






"""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, 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     :     Union[str, Any]            =	logging.getLogger(__name__)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]							)->						Tuple:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[int]				=				p * torch.log(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				0
     return -plogp.sum(dim=-1							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Tuple							)->						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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Any       , snake_case    :      Optional[Any]       , snake_case    :      List[Any]=True       , snake_case    :      str=True       , snake_case    :      List[str]=None       , snake_case    :      Any=False							)->						Dict:






     '''simple docstring'''



     UpperCAmelCase__				,							UpperCAmelCase__		:  Tuple				=				model.config.num_hidden_layers, model.config.num_attention_heads
     UpperCAmelCase__		:  List[Any]				=				torch.zeros(snake_case       , snake_case							).to(args.device							)
     UpperCAmelCase__		:  Tuple				=				torch.zeros(snake_case       , snake_case							).to(args.device							)

     if head_mask is None:
          UpperCAmelCase__		:  str				=				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:
          UpperCAmelCase__		:  Tuple				=				None

     UpperCAmelCase__		:  Tuple				=				None
     UpperCAmelCase__		:  Any				=				None
     UpperCAmelCase__		:  Any				=				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():
               UpperCAmelCase__		:  List[Any]				=				v.to(args.device							)

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          UpperCAmelCase__		:  str				=				model(**snake_case       , head_mask=snake_case							)
          UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  Tuple				=				(
              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							):
                    UpperCAmelCase__		:  List[str]				=				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:
               UpperCAmelCase__		:  Any				=				logits.detach().cpu().numpy()
               UpperCAmelCase__		:  Any				=				inputs["labels"].detach().cpu().numpy()
          else:
               UpperCAmelCase__		:  Optional[int]				=				np.append(snake_case       , logits.detach().cpu().numpy()       , axis=0							)
               UpperCAmelCase__		:  Dict				=				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:
          UpperCAmelCase__		:  Dict				=				2
          UpperCAmelCase__		:  Union[str, Any]				=				torch.pow(torch.pow(snake_case       , snake_case							).sum(-1							)       , 1 / exponent							)
          head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-2_0

     if not args.dont_normalize_global_importance:
          UpperCAmelCase__		:  Optional[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(snake_case							)
     logger.info("Head importance scores"							)
     print_ad_tensor(snake_case							)
     logger.info("Head ranked by importance scores"							)
     UpperCAmelCase__		:  List[str]				=				torch.zeros(head_importance.numel()       , dtype=torch.long       , device=args.device							)
     UpperCAmelCase__		:  List[str]				=				torch.arange(
         head_importance.numel()       , device=args.device							)
     UpperCAmelCase__		:  List[Any]				=				head_ranks.view_as(snake_case							)
     print_ad_tensor(snake_case							)

     return attn_entropy, head_importance, preds, labels


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int       , snake_case    :      str       , snake_case    :      Any							)->						int:






     '''simple docstring'''



     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  str				=				compute_heads_importance(snake_case       , snake_case       , snake_case       , compute_entropy=snake_case							)
     UpperCAmelCase__		:  List[Any]				=				np.argmax(snake_case       , axis=1							) if args.output_mode == "classification" else np.squeeze(snake_case							)
     UpperCAmelCase__		:  int				=				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							)

     UpperCAmelCase__		:  Tuple				=				torch.ones_like(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				max(1       , int(new_head_mask.numel() * args.masking_amount							)							)

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

          if len(snake_case							) <= num_to_mask:
               break

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

          # Compute metric and head importance again
          UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  str				=				compute_heads_importance(
              snake_case       , snake_case       , snake_case       , compute_entropy=snake_case       , head_mask=snake_case							)
          UpperCAmelCase__		:  Tuple				=				np.argmax(snake_case       , axis=1							) if args.output_mode == "classification" else np.squeeze(snake_case							)
          UpperCAmelCase__		:  Optional[int]				=				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() * 100       , )

     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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Tuple       , snake_case    :      Optional[Any]       , snake_case    :      str       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				datetime.now()
     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				compute_heads_importance(
         snake_case       , snake_case       , snake_case       , compute_entropy=snake_case       , compute_importance=snake_case       , head_mask=snake_case							)
     UpperCAmelCase__		:  Any				=				np.argmax(snake_case       , axis=1							) if args.output_mode == "classification" else np.squeeze(snake_case							)
     UpperCAmelCase__		:  Union[str, Any]				=				glue_compute_metrics(args.task_name       , snake_case       , snake_case							)[args.metric_name]
     UpperCAmelCase__		:  Tuple				=				datetime.now() - before_time

     UpperCAmelCase__		:  str				=				sum(p.numel() for p in model.parameters()							)
     UpperCAmelCase__		:  Optional[int]				=				{
         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							)
     UpperCAmelCase__		:  Any				=				sum(p.numel() for p in model.parameters()							)

     UpperCAmelCase__		:  Optional[Any]				=				datetime.now()
     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  Any				=				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       , )
     UpperCAmelCase__		:  List[str]				=				np.argmax(snake_case       , axis=1							) if args.output_mode == "classification" else np.squeeze(snake_case							)
     UpperCAmelCase__		:  Optional[int]				=				glue_compute_metrics(args.task_name       , snake_case       , snake_case							)[args.metric_name]
     UpperCAmelCase__		:  Any				=				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 * 100       , )
     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 * 100							)


def      SCREAMING_SNAKE_CASE__    (   )->						Any:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[Any]				=				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=128       , 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=42							)
     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."							)
     UpperCAmelCase__		:  Dict				=				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:
          UpperCAmelCase__		:  List[str]				=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"							)
          UpperCAmelCase__		:  Any				=				0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank							)
          UpperCAmelCase__		:  List[str]				=				torch.device("cuda"       , args.local_rank							)
          UpperCAmelCase__		:  Tuple				=				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
     UpperCAmelCase__		:  Optional[Any]				=				args.task_name.lower()
     if args.task_name not in glue_processors:
          raise ValueError("Task not found: %s" % (args.task_name)							)
     UpperCAmelCase__		:  List[str]				=				glue_processors[args.task_name]()
     UpperCAmelCase__		:  Optional[Any]				=				glue_output_modes[args.task_name]
     UpperCAmelCase__		:  Tuple				=				processor.get_labels()
     UpperCAmelCase__		:  List[str]				=				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.

     UpperCAmelCase__		:  Optional[Any]				=				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       , )
     UpperCAmelCase__		:  Any				=				AutoTokenizer.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path       , cache_dir=args.cache_dir       , )
     UpperCAmelCase__		:  Optional[Any]				=				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:
          UpperCAmelCase__		:  Union[str, Any]				=				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:
          UpperCAmelCase__		:  int				=				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
     UpperCAmelCase__		:  int				=				GlueDataset(snake_case       , tokenizer=snake_case       , mode="dev"							)
     if args.data_subset > 0:
          UpperCAmelCase__		:  Dict				=				Subset(snake_case       , list(range(min(args.data_subset       , len(snake_case							)							)							)							)							)
     UpperCAmelCase__		:  Tuple				=				SequentialSampler(snake_case							) if args.local_rank == -1 else DistributedSampler(snake_case							)
     UpperCAmelCase__		:  str				=				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:
          UpperCAmelCase__		:  int				=				mask_heads(snake_case       , snake_case       , snake_case							)
          prune_heads(snake_case       , snake_case       , snake_case       , snake_case							)


if __name__ == "__main__":
 main()









"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



