

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()


lowerCamelCase_					  =   logging.getLogger(__name__)







@dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : str  			=   field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
      lowerCamelCase_      : Optional[str]  			=   field(
          default=__snake_case    ,				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=__snake_case    ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
      lowerCamelCase_      : bool  			=   field(default=__snake_case    ,				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=__snake_case    ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,				)







@dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : str  			=   field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
      lowerCamelCase_      : Optional[str]  			=   field(
          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=__snake_case    ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}      )



def   UpperCamelCase(   )      ->							Tuple:





  '''simple docstring'''





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

  snake_case_					= import_module("""tasks"""				)

  try:
    snake_case_					= getattr(lowercase_     ,   model_args.task_type				)
    snake_case_					= 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"""     ,   lowercase_				)

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

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

  snake_case_					= AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,   num_labels=lowercase_     ,   idalabel=lowercase_     ,   labelaid={label: i for i, label in enumerate(lowercase_				)}     ,   cache_dir=model_args.cache_dir     ,   )
  snake_case_					= 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():
    snake_case_					= TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path     ,   from_pt=bool(""".bin""" in model_args.model_name_or_path				)     ,   config=lowercase_     ,   cache_dir=model_args.cache_dir     ,   )

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

    for i in range(lowercase_				):
      for j in range(lowercase_				):
        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(lowercase_				) -> Dict:
    snake_case_		,							snake_case_					= align_predictions(p.predictions     ,   p.label_ids				)

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

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

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

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

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

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

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

      results.update(lowercase_				)

    # Predict
  if training_args.do_predict:
    snake_case_					= TFTokenClassificationDataset(
        token_classification_task=lowercase_     ,   data_dir=data_args.data_dir     ,   tokenizer=lowercase_     ,   labels=lowercase_     ,   model_type=config.model_type     ,   max_seq_length=data_args.max_seq_length     ,   overwrite_cache=data_args.overwrite_cache     ,   mode=Split.test     ,   )

    snake_case_		,							snake_case_		,							snake_case_					= trainer.predict(test_dataset.get_dataset()				)
    snake_case_		,							snake_case_					= align_predictions(lowercase_     ,   lowercase_				)
    snake_case_					= classification_report(lowercase_     ,   lowercase_				)

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

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

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

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

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

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

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

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

  return results


if __name__ == "__main__":
    main()


from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()