





'''simple docstring'''



import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__magic_name__   :						Optional[Any]    =			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=__UpperCamelCase					,    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=__UpperCamelCase					,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
     UpperCAmelCase__       :						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.
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=__UpperCamelCase					,    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(
         default=__UpperCamelCase					,    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					,    )
     UpperCAmelCase__       :						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.'''
             )
         }					,    )
     UpperCAmelCase__       :						bool      =   field(
         default=__UpperCamelCase					,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
  if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _snake_case		,		_snake_case		,		_snake_case     		=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
  else:
    _snake_case		,		_snake_case		,		_snake_case     		=				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."       )

  _snake_case     		=				import_module("tasks"       )
  try:
    _snake_case     		=				getattr(SCREAMING_SNAKE_CASE__					,	model_args.task_type       )
    _snake_case     		=				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 if training_args.local_rank in [-1, 0] else logging.WARN					,	)
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,	training_args.local_rank					,	training_args.device					,	training_args.n_gpu					,	bool(training_args.local_rank != -1       )					,	training_args.fpaa					,	)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank       ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"					,	SCREAMING_SNAKE_CASE__       )

  # Set seed
  set_seed(training_args.seed       )

  # Prepare CONLL-2003 task
  _snake_case     		=				token_classification_task.get_labels(data_args.labels       )
  _snake_case     		=				dict(enumerate(SCREAMING_SNAKE_CASE__       )       )
  _snake_case     		=				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.

  _snake_case     		=				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					,	)
  _snake_case     		=				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					,	)
  _snake_case     		=				AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path					,	from_tf=bool(".ckpt" in model_args.model_name_or_path       )					,	config=SCREAMING_SNAKE_CASE__					,	cache_dir=model_args.cache_dir					,	)

  # Get datasets
  _snake_case     		=				(
      TokenClassificationDataset(
          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
  )
  _snake_case     		=				(
      TokenClassificationDataset(
          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]]:
    _snake_case     		=				np.argmax(SCREAMING_SNAKE_CASE__					,	axis=2       )

    _snake_case		,		_snake_case     		=				preds.shape

    _snake_case     		=				[[] for _ in range(SCREAMING_SNAKE_CASE__       )]
    _snake_case     		=				[[] 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] != nn.CrossEntropyLoss().ignore_index:
          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:
    _snake_case		,		_snake_case     		=				align_predictions(p.predictions					,	p.label_ids       )
    return {
        "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ),
        "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__       ),
    }

  # Data collator
  _snake_case     		=				DataCollatorWithPadding(SCREAMING_SNAKE_CASE__					,	pad_to_multiple_of=8       ) if training_args.fpaa else None

  # Initialize our Trainer
  _snake_case     		=				Trainer(
      model=SCREAMING_SNAKE_CASE__					,	args=SCREAMING_SNAKE_CASE__					,	train_dataset=SCREAMING_SNAKE_CASE__					,	eval_dataset=SCREAMING_SNAKE_CASE__					,	compute_metrics=SCREAMING_SNAKE_CASE__					,	data_collator=SCREAMING_SNAKE_CASE__					,	)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_process_zero():
      tokenizer.save_pretrained(training_args.output_dir       )

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

    _snake_case     		=				trainer.evaluate()

    _snake_case     		=				os.path.join(training_args.output_dir					,	"eval_results.txt"       )
    if trainer.is_world_process_zero():
      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:
    _snake_case     		=				TokenClassificationDataset(
        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					,	)

    _snake_case		,		_snake_case		,		_snake_case     		=				trainer.predict(SCREAMING_SNAKE_CASE__       )
    _snake_case		,		_snake_case     		=				align_predictions(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				os.path.join(training_args.output_dir					,	"test_results.txt"       )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE__					,	"w"       ) as writer:
        for key, value in metrics.items():
          logger.info("  %s = %s"					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
          writer.write("%s = %s\n" % (key, value)       )

        # Save predictions
    _snake_case     		=				os.path.join(training_args.output_dir					,	"test_predictions.txt"       )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE__					,	"w"       ) as writer:
        with open(os.path.join(data_args.data_dir					,	"test.txt"       )					,	"r"       ) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  return results




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  main()


if __name__ == "__main__":
 main()









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


