

'''simple docstring'''




import re


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  UpperCAmelCase    				=						re.compile(
      R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$'    )

  return bool(re.search(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
  _a					:			Any							=    '0094702343221'

  print(is_sri_lankan_phone_number(phone))





'''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


_a					:			List[Any]							=    logging.getLogger(__name__)



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							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=a			,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  __lowerCAmelCase       :			bool     	= field(default=a			,							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=a			,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,							)




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= field(
      metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
  __lowerCAmelCase       :			Optional[str]     	= field(
      default=a			,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}			,							)
  __lowerCAmelCase       :			int     	= field(
      default=1_28			,							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=a			,							metadata={"help": "Overwrite the cached training and evaluation sets"}			)




def    lowerCamelCase__		(    ):
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  UpperCAmelCase    				=						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.
    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
  else:
    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(SCREAMING_SNAKE_CASE							, 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 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
  UpperCAmelCase    				=						token_classification_task.get_labels(data_args.labels    )
  UpperCAmelCase    				=						dict(enumerate(SCREAMING_SNAKE_CASE    )    )
  UpperCAmelCase    				=						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.

  UpperCAmelCase    				=						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							, )
  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							, )
  UpperCAmelCase    				=						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
  UpperCAmelCase    				=						(
      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
  )
  UpperCAmelCase    				=						(
      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   : np.ndarray							, SCREAMING_SNAKE_CASE   : np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase    				=						np.argmax(SCREAMING_SNAKE_CASE							, axis=2    )

    UpperCAmelCase				,   UpperCAmelCase    				=						preds.shape

    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]
    UpperCAmelCase    				=						[[] 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   : EvalPrediction    ) -> Dict:
    UpperCAmelCase				,   UpperCAmelCase    				=						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
  UpperCAmelCase    				=						DataCollatorWithPadding(SCREAMING_SNAKE_CASE							, pad_to_multiple_of=8    ) if training_args.fpaa else None

  # Initialize our Trainer
  UpperCAmelCase    				=						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
  UpperCAmelCase    				=						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'    )

    UpperCAmelCase    				=						trainer.evaluate()

    UpperCAmelCase    				=						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:
    UpperCAmelCase    				=						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							, )

    UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						trainer.predict(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()


