


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_           =	logging.getLogger(__name__)


@dataclass
class        A_					:




      '''simple docstring'''


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



@dataclass
class        A_					:




      '''simple docstring'''


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



def       UpperCamelCase__     (  ):
 # 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.
 __lowerCamelCase		:							Optional[int]							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
 __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase		:							Optional[int]							=						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.'						)

 __lowerCamelCase		:							str							=						import_module('tasks'						)

 try:
  __lowerCamelCase		:							Any							=						getattr(SCREAMING_SNAKE_CASE__			,       model_args.task_type						)
  __lowerCamelCase		:							TokenClassificationTask							=						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'			,       SCREAMING_SNAKE_CASE__						)

 # Prepare Token Classification task
 __lowerCamelCase		:							List[Any]							=						token_classification_task.get_labels(data_args.labels						)
 __lowerCamelCase		:							Dict[int, str]							=						dict(enumerate(SCREAMING_SNAKE_CASE__						)						)
 __lowerCamelCase		:							Union[str, Any]							=						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.

 __lowerCamelCase		:							List[str]							=						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			,       )
 __lowerCamelCase		:							Tuple							=						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():
  __lowerCamelCase		:							Any							=						TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,       from_pt=bool('.bin' in model_args.model_name_or_path						)			,       config=SCREAMING_SNAKE_CASE__			,       cache_dir=model_args.cache_dir			,       )

 # Get datasets
 __lowerCamelCase		:							List[str]							=						(
     TFTokenClassificationDataset(
         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
 )
 __lowerCamelCase		:							str							=						(
     TFTokenClassificationDataset(
         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]]:
  __lowerCamelCase		:							Dict							=						np.argmax(SCREAMING_SNAKE_CASE__			,       axis=2						)
  __lowerCamelCase					,     __lowerCamelCase		:							Any							=						preds.shape
  __lowerCamelCase		:							Tuple							=						[[] for _ in range(SCREAMING_SNAKE_CASE__						)]
  __lowerCamelCase		:							Dict							=						[[] 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] != -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(SCREAMING_SNAKE_CASE__						) -> Dict:
  __lowerCamelCase					,     __lowerCamelCase		:							Any							=						align_predictions(p.predictions			,       p.label_ids						)

  return {
      "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__						),
  }

 # Initialize our Trainer
 __lowerCamelCase		:							List[str]							=						TFTrainer(
     model=SCREAMING_SNAKE_CASE__			,       args=SCREAMING_SNAKE_CASE__			,       train_dataset=train_dataset.get_dataset() if train_dataset else None			,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,       compute_metrics=SCREAMING_SNAKE_CASE__			,       )

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

 # Evaluation
 __lowerCamelCase		:							List[str]							=						{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'						)

  __lowerCamelCase		:							str							=						trainer.evaluate()
  __lowerCamelCase		:							List[str]							=						os.path.join(training_args.output_dir			,       'eval_results.txt'						)

  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:
  __lowerCamelCase		:							Tuple							=						TFTokenClassificationDataset(
      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			,       )

  __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase		:							Optional[Any]							=						trainer.predict(test_dataset.get_dataset()						)
  __lowerCamelCase					,     __lowerCamelCase		:							Tuple							=						align_predictions(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Optional[int]							=						classification_report(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						)

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

  __lowerCamelCase		:							Optional[int]							=						os.path.join(training_args.output_dir			,       'test_results.txt'						)

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

  # Save predictions
  __lowerCamelCase		:							Optional[Any]							=						os.path.join(training_args.output_dir			,       'test_predictions.txt'						)

  with open(SCREAMING_SNAKE_CASE__			,       'w'						) as writer:
   with open(os.path.join(data_args.data_dir			,       'test.txt'						)			,       'r'						) as f:
    __lowerCamelCase		:							Union[str, Any]							=						0

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

      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      __lowerCamelCase		:							Union[str, Any]							=						line.split()[0] + ' ' + preds_list[example_id].pop(0						) + '\n'

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

 return results


if __name__ == "__main__":
       main()





def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__ = 100						):
 __lowerCamelCase		:							List[Any]							=						[[0 for _ in range(SCREAMING_SNAKE_CASE__						)] for _ in range(m + 1						)]
 for i in range(m + 1						):
  __lowerCamelCase		:							Any							=						1

 for n in range(m + 1						):
  for k in range(1			,       SCREAMING_SNAKE_CASE__						):
   memo[n][k] += memo[n][k - 1]
   if n > k:
    memo[n][k] += memo[n - k - 1][k]

 return memo[m][m - 1] - 1


if __name__ == "__main__":
       print(solution(int(input('Enter a number: ').strip())))

