



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


a_    :  Optional[int]						  =		logging.getLogger(__name__)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__=False			):


      """simple docstring"""



      lowerCamelCase             =		2
      if unlogit:
            lowerCamelCase             =		torch.pow(UpperCAmelCase__ , UpperCAmelCase__			)
      lowerCamelCase             =		p * torch.log(UpperCAmelCase__			)
      lowerCamelCase             =		0
      return -plogp.sum(dim=-1			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=True , UpperCAmelCase__=True , UpperCAmelCase__=None , UpperCAmelCase__=False			):


      """simple docstring"""



      lowerCamelCase      ,				lowerCamelCase             =		model.config.num_hidden_layers, model.config.num_attention_heads
      lowerCamelCase             =		torch.zeros(UpperCAmelCase__ , UpperCAmelCase__			).to(args.device			)
      lowerCamelCase             =		torch.zeros(UpperCAmelCase__ , UpperCAmelCase__			).to(args.device			)

      if head_mask is None:
            lowerCamelCase             =		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:
            lowerCamelCase             =		None

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

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            lowerCamelCase             =		model(UpperCAmelCase__ , labels=UpperCAmelCase__ , head_mask=UpperCAmelCase__			)
            #  (loss), lm_logits, presents, (all hidden_states), (attentions)
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		(
                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__			):
                        lowerCamelCase             =		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:
            lowerCamelCase             =		2
            lowerCamelCase             =		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:
            lowerCamelCase             =		(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"			)
      lowerCamelCase             =		torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device			)
      lowerCamelCase             =		torch.arange(
          head_importance.numel() , device=args.device			)
      lowerCamelCase             =		head_ranks.view_as(UpperCAmelCase__			)
      print_ad_tensor(UpperCAmelCase__			)
      return attn_entropy, head_importance, total_loss

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		compute_heads_importance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__			)
      lowerCamelCase             =		1 / loss  # instead of downsteam score use the LM loss
      logger.info("Pruning: original score: %f, threshold: %f" , UpperCAmelCase__ , original_score * args.masking_threshold			)

      lowerCamelCase             =		torch.ones_like(UpperCAmelCase__			)
      lowerCamelCase             =		max(1 , int(new_head_mask.numel() * args.masking_amount			)			)

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

            if len(UpperCAmelCase__			) <= num_to_mask:
                  print("BREAK BY num_to_mask"			)
                  break

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

            # Compute metric and head importance again
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		compute_heads_importance(
                UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , head_mask=UpperCAmelCase__			)
            lowerCamelCase             =		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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		datetime.now()
      lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		compute_heads_importance(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , compute_importance=UpperCAmelCase__ , head_mask=UpperCAmelCase__			)
      lowerCamelCase             =		1 / loss
      lowerCamelCase             =		datetime.now() - before_time

      lowerCamelCase             =		sum(p.numel() for p in model.parameters()			)
      lowerCamelCase             =		{
          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__			):
                  lowerCamelCase             =		[
                      v,
                  ]

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

      lowerCamelCase             =		datetime.now()
      lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		compute_heads_importance(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , compute_importance=UpperCAmelCase__ , head_mask=UpperCAmelCase__ , actually_pruned=UpperCAmelCase__ , )

      lowerCamelCase             =		1 / loss
      lowerCamelCase             =		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      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		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."			)
      lowerCamelCase             =		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:
            lowerCamelCase             =		torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"			)
            lowerCamelCase             =		0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank			)
            lowerCamelCase             =		torch.device("cuda" , args.local_rank			)
            lowerCamelCase             =		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			)			)			)

      lowerCamelCase             =		GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

