

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()


_lowerCamelCase								=logging.getLogger(__name__)



@dataclass
class 		a_	:



      """simple docstring"""







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







@dataclass
class 		a_	:



      """simple docstring"""







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







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_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.'      )

     SCREAMING_SNAKE_CASE										=import_module('tasks'      )

     try:
          SCREAMING_SNAKE_CASE										=getattr(A_,   model_args.task_type      )
          SCREAMING_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,   )
     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',   A_      )

     # Prepare Token Classification task
     SCREAMING_SNAKE_CASE										=token_classification_task.get_labels(data_args.labels      )
     SCREAMING_SNAKE_CASE										=dict(enumerate(A_      )      )
     SCREAMING_SNAKE_CASE										=len(A_      )

     # 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										=AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,   num_labels=A_,   idalabel=A_,   labelaid={label: i for i, label in enumerate(A_      )},   cache_dir=model_args.cache_dir,   )
     SCREAMING_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,   )

     with training_args.strategy.scope():
          SCREAMING_SNAKE_CASE										=TFAutoModelForTokenClassification.from_pretrained(
              model_args.model_name_or_path,   from_pt=bool('.bin' in model_args.model_name_or_path      ),   config=A_,   cache_dir=model_args.cache_dir,   )

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

          for i in range(A_      ):
               for j in range(A_      ):
                    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(lowerCAmelCase_      ) -> Dict:
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=align_predictions(p.predictions,   p.label_ids      )

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

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

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

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

          SCREAMING_SNAKE_CASE										=trainer.evaluate()
          SCREAMING_SNAKE_CASE										=os.path.join(training_args.output_dir,   'eval_results.txt'      )

          with open(A_,   'w'      ) as writer:
               logger.info('***** Eval results *****'      )

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

               results.update(A_      )

          # Predict
     if training_args.do_predict:
          SCREAMING_SNAKE_CASE										=TFTokenClassificationDataset(
              token_classification_task=A_,   data_dir=data_args.data_dir,   tokenizer=A_,   labels=A_,   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										=trainer.predict(test_dataset.get_dataset()      )
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=align_predictions(A_,   A_      )
          SCREAMING_SNAKE_CASE										=classification_report(A_,   A_      )

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

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

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

          # Save predictions
          SCREAMING_SNAKE_CASE										=os.path.join(training_args.output_dir,   'test_predictions.txt'      )

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

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

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

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

     return results


if __name__ == "__main__":
 main()




import decimal







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=0
     SCREAMING_SNAKE_CASE										=decimal.Context(prec=105      )
     for i in range(2,   100      ):
          SCREAMING_SNAKE_CASE										=decimal.Decimal(lowerCAmelCase_      )
          SCREAMING_SNAKE_CASE										=number.sqrt(lowerCAmelCase_      )
          if len(str(lowerCAmelCase_      )      ) > 1:
               answer += int(str(lowerCAmelCase_      )[0]      )
               SCREAMING_SNAKE_CASE										=str(lowerCAmelCase_      )[2:101]
               answer += sum(int(lowerCAmelCase_      ) for x in sqrt_number_str      )
     return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(f'{solution() = }')

