





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




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




def      A__       (    )  ->  List[str]:






   """simple docstring"""







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

   _UpperCAmelCase           =   import_module('''tasks'''						)
   try:
      _UpperCAmelCase           =   getattr(SCREAMING_SNAKE_CASE_				,	model_args.task_type						)
      _UpperCAmelCase           =   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'''				,	SCREAMING_SNAKE_CASE_						)

   # Set seed
   set_seed(training_args.seed						)

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

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

   _UpperCAmelCase           =   AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=SCREAMING_SNAKE_CASE_				,	idalabel=SCREAMING_SNAKE_CASE_				,	labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_						)}				,	cache_dir=model_args.cache_dir				,	)
   _UpperCAmelCase           =   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				,	)
   _UpperCAmelCase           =   AutoModelForTokenClassification.from_pretrained(
       model_args.model_name_or_path				,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)				,	config=SCREAMING_SNAKE_CASE_				,	cache_dir=model_args.cache_dir				,	)

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

      _UpperCAmelCase       ,      _UpperCAmelCase           =   preds.shape

      _UpperCAmelCase           =   [[] for _ in range(SCREAMING_SNAKE_CASE_						)]
      _UpperCAmelCase           =   [[] for _ in range(SCREAMING_SNAKE_CASE_						)]

      for i in range(SCREAMING_SNAKE_CASE_						):
         for j in range(SCREAMING_SNAKE_CASE_						):
            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(SCREAMING_SNAKE_CASE_   :      EvalPrediction						) -> Dict:
      _UpperCAmelCase       ,      _UpperCAmelCase           =   align_predictions(p.predictions				,	p.label_ids						)
      return {
          "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						),
          "precision": precision_score(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						),
          "recall": recall_score(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						),
          "f1": fa_score(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						),
      }

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

   # Initialize our Trainer
   _UpperCAmelCase           =   Trainer(
       model=SCREAMING_SNAKE_CASE_				,	args=SCREAMING_SNAKE_CASE_				,	train_dataset=SCREAMING_SNAKE_CASE_				,	eval_dataset=SCREAMING_SNAKE_CASE_				,	compute_metrics=SCREAMING_SNAKE_CASE_				,	data_collator=SCREAMING_SNAKE_CASE_				,	)

   # 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
   _UpperCAmelCase           =   {}
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''						)

      _UpperCAmelCase           =   trainer.evaluate()

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

         results.update(SCREAMING_SNAKE_CASE_						)

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

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

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

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

   return results
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Any:






   """simple docstring"""







   main()


if __name__ == "__main__":
   main()






import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()