

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


__UpperCAmelCase								=			logging.getLogger(__name__)



@dataclass
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






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






@dataclass
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
  lowerCamelCase   :      Optional[str]										=field(
      default=a_       ,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}       ,							)
  lowerCamelCase   :      int										=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."
          )
      }       ,							)
  lowerCamelCase   :      bool										=field(
      default=a_       ,							metadata={"help": "Overwrite the cached training and evaluation sets"}			)






def      snake_case_				()      ->     Union[str, 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.

   __lowerCAmelCase  :      Optional[int]    =						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  :      Tuple    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						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  :      Dict    =						import_module("""tasks"""					)
   try:
      __lowerCAmelCase  :      Dict    =						getattr(__A ,       model_args.task_type					)
      __lowerCAmelCase  :      TokenClassificationTask    =						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""" ,       __A					)

   # Set seed
   set_seed(training_args.seed					)

   # Prepare CONLL-2003 task
   __lowerCAmelCase  :      Union[str, Any]    =						token_classification_task.get_labels(data_args.labels					)
   __lowerCAmelCase  :      Dict[int, str]    =						dict(enumerate(__A					)					)
   __lowerCAmelCase  :      List[Any]    =						len(__A					)

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

   __lowerCAmelCase  :      Optional[Any]    =						AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path ,       num_labels=__A ,       idalabel=__A ,       labelaid={label: i for i, label in enumerate(__A					)} ,       cache_dir=model_args.cache_dir ,       )
   __lowerCAmelCase  :      Tuple    =						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  :      Any    =						AutoModelForTokenClassification.from_pretrained(
       model_args.model_name_or_path ,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path					) ,       config=__A ,       cache_dir=model_args.cache_dir ,       )

   # Get datasets
   __lowerCAmelCase  :      Tuple    =						(
       TokenClassificationDataset(
           token_classification_task=__A ,       data_dir=data_args.data_dir ,       tokenizer=__A ,       labels=__A ,       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  :      str    =						(
       TokenClassificationDataset(
           token_classification_task=__A ,       data_dir=data_args.data_dir ,       tokenizer=__A ,       labels=__A ,       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(__A					:						np.ndarray ,       __A					:						np.ndarray					) -> Tuple[List[int], List[int]]:
      __lowerCAmelCase  :      str    =						np.argmax(__A ,       axis=2					)

      __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						preds.shape

      __lowerCAmelCase  :      Any    =						[[] for _ in range(__A					)]
      __lowerCAmelCase  :      Optional[int]    =						[[] for _ in range(__A					)]

      for i in range(__A					):
         for j in range(__A					):
            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(__A					:						EvalPrediction					) -> Dict:
      __lowerCAmelCase   ,__lowerCAmelCase  :      Union[str, Any]    =						align_predictions(p.predictions ,       p.label_ids					)
      return {
          "accuracy_score": accuracy_score(__A ,       __A					),
          "precision": precision_score(__A ,       __A					),
          "recall": recall_score(__A ,       __A					),
          "f1": fa_score(__A ,       __A					),
      }

   # Data collator
   __lowerCAmelCase  :      Optional[Any]    =						DataCollatorWithPadding(__A ,       pad_to_multiple_of=8					) if training_args.fpaa else None

   # Initialize our Trainer
   __lowerCAmelCase  :      Any    =						Trainer(
       model=__A ,       args=__A ,       train_dataset=__A ,       eval_dataset=__A ,       compute_metrics=__A ,       data_collator=__A ,       )

   # 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  :      Union[str, Any]    =						{}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""					)

      __lowerCAmelCase  :      List[str]    =						trainer.evaluate()

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

         results.update(__A					)

    # Predict
   if training_args.do_predict:
      __lowerCAmelCase  :      Tuple    =						TokenClassificationDataset(
          token_classification_task=__A ,       data_dir=data_args.data_dir ,       tokenizer=__A ,       labels=__A ,       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  :      Optional[int]    =						trainer.predict(__A					)
      __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						align_predictions(__A ,       __A					)

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

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

   return results





def      snake_case_				(__A					:						Tuple					)      ->     List[str]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()





def      snake_case_				()      ->     List[Any]:
   for n in range(1 ,       1_0_0_0_0_0_0					):
      yield n * (n + 1) // 2





def      snake_case_				(__A					:						Dict					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						1
   __lowerCAmelCase  :      Optional[int]    =						2
   while i * i <= n:
      __lowerCAmelCase  :      Optional[int]    =						0
      while n % i == 0:
         n //= i
         multiplicity += 1
      divisors_count *= multiplicity + 1
      i += 1
   if n > 1:
      divisors_count *= 2
   return divisors_count





def      snake_case_				()      ->     Dict:
   return next(i for i in triangle_number_generator() if count_divisors(__A					) > 5_0_0					)


if __name__ == "__main__":
   print(solution())