      # Distributed and parallel training
      model.to(args.device			)
      if args.local_rank != -1:
            lowerCamelCase             =		nn.parallel.DistributedDataParallel(
                UpperCAmelCase__ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=UpperCAmelCase__			)
      elif args.n_gpu > 1:
            lowerCamelCase             =		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
      lowerCamelCase             =		np.concatenate(
          [
              np.loadtxt(args.data_dir , dtype=np.intaa			),
          ]			)
      lowerCamelCase             =		(torch.from_numpy(UpperCAmelCase__			),)
      lowerCamelCase             =		TensorDataset(*UpperCAmelCase__			)
      lowerCamelCase             =		RandomSampler(UpperCAmelCase__			)
      lowerCamelCase             =		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:
            lowerCamelCase             =		mask_heads(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
            prune_heads(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)


if __name__ == "__main__":
     main()




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_    :  Optional[int]						  =		logging.getLogger(__name__)





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Pretrained config name or path if not the same as model_name'})
     _A      =      field(
         default='NER'							,     metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Set this flag to use fast tokenization.'})
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,     )





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							,     )
     _A      =      field(
         default=1_28							,     metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }							,     )
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Overwrite the cached training and evaluation sets'})



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir			)
          and os.listdir(training_args.output_dir			)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                " --overwrite_output_dir to overcome."			)

      lowerCamelCase             =		import_module("tasks"			)
      try:
            lowerCamelCase             =		getattr(UpperCAmelCase__ , model_args.task_type			)
            lowerCamelCase             =		token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""			)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1			) , training_args.fpaa , )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank			):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s" , UpperCAmelCase__			)

      # Set seed
      set_seed(training_args.seed			)

      # Prepare CONLL-2003 task
      lowerCamelCase             =		token_classification_task.get_labels(data_args.labels			)
      lowerCamelCase             =		dict(enumerate(UpperCAmelCase__			)			)
      lowerCamelCase             =		len(UpperCAmelCase__			)

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

      lowerCamelCase             =		AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__			)} , cache_dir=model_args.cache_dir , )
      lowerCamelCase             =		AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
      lowerCamelCase             =		AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path			) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )

      # Get datasets
      lowerCamelCase             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
          if training_args.do_train
          else None
      )
      lowerCamelCase             =		(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
          if training_args.do_eval
          else None
      )

      def align_predictions(UpperCAmelCase__ , UpperCAmelCase__			) -> Tuple[List[int], List[int]]:
            lowerCamelCase             =		np.argmax(UpperCAmelCase__ , axis=2			)

            lowerCamelCase      ,				lowerCamelCase             =		preds.shape

            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]
            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]

            for i in range(UpperCAmelCase__			):
                  for j in range(UpperCAmelCase__			):
                        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                              out_label_list[i].append(label_map[label_ids[i][j]]			)
                              preds_list[i].append(label_map[preds[i][j]]			)

            return preds_list, out_label_list

      def compute_metrics(UpperCAmelCase__			) -> Dict:
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(p.predictions , p.label_ids			)
            return {
                "accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__			),
                "f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__			),
            }

      # Data collator
      lowerCamelCase             =		DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8			) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCamelCase             =		Trainer(
          model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_process_zero():
                  tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
      lowerCamelCase             =		{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"			)

            lowerCamelCase             =		trainer.evaluate()

            lowerCamelCase             =		os.path.join(training_args.output_dir , "eval_results.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        logger.info("***** Eval results *****"			)
                        for key, value in result.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

                  results.update(UpperCAmelCase__			)

    # Predict
      if training_args.do_predict:
            lowerCamelCase             =		TokenClassificationDataset(
                token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )

            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		trainer.predict(UpperCAmelCase__			)
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(UpperCAmelCase__ , UpperCAmelCase__			)

            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_results.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        for key, value in metrics.items():
                              logger.info("  %s = %s" , UpperCAmelCase__ , UpperCAmelCase__			)
                              writer.write("%s = %s\n" % (key, value)			)

        # Save predictions
            lowerCamelCase             =		os.path.join(training_args.output_dir , "test_predictions.txt"			)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase__ , "w"			) as writer:
                        with open(os.path.join(data_args.data_dir , "test.txt"			) , "r"			) as f:
                              token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)

      return results

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      main()


if __name__ == "__main__":
     main()