


'''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()


__lowercase:		List[str]    						=  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 							SCREAMING_SNAKE_CASE__(		)		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    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." )

   UpperCamelCase__			    =    import_module("tasks" )

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

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

   UpperCamelCase__			    =    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     ,		)
   UpperCamelCase__			    =    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():
      UpperCamelCase__			    =    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
   UpperCamelCase__			    =    (
       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
   )
   UpperCamelCase__			    =    (
       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]]:
      UpperCamelCase__			    =    np.argmax(_UpperCamelCase     ,		axis=2 )
      UpperCamelCase__    ,		UpperCamelCase__			    =    preds.shape
      UpperCamelCase__			    =    [[] for _ in range(_UpperCamelCase )]
      UpperCamelCase__			    =    [[] for _ in range(_UpperCamelCase )]

      for i in range(_UpperCamelCase ):
         for j in range(_UpperCamelCase ):
            if label_ids[i, j] != -1_00:
               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:
      UpperCamelCase__    ,		UpperCamelCase__			    =    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
   UpperCamelCase__			    =    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
   UpperCamelCase__			    =    {}
   if training_args.do_eval:
      logger.info("*** Evaluate ***" )

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

      UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    trainer.predict(test_dataset.get_dataset() )
      UpperCamelCase__    ,		UpperCamelCase__			    =    align_predictions(_UpperCamelCase     ,		_UpperCamelCase )
      UpperCamelCase__			    =    classification_report(_UpperCamelCase     ,		_UpperCamelCase )

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

      UpperCamelCase__			    =    os.path.join(training_args.output_dir     ,		"test_results.txt" )

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

      # Save predictions
      UpperCamelCase__			    =    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:
            UpperCamelCase__			    =    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]:
                  UpperCamelCase__			    =    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()
"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



