

import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)









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()