
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()


lowercase_											=	logging.getLogger(__name__)





@dataclass
class   __UpperCamelCase				:






     """simple docstring"""







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






@dataclass
class   __UpperCamelCase				:






     """simple docstring"""







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






def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
  __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						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.'''    )

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						import_module('''tasks'''    )

  try:
    __SCREAMING_SNAKE_CASE     :					Any			=						getattr(A_   ,	model_args.task_type    )
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						token_classification_task.get_labels(data_args.labels    )
  __SCREAMING_SNAKE_CASE     :					Dict			=						dict(enumerate(A_    )    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						len(A_    )

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

  __SCREAMING_SNAKE_CASE     :					Any			=						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   ,	)
  __SCREAMING_SNAKE_CASE     :					List[str]			=						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():
    __SCREAMING_SNAKE_CASE     :					Any			=						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
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						(
      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
  )
  __SCREAMING_SNAKE_CASE     :					Dict			=						(
      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(snake_case   ,	snake_case    ) -> Tuple[List[int], List[int]]:
    __SCREAMING_SNAKE_CASE     :					int			=						np.argmax(A_   ,	axis=2    )
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[Any]			=						preds.shape
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[[] for _ in range(A_    )]
    __SCREAMING_SNAKE_CASE     :					Tuple			=						[[] for _ in range(A_    )]

    for i in range(A_    ):
      for j in range(A_    ):
        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    ) -> Dict:
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						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
  __SCREAMING_SNAKE_CASE     :					int			=						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
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''    )

    __SCREAMING_SNAKE_CASE     :					List[Any]			=						trainer.evaluate()
    __SCREAMING_SNAKE_CASE     :					int			=						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:
    __SCREAMING_SNAKE_CASE     :					Any			=						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   ,	)

    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Any			=						trainer.predict(test_dataset.get_dataset()    )
    __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						align_predictions(A_   ,	A_    )
    __SCREAMING_SNAKE_CASE     :					str			=						classification_report(A_   ,	A_    )

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

    __SCREAMING_SNAKE_CASE     :					Any			=						os.path.join(training_args.output_dir   ,	'''test_results.txt'''    )

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

    # Save predictions
    __SCREAMING_SNAKE_CASE     :					Dict			=						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:
        __SCREAMING_SNAKE_CASE     :					Optional[int]			=						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]:
            __SCREAMING_SNAKE_CASE     :					Tuple			=						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()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
