



"""simple docstring"""





import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE				:     List[str]          =	logging.getLogger(__name__)


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





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






def 	__UpperCAmelCase					(       )							->	str:






   """simple docstring"""


   _lowerCAmelCase					     =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
   _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(snake_case_   ,	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   ,	)
   logger.info(
       """n_replicas: %s, distributed training: %s, 16-bits training: %s"""   ,	training_args.n_replicas   ,	bool(training_args.n_replicas > 1   )   ,	training_args.fpaa   ,	)
   logger.info("""Training/evaluation parameters %s"""   ,	snake_case_   )

   # Prepare Token Classification task
   _lowerCAmelCase					     =		token_classification_task.get_labels(data_args.labels   )
   _lowerCAmelCase					     =		dict(enumerate(snake_case_   )   )
   _lowerCAmelCase					     =		len(snake_case_   )

   # 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=snake_case_   ,	idalabel=snake_case_   ,	labelaid={label: i for i, label in enumerate(snake_case_   )}   ,	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   ,	)

   with training_args.strategy.scope():
      _lowerCAmelCase					     =		TFAutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path   ,	from_pt=bool(""".bin""" in model_args.model_name_or_path   )   ,	config=snake_case_   ,	cache_dir=model_args.cache_dir   ,	)

   # Get datasets
   _lowerCAmelCase					     =		(
       TFTokenClassificationDataset(
           token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=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
   )
   _lowerCAmelCase					     =		(
       TFTokenClassificationDataset(
           token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=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(snake_case_  :  np.ndarray   ,	snake_case_  :  np.ndarray   ) -> Tuple[List[int], List[int]]:
      _lowerCAmelCase					     =		np.argmax(snake_case_   ,	axis=2   )
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		preds.shape
      _lowerCAmelCase					     =		[[] for _ in range(snake_case_   )]
      _lowerCAmelCase					     =		[[] for _ in range(snake_case_   )]

      for i in range(snake_case_   ):
         for j in range(snake_case_   ):
            if label_ids[i, j] != -100:
               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(snake_case_  :  EvalPrediction   ) -> Dict:
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		align_predictions(p.predictions   ,	p.label_ids   )

      return {
          "precision": precision_score(snake_case_   ,	snake_case_   ),
          "recall": recall_score(snake_case_   ,	snake_case_   ),
          "f1": fa_score(snake_case_   ,	snake_case_   ),
      }

   # Initialize our Trainer
   _lowerCAmelCase					     =		TFTrainer(
       model=snake_case_   ,	args=snake_case_   ,	train_dataset=train_dataset.get_dataset() if train_dataset else None   ,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None   ,	compute_metrics=snake_case_   ,	)

   # Training
   if training_args.do_train:
      trainer.train()
      trainer.save_model()
      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"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         logger.info("""***** Eval results *****"""   )

         for key, value in result.items():
            logger.info("""  %s = %s"""   ,	snake_case_   ,	snake_case_   )
            writer.write("""%s = %s\n""" % (key, value)   )

         results.update(snake_case_   )

    # Predict
   if training_args.do_predict:
      _lowerCAmelCase					     =		TFTokenClassificationDataset(
          token_classification_task=snake_case_   ,	data_dir=data_args.data_dir   ,	tokenizer=snake_case_   ,	labels=snake_case_   ,	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(test_dataset.get_dataset()   )
      _lowerCAmelCase     ,						_lowerCAmelCase					     =		align_predictions(snake_case_   ,	snake_case_   )
      _lowerCAmelCase					     =		classification_report(snake_case_   ,	snake_case_   )

      logger.info("""\n%s"""   ,	snake_case_   )

      _lowerCAmelCase					     =		os.path.join(training_args.output_dir   ,	"""test_results.txt"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         writer.write("""%s\n""" % report   )

      # Save predictions
      _lowerCAmelCase					     =		os.path.join(training_args.output_dir   ,	"""test_predictions.txt"""   )

      with open(snake_case_   ,	"""w"""   ) as writer:
         with open(os.path.join(data_args.data_dir   ,	"""test.txt"""   )   ,	"""r"""   ) as f:
            _lowerCAmelCase					     =		0

            for line in f:
               if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                  writer.write(snake_case_   )

                  if not preds_list[example_id]:
                     example_id += 1
               elif preds_list[example_id]:
                  _lowerCAmelCase					     =		line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""

                  writer.write(snake_case_   )
               else:
                  logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,	line.split()[0]   )

   return results


if __name__ == "__main__":
  main()




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()