


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()


_snake_case					=					logging.getLogger(__name__)
@dataclass
class      UpperCAmelCase_ :

   '''simple docstring'''




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



@dataclass
class      UpperCAmelCase_ :

   '''simple docstring'''




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






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: List[Any]   =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
  lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: List[str]   =    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			: Dict   =    import_module("tasks"     )

  try:
    lowerCamelCase			: str   =    getattr(SCREAMING_SNAKE_CASE_				,						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"				,						SCREAMING_SNAKE_CASE_     )

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

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

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

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

    for i in range(SCREAMING_SNAKE_CASE_     ):
      for j in range(SCREAMING_SNAKE_CASE_     ):
        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(SCREAMING_SNAKE_CASE_     ) -> Dict:
    lowerCamelCase   ,     lowerCamelCase			: Union[str, Any]   =    align_predictions(p.predictions				,						p.label_ids     )

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

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

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

  # Evaluation
  lowerCamelCase			: Optional[Any]   =    {}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"     )

    lowerCamelCase			: List[Any]   =    trainer.evaluate()
    lowerCamelCase			: Optional[Any]   =    os.path.join(training_args.output_dir				,						"eval_results.txt"     )

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

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

      results.update(SCREAMING_SNAKE_CASE_     )

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

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

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

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

    # Save predictions
    lowerCamelCase			: List[Any]   =    os.path.join(training_args.output_dir				,						"test_predictions.txt"     )

    with open(SCREAMING_SNAKE_CASE_				,						"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(SCREAMING_SNAKE_CASE_     )

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

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

  return results


if __name__ == "__main__":
   main()







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		True






   def __call__(		self						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A = True						,    __A = False						,    __A = None						,    __A = None						,    __A = 0						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = False						,    __A = False						,    __A = True						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     if self.parse_html:
       if html_strings is None:
         raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"			)

       if nodes is not None or xpaths is not None or node_labels is not None:
         raise ValueError(
             "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: Any   =    features["xpaths"]
     else:
       if html_strings is not None:
         raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."			)
       if nodes is None or xpaths is None:
         raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"			)

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
       if isinstance(__A						,    __A			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     lowerCamelCase			: Tuple   =    self.tokenizer(
         text=questions if questions is not None else nodes						,    text_pair=nodes if questions is not None else None						,    xpaths=__A						,    node_labels=__A						,    add_special_tokens=__A						,    padding=__A						,    truncation=__A						,    max_length=__A						,    stride=__A						,    pad_to_multiple_of=__A						,    return_token_type_ids=__A						,    return_attention_mask=__A						,    return_overflowing_tokens=__A						,    return_special_tokens_mask=__A						,    return_offsets_mapping=__A						,    return_length=__A						,    verbose=__A						,    return_tensors=__A						,    **__A						,    )

     return encoded_inputs






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



