

"""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()


_a				   =							logging.getLogger(__name__)




@dataclass
class       _lowerCAmelCase						:







 """simple docstring"""







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







@dataclass
class       _lowerCAmelCase						:







 """simple docstring"""







 __UpperCAmelCase			:      str     	=			field(
     metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}     )
 __UpperCAmelCase			:      Optional[str]     	=			field(
     metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}     )
 __UpperCAmelCase			:      int     	=			field(
     default=1_2_8  ,metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }  ,)
 __UpperCAmelCase			:      bool     	=			field(
     default=lowercase  ,metadata={"help": "Overwrite the cached training and evaluation sets"}     )







def 						_A						(						)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
   __lowercase	,__lowercase	,__lowercase			=		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.")

   __lowercase			=		import_module("tasks")

   try:
      __lowercase			=		getattr(UpperCamelCase_,    model_args.task_type)
      __lowercase			=		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",    UpperCamelCase_)

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

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

   __lowercase			=		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,    )
   __lowercase			=		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():
      __lowercase			=		TFAutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path,    from_pt=bool(".bin" in model_args.model_name_or_path),    config=UpperCamelCase_,    cache_dir=model_args.cache_dir,    )

   # Get datasets
   __lowercase			=		(
       TFTokenClassificationDataset(
           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
   )
   __lowercase			=		(
       TFTokenClassificationDataset(
           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]]:
      __lowercase			=		np.argmax(UpperCamelCase_,    axis=2)
      __lowercase	,__lowercase			=		preds.shape
      __lowercase			=		[[] for _ in range(UpperCamelCase_)]
      __lowercase			=		[[] for _ in range(UpperCamelCase_)]

      for i in range(UpperCamelCase_):
         for j in range(UpperCamelCase_):
            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(UpperCamelCase_  : EvalPrediction) -> Dict:
      __lowercase	,__lowercase			=		align_predictions(p.predictions,    p.label_ids)

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

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

   # Training
   if training_args.do_train:
      trainer.train()
      trainer.save_model()
      tokenizer.save_pretrained(training_args.output_dir)

   # Evaluation
   __lowercase			=		{}
   if training_args.do_eval:
      logger.info("*** Evaluate ***")

      __lowercase			=		trainer.evaluate()
      __lowercase			=		os.path.join(training_args.output_dir,    "eval_results.txt")

      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:
      __lowercase			=		TFTokenClassificationDataset(
          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,    )

      __lowercase	,__lowercase	,__lowercase			=		trainer.predict(test_dataset.get_dataset())
      __lowercase	,__lowercase			=		align_predictions(UpperCamelCase_,    UpperCamelCase_)
      __lowercase			=		classification_report(UpperCamelCase_,    UpperCamelCase_)

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

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

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

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

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

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

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

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

   return results


if __name__ == "__main__":
   main()







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : int,    UpperCamelCase_  : int)			->		None:
   '''simple docstring'''


   if target == 0:
      answer.append(path.copy())
   else:
      for index in range(UpperCamelCase_,    len(UpperCamelCase_)):
         if target >= candidates[index]:
            path.append(candidates[index])
            backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    target - candidates[index],    UpperCamelCase_)
            path.pop(len(UpperCamelCase_) - 1)




def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : int)			->		list:
   '''simple docstring'''


   __lowercase			=		[]  # type: list[int]
   __lowercase			=		[]  # type: list[int]
   backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    0)
   return answer




def 						_A						(						)			->		None:
   '''simple docstring'''


   print(combination_sum([-8, 2.3, 0],    1))


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()




