





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()


a__			:    Any			      =		logging.getLogger(__name__)





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





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




def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
     A_      ,						A_      ,						A_					      =					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.'''							)

     A_					      =					import_module('''tasks'''							)

     try:
          A_					      =					getattr(_UpperCAmelCase     ,	model_args.task_type							)
          A_					      =					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
     A_					      =					token_classification_task.get_labels(data_args.labels							)
     A_					      =					dict(enumerate(_UpperCAmelCase							)							)
     A_					      =					len(_UpperCAmelCase							)

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

     A_					      =					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     ,	)
     A_					      =					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():
          A_					      =					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
     A_					      =					(
         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
     )
     A_					      =					(
         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(_UpperCAmelCase       :np.ndarray     ,	_UpperCAmelCase       :np.ndarray							) -> Tuple[List[int], List[int]]:
          A_					      =					np.argmax(_UpperCAmelCase     ,	axis=2							)
          A_      ,						A_					      =					preds.shape
          A_					      =					[[] for _ in range(_UpperCAmelCase							)]
          A_					      =					[[] 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(_UpperCAmelCase       :EvalPrediction							) -> Dict:
          A_      ,						A_					      =					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
     A_					      =					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
     A_					      =					{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''							)

          A_					      =					trainer.evaluate()
          A_					      =					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:
          A_					      =					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     ,	)

          A_      ,						A_      ,						A_					      =					trainer.predict(test_dataset.get_dataset()							)
          A_      ,						A_					      =					align_predictions(_UpperCAmelCase     ,	_UpperCAmelCase							)
          A_					      =					classification_report(_UpperCAmelCase     ,	_UpperCAmelCase							)

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

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

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

          # Save predictions
          A_					      =					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:
                    A_					      =					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]:
                              A_					      =					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 sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a__			:    List[str]			      =		load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
a__      ,							a__      ,							a__      ,							a__			:    Any			      =		train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

a__			:    List[Any]			      =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a__			:    Optional[Any]			      =		[[1, 2, 1, 4], [2, 3, 4, 5]]

a__			:    Union[str, Any]			      =		knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris['target_names'][prediction]}'''
)



