




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


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





@dataclass
class        lowerCamelCase_							:


 '''simple docstring'''







 lowercase_				    =    field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
 lowercase_				    =    field(
     default=_SCREAMING_SNAKE_CASE       ,   metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
 lowercase_				    =    field(
     default="NER"       ,   metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}					)
 lowercase_				    =    field(
     default=_SCREAMING_SNAKE_CASE       ,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
 lowercase_				    =    field(default=_SCREAMING_SNAKE_CASE       ,   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.
 lowercase_				    =    field(
     default=_SCREAMING_SNAKE_CASE       ,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,   )





@dataclass
class        lowerCamelCase_							:


 '''simple docstring'''







 lowercase_				    =    field(
     metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}					)
 lowercase_				    =    field(
     default=_SCREAMING_SNAKE_CASE       ,   metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}       ,   )
 lowercase_				    =    field(
     default=128       ,   metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }       ,   )
 lowercase_				    =    field(
     default=_SCREAMING_SNAKE_CASE       ,   metadata={"help": "Overwrite the cached training and evaluation sets"}					)


def 					UpperCAmelCase_							(      )					-> Any:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE_							=			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.
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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.'							)

      SCREAMING_SNAKE_CASE_							=			import_module('tasks'							)
      try:
            SCREAMING_SNAKE_CASE_							=			getattr(__UpperCAmelCase			,			model_args.task_type							)
            SCREAMING_SNAKE_CASE_							=			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
      SCREAMING_SNAKE_CASE_							=			token_classification_task.get_labels(data_args.labels							)
      SCREAMING_SNAKE_CASE_							=			dict(enumerate(__UpperCAmelCase							)							)
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			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			,			)
      SCREAMING_SNAKE_CASE_							=			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			,			)
      SCREAMING_SNAKE_CASE_							=			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
      SCREAMING_SNAKE_CASE_							=			(
          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
      )
      SCREAMING_SNAKE_CASE_							=			(
          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						:	np.ndarray			,			__UpperCAmelCase						:	np.ndarray							) -> Tuple[List[int], List[int]]:
            SCREAMING_SNAKE_CASE_							=			np.argmax(__UpperCAmelCase			,			axis=2							)

            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			preds.shape

            SCREAMING_SNAKE_CASE_							=			[[] for _ in range(__UpperCAmelCase							)]
            SCREAMING_SNAKE_CASE_							=			[[] 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						:	EvalPrediction							) -> Dict:
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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
      SCREAMING_SNAKE_CASE_							=			DataCollatorWithPadding(__UpperCAmelCase			,			pad_to_multiple_of=8							) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_							=			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
      SCREAMING_SNAKE_CASE_							=			{}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'							)

            SCREAMING_SNAKE_CASE_							=			trainer.evaluate()

            SCREAMING_SNAKE_CASE_							=			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:
            SCREAMING_SNAKE_CASE_							=			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			,			)

            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			trainer.predict(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			align_predictions(__UpperCAmelCase			,			__UpperCAmelCase							)

            SCREAMING_SNAKE_CASE_							=			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
            SCREAMING_SNAKE_CASE_							=			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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]							)					-> List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
   main()





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number | (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number & ~(1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number ^ (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> bool:
      return ((number >> position) & 1) == 1

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()