






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        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


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





@dataclass
class        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


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





def      lowerCamelCase__	(   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
  SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			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				:					Optional[int]		  =			import_module("tasks" )

  try:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			getattr(__UpperCAmelCase  ,   model_args.task_type )
    SCREAMING_SNAKE_CASE				:					Tuple		  =			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"  ,   __UpperCAmelCase )

  # Prepare Token Classification task
  SCREAMING_SNAKE_CASE				:					Tuple		  =			token_classification_task.get_labels(data_args.labels )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			dict(enumerate(__UpperCAmelCase ) )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			len(__UpperCAmelCase )

  # 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				:					str		  =			AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,   num_labels=__UpperCAmelCase  ,   idalabel=__UpperCAmelCase  ,   labelaid={label: i for i, label in enumerate(__UpperCAmelCase )}  ,   cache_dir=model_args.cache_dir  ,   )
  SCREAMING_SNAKE_CASE				:					List[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():
    SCREAMING_SNAKE_CASE				:					Dict		  =			TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path  ,   from_pt=bool(".bin" in model_args.model_name_or_path )  ,   config=__UpperCAmelCase  ,   cache_dir=model_args.cache_dir  ,   )

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

    for i in range(__UpperCAmelCase ):
      for j in range(__UpperCAmelCase ):
        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:
    SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			align_predictions(p.predictions  ,   p.label_ids )

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

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE				:					Any		  =			TFTrainer(
      model=__UpperCAmelCase  ,   args=__UpperCAmelCase  ,   train_dataset=train_dataset.get_dataset() if train_dataset else None  ,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None  ,   compute_metrics=__UpperCAmelCase  ,   )

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

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

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

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

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

      results.update(__UpperCAmelCase )

    # Predict
  if training_args.do_predict:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			TFTokenClassificationDataset(
        token_classification_task=__UpperCAmelCase  ,   data_dir=data_args.data_dir  ,   tokenizer=__UpperCAmelCase  ,   labels=__UpperCAmelCase  ,   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				:					Union[str, Any]		  =			trainer.predict(test_dataset.get_dataset() )
    SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Tuple		  =			align_predictions(__UpperCAmelCase  ,   __UpperCAmelCase )
    SCREAMING_SNAKE_CASE				:					Any		  =			classification_report(__UpperCAmelCase  ,   __UpperCAmelCase )

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

    SCREAMING_SNAKE_CASE				:					List[str]		  =			os.path.join(training_args.output_dir  ,   "test_results.txt" )

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

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

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

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

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

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

  return results


if __name__ == "__main__":
   main()











from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 							a_  (		datasets.BuilderConfig				):
						UpperCamelCase__      :						Optional[Callable]				       =None
						UpperCamelCase__      :						Optional[dict]				       =None
						UpperCamelCase__      :						Optional[datasets.Features]				       =None

						def 	__a       (    self	:List[str])		->					List[str]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase_     							=     {}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						List[str]				       =GeneratorConfig

						def 	__a       (    self	:List[str])		->					List[Any]:
												return datasets.DatasetInfo(features=self.config.features)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:List[str])		->					Optional[Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs=self.config.gen_kwargs)]




						def 	__a       (    self	:Optional[int]   ,						**_lowercase	:str)		->					Union[str, Any]:
												for idx, ex in enumerate(self.config.generator(**_lowercase)):
																		yield idx, ex





