


"""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()


__UpperCAmelCase    				=      logging.getLogger(__name__)

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

@dataclass
class  lowercase_  :
      __magic_name__     :							str          =				field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}						)
      __magic_name__     :							Optional[str]          =				field(
          metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						)
      __magic_name__     :							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."""
              )
          }							,							)
      __magic_name__     :							bool          =				field(
          default=a_							,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)





def     lowercase__	(  )					->     List[str]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.
  lowerCAmelCase__    :    Tuple								=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
  lowerCAmelCase__				,		lowerCAmelCase__				,		lowerCAmelCase__    :    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."				)

  lowerCAmelCase__    :    int								=					import_module("tasks"				)

  try:
    lowerCAmelCase__    :    List[str]								=					getattr(lowerCamelCase				,	model_args.task_type				)
    lowerCAmelCase__    :    TokenClassificationTask								=					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"				,	lowerCamelCase				)

  # Prepare Token Classification task
  lowerCAmelCase__    :    Dict								=					token_classification_task.get_labels(data_args.labels				)
  lowerCAmelCase__    :    Dict[int, str]								=					dict(enumerate(lowerCamelCase				)				)
  lowerCAmelCase__    :    Any								=					len(lowerCamelCase				)

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

  lowerCAmelCase__    :    Tuple								=					AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=lowerCamelCase				,	idalabel=lowerCamelCase				,	labelaid={label: i for i, label in enumerate(lowerCamelCase				)}				,	cache_dir=model_args.cache_dir				,	)
  lowerCAmelCase__    :    Optional[Any]								=					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__    :    Dict								=					TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path				,	from_pt=bool(".bin" in model_args.model_name_or_path				)				,	config=lowerCamelCase				,	cache_dir=model_args.cache_dir				,	)

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

    for i in range(lowerCamelCase				):
      for j in range(lowerCamelCase				):
        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(lowerCamelCase     :  EvalPrediction				) -> Dict:
    lowerCAmelCase__				,		lowerCAmelCase__    :    List[str]								=					align_predictions(p.predictions				,	p.label_ids				)

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

  # Initialize our Trainer
  lowerCAmelCase__    :    Dict								=					TFTrainer(
      model=lowerCamelCase				,	args=lowerCamelCase				,	train_dataset=train_dataset.get_dataset() if train_dataset else None				,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,	compute_metrics=lowerCamelCase				,	)

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

  # Evaluation
  lowerCAmelCase__    :    Union[str, Any]								=					{}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"				)

    lowerCAmelCase__    :    Dict								=					trainer.evaluate()
    lowerCAmelCase__    :    List[str]								=					os.path.join(training_args.output_dir				,	"eval_results.txt"				)

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

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

      results.update(lowerCamelCase				)

    # Predict
  if training_args.do_predict:
    lowerCAmelCase__    :    Optional[Any]								=					TFTokenClassificationDataset(
        token_classification_task=lowerCamelCase				,	data_dir=data_args.data_dir				,	tokenizer=lowerCamelCase				,	labels=lowerCamelCase				,	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__    :    List[Any]								=					trainer.predict(test_dataset.get_dataset()				)
    lowerCAmelCase__				,		lowerCAmelCase__    :    Tuple								=					align_predictions(lowerCamelCase				,	lowerCamelCase				)
    lowerCAmelCase__    :    Optional[int]								=					classification_report(lowerCamelCase				,	lowerCamelCase				)

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

    lowerCAmelCase__    :    Tuple								=					os.path.join(training_args.output_dir				,	"test_results.txt"				)

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

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

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

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

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

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

  return results


if __name__ == "__main__":
   main()




"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

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