


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_				:			str   =	logging.getLogger(__name__)
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					str					       =						field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default='''NER'''						,   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
      snake_case__    :					bool					       =						field(default=lowercase__						,   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.
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,   )
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					str					       =						field(
          metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}      )
      snake_case__    :					Optional[str]					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}						,   )
      snake_case__    :					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.'''
              )
          }						,   )
      snake_case__    :					bool					       =						field(
          default=lowercase__						,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:
       a_ :		Tuple							=  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.
              a_ :		int							=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a_ :		Tuple							=  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.'    )

       a_ :		str							=  import_module('tasks'    )
       try:
              a_ :		Dict							=  getattr(__A  ,   model_args.task_type    )
              a_ :		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
       a_ :		List[Any]							=  token_classification_task.get_labels(data_args.labels    )
       a_ :		Dict[int, str]							=  dict(enumerate(__A    )    )
       a_ :		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.

       a_ :		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  ,   )
       a_ :		int							=  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  ,   )
       a_ :		Tuple							=  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
       a_ :		Any							=  (
           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
       )
       a_ :		Optional[Any]							=  (
           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]]:
              a_ :		Union[str, Any]							=  np.argmax(__A  ,   axis=2    )

              a_ :		Dict							=  preds.shape

              a_ :		Optional[int]							=  [[] for _ in range(__A    )]
              a_ :		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:
              a_ :		Tuple							=  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
       a_ :		Union[str, Any]							=  DataCollatorWithPadding(__A  ,   pad_to_multiple_of=8    ) if training_args.fpaa else None

       # Initialize our Trainer
       a_ :		Optional[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
       a_ :		List[str]							=  {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'    )

              a_ :		str							=  trainer.evaluate()

              a_ :		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:
              a_ :		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  ,   )

              a_ :		Any							=  trainer.predict(__A    )
              a_ :		Optional[Any]							=  align_predictions(__A  ,   __A    )

              a_ :		Dict							=  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
              a_ :		List[Any]							=  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 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]    )     ->		Optional[int]:
       main()


if __name__ == "__main__":
   main()






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input must be an integer'    )
       if input_num <= 0:
              raise ValueError('Input must be positive'    )
       return sum(
           divisor for divisor in range(1  ,   input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


