_     :  int				      =     torch.zeros(__UpperCamelCase	,	__UpperCamelCase			).to(args.device			)
     a_     :  List[str]				      =     torch.zeros(__UpperCamelCase	,	__UpperCamelCase			).to(args.device			)

     if head_mask is None:
          a_     :  Optional[Any]				      =     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:
          a_     :  List[str]				      =     None

     a_     :  Tuple				      =     None
     a_     :  Optional[Any]				      =     None
     a_     :  Dict				      =     0.0

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

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

          if compute_entropy:
               for layer, attn in enumerate(__UpperCamelCase			):
                    a_     :  Dict				      =     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_     :  str				      =     logits.detach().cpu().numpy()
               a_     :  int				      =     inputs["""labels"""].detach().cpu().numpy()
          else:
               a_     :  int				      =     np.append(__UpperCamelCase	,	logits.detach().cpu().numpy()	,	axis=0			)
               a_     :  int				      =     np.append(__UpperCamelCase	,	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_     :  Tuple				      =     2
          a_     :  List[Any]				      =     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:
          a_     :  Union[str, Any]				      =     (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(__UpperCamelCase			)
     logger.info("""Head importance scores"""			)
     print_ad_tensor(__UpperCamelCase			)
     logger.info("""Head ranked by importance scores"""			)
     a_     :  Optional[int]				      =     torch.zeros(head_importance.numel()	,	dtype=torch.long	,	device=args.device			)
     a_     :  int				      =     torch.arange(
         head_importance.numel()	,	device=args.device			)
     a_     :  List[Any]				      =     head_ranks.view_as(__UpperCamelCase			)
     print_ad_tensor(__UpperCamelCase			)

     return attn_entropy, head_importance, preds, labels



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_      ,      a_      ,      a_     :  int				      =     compute_heads_importance(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	compute_entropy=__UpperCamelCase			)
     a_     :  List[Any]				      =     np.argmax(__UpperCamelCase	,	axis=1			) if args.output_mode == """classification""" else np.squeeze(__UpperCamelCase			)
     a_     :  Tuple				      =     glue_compute_metrics(args.task_name	,	__UpperCamelCase	,	__UpperCamelCase			)[args.metric_name]
     logger.info("""Pruning: original score: %f, threshold: %f"""	,	__UpperCamelCase	,	original_score * args.masking_threshold			)

     a_     :  str				      =     torch.ones_like(__UpperCamelCase			)
     a_     :  Any				      =     max(1	,	int(new_head_mask.numel() * args.masking_amount			)			)

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

          if len(__UpperCamelCase			) <= num_to_mask:
               break

          # mask heads
          a_     :  Any				      =     current_heads_to_mask[:num_to_mask]
          logger.info("""Heads to mask: %s"""	,	str(current_heads_to_mask.tolist()			)			)
          a_     :  List[str]				      =     new_head_mask.view(-1			)
          a_     :  int				      =     0.0
          a_     :  Any				      =     new_head_mask.view_as(__UpperCamelCase			)
          a_     :  Tuple				      =     new_head_mask.clone().detach()
          print_ad_tensor(__UpperCamelCase			)

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

     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[int]				      =     datetime.now()
     a_      ,      a_      ,      a_      ,      a_     :  List[Any]				      =     compute_heads_importance(
         __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	compute_entropy=__UpperCamelCase	,	compute_importance=__UpperCamelCase	,	head_mask=__UpperCamelCase			)
     a_     :  List[Any]				      =     np.argmax(__UpperCamelCase	,	axis=1			) if args.output_mode == """classification""" else np.squeeze(__UpperCamelCase			)
     a_     :  int				      =     glue_compute_metrics(args.task_name	,	__UpperCamelCase	,	__UpperCamelCase			)[args.metric_name]
     a_     :  Any				      =     datetime.now() - before_time

