
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()


_UpperCAmelCase    			=					logging.getLogger(__name__)

@dataclass
class 		__magic_name__				:


  """simple docstring"""





  _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 		__magic_name__				:


  """simple docstring"""





  _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 		_lowerCamelCase       (   ):
  """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(_a			,   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'''			,   _a  )

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

  # 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=_a			,   idalabel=_a			,   labelaid={label: i for i, label in enumerate(_a  )}			,   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=_a			,   cache_dir=model_args.cache_dir			,   )

  # Get datasets
  _lowerCamelCase      =						(
      TFTokenClassificationDataset(
          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
  )
  _lowerCamelCase      =						(
      TFTokenClassificationDataset(
          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			,   _a  ) -> Tuple[List[int], List[int]]:
    _lowerCamelCase      =						np.argmax(_a			,   axis=2  )
    _lowerCamelCase    ,    _lowerCamelCase      =						preds.shape
    _lowerCamelCase      =						[[] for _ in range(_a  )]
    _lowerCamelCase      =						[[] for _ in range(_a  )]

    for i in range(_a  ):
      for j in range(_a  ):
        if label_ids[i, j] != -1_0_0:
          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  ) -> Dict:
    _lowerCamelCase    ,    _lowerCamelCase      =						align_predictions(p.predictions			,   p.label_ids  )

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

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

  # 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(_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:
    _lowerCamelCase      =						TFTokenClassificationDataset(
        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			,   )

    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase      =						trainer.predict(test_dataset.get_dataset()  )
    _lowerCamelCase    ,    _lowerCamelCase      =						align_predictions(_a			,   _a  )
    _lowerCamelCase      =						classification_report(_a			,   _a  )

    logger.info('''\n%s'''			,   _a  )

    _lowerCamelCase      =						os.path.join(training_args.output_dir			,   '''test_results.txt'''  )

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

    # Save predictions
    _lowerCamelCase      =						os.path.join(training_args.output_dir			,   '''test_predictions.txt'''  )

    with open(_a			,   '''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(_a  )

            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(_a  )
          else:
            logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''			,   line.split()[0]  )

  return results


if __name__ == "__main__":
  main()






class 		__magic_name__				:


  """simple docstring"""





  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = "_prev", "_data", "_next"


    def __init__(					self	,		a__	,		a__	,		a__		):
      _lowerCamelCase      =						link_p
      _lowerCamelCase      =						element
      _lowerCamelCase      =						link_n


    def     _UpperCAmelCase  (					self		):
      return (
          f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
      )







  def __init__(					self		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._trailer
    _lowerCamelCase      =						self._header
    _lowerCamelCase      =						0


  def __len__(					self		):
    return self._size


  def     _UpperCAmelCase  (					self		):
    return self.__len__() == 0


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # Create new_node by setting it's prev.link -> header
    # setting it's next.link -> trailer
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						new_node
    _lowerCamelCase      =						new_node
    self._size += 1
    return self


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						node._prev
    _lowerCamelCase      =						node._next

    _lowerCamelCase      =						successor
    _lowerCamelCase      =						predecessor
    self._size -= 1
    _lowerCamelCase      =						node._data
    _lowerCamelCase      =						_lowerCamelCase      =						_lowerCamelCase      =						None
    del node
    return temp







class 		__magic_name__				( _DoublyLinkedBase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._header._next._data


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._trailer._prev._data


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._header	,		a__	,		self._header._next		)


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._trailer._prev	,		a__	,		self._trailer		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._header._next		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._trailer._prev		)