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

     assert sum(len(__UpperCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
     model.prune_heads(__UpperCamelCase			)
     a_     :  Tuple				      =     sum(p.numel() for p in model.parameters()			)

     a_     :  List[str]				      =     datetime.now()
     a_      ,      a_      ,      a_      ,      a_     :  int				      =     compute_heads_importance(
         __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	compute_entropy=__UpperCamelCase	,	compute_importance=__UpperCamelCase	,	head_mask=__UpperCamelCase	,	actually_pruned=__UpperCamelCase	,	)
     a_     :  str				      =     np.argmax(__UpperCamelCase	,	axis=1			) if args.output_mode == """classification""" else np.squeeze(__UpperCamelCase			)
     a_     :  Any				      =     glue_compute_metrics(args.task_name	,	__UpperCamelCase	,	__UpperCamelCase			)[args.metric_name]
     a_     :  Any				      =     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 * 1_0_0	,	)
     logger.info("""Pruning: score with masking: %f score with pruning: %f"""	,	__UpperCamelCase	,	__UpperCamelCase			)
     logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""	,	original_time / new_time * 1_0_0			)



def 			_a      (    ):
     a_     :  Any				      =     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(
         """--task_name"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	required=__UpperCamelCase	,	help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()			)	,	)
     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 huggingface.co"""	,	)
     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=1_2_8	,	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=4_2			)
     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."""			)
     a_     :  List[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=__UpperCamelCase			)
          ptvsd.wait_for_attach()

     # Setup devices and distributed training
     if args.local_rank == -1 or args.no_cuda:
          a_     :  Union[str, Any]				      =     torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
          a_     :  List[str]				      =     0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank			)
          a_     :  Dict				      =     torch.device("""cuda"""	,	args.local_rank			)
          a_     :  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			)			)			)
     # 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_     :  List[str]				      =     args.task_name.lower()
     if args.task_name not in glue_processors:
          raise ValueError("""Task not found: %s""" % (args.task_name)			)
     a_     :  List[str]				      =     glue_processors[args.task_name]()
     a_     :  str				      =     glue_output_modes[args.task_name]
     a_     :  int				      =     processor.get_labels()
     a_     :  Dict				      =     len(__UpperCamelCase			)

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

     a_     :  Optional[Any]				      =     AutoConfig.from_pretrained(
         args.config_name if args.config_name else args.model_name_or_path	,	num_labels=__UpperCamelCase	,	finetuning_task=args.task_name	,	output_attentions=__UpperCamelCase	,	cache_dir=args.cache_dir	,	)
     a_     :  Optional[Any]				      =     AutoTokenizer.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path	,	cache_dir=args.cache_dir	,	)
     a_     :  Tuple				      =     AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path	,	from_tf=bool(""".ckpt""" in args.model_name_or_path			)	,	config=__UpperCamelCase	,	cache_dir=args.cache_dir	,	)

     # Distributed and parallel training
     model.to(args.device			)
     if args.local_rank != -1:
          a_     :  Union[str, Any]				      =     nn.parallel.DistributedDataParallel(
              __UpperCamelCase	,	device_ids=[args.local_rank]	,	output_device=args.local_rank	,	find_unused_parameters=__UpperCamelCase			)
     elif args.n_gpu > 1:
          a_     :  Optional[int]				      =     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 for the GLUE task
     a_     :  Optional[int]				      =     GlueDataset(__UpperCamelCase	,	tokenizer=__UpperCamelCase	,	mode="""dev"""			)
     if args.data_subset > 0:
          a_     :  List[Any]				      =     Subset(__UpperCamelCase	,	list(range(min(args.data_subset	,	len(__UpperCamelCase			)			)			)			)			)
     a_     :  Tuple				      =     SequentialSampler(__UpperCamelCase			) if args.local_rank == -1 else DistributedSampler(__UpperCamelCase			)
     a_     :  Tuple				      =     DataLoader(
         __UpperCamelCase	,	sampler=__UpperCamelCase	,	batch_size=args.batch_size	,	collate_fn=__UpperCamelCase			)

     # 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:
          a_     :  Any				      =     mask_heads(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
          prune_heads(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)


if __name__ == "__main__":
       main()







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



