"""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


__lowerCAmelCase       :			Dict   	=logging.getLogger(__name__)





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






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






def      UpperCAmelCase__			(							)					->       Dict:
      '''simple docstring'''


      lowercase								=      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.
            lowercase				,				lowercase				,				lowercase								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            lowercase				,				lowercase				,				lowercase								=      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."""						)

      lowercase								=      import_module("""tasks"""						)
      try:
            lowercase								=      getattr(lowerCAmelCase__   ,						model_args.task_type						)
            lowercase								=      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"""   ,						lowerCAmelCase__						)

      # Set seed
      set_seed(training_args.seed						)

      # Prepare CONLL-2003 task
      lowercase								=      token_classification_task.get_labels(data_args.labels						)
      lowercase								=      dict(enumerate(lowerCAmelCase__						)						)
      lowercase								=      len(lowerCAmelCase__						)

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

      lowercase								=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,						num_labels=lowerCAmelCase__   ,						idalabel=lowerCAmelCase__   ,						labelaid={label: i for i, label in enumerate(lowerCAmelCase__						)}   ,						cache_dir=model_args.cache_dir   ,						)
      lowercase								=      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   ,						)
      lowercase								=      AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path   ,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)   ,						config=lowerCAmelCase__   ,						cache_dir=model_args.cache_dir   ,						)

      # Get datasets
      lowercase								=      (
          TokenClassificationDataset(
              token_classification_task=lowerCAmelCase__   ,						data_dir=data_args.data_dir   ,						tokenizer=lowerCAmelCase__   ,						labels=lowerCAmelCase__   ,						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
      )
      lowercase								=      (
          TokenClassificationDataset(
              token_classification_task=lowerCAmelCase__   ,						data_dir=data_args.data_dir   ,						tokenizer=lowerCAmelCase__   ,						labels=lowerCAmelCase__   ,						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__	:np.ndarray   ,						lowerCAmelCase__	:np.ndarray						) -> Tuple[List[int], List[int]]:
            lowercase								=      np.argmax(lowerCAmelCase__   ,						axis=2						)

            lowercase				,				lowercase								=      preds.shape

            lowercase								=      [[] for _ in range(lowerCAmelCase__						)]
            lowercase								=      [[] for _ in range(lowerCAmelCase__						)]

            for i in range(lowerCAmelCase__						):
                  for j in range(lowerCAmelCase__						):
                        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(lowerCAmelCase__	:EvalPrediction						) -> Dict:
            lowercase				,				lowercase								=      align_predictions(p.predictions   ,						p.label_ids						)
            return {
                "accuracy_score": accuracy_score(lowerCAmelCase__   ,						lowerCAmelCase__						),
                "precision": precision_score(lowerCAmelCase__   ,						lowerCAmelCase__						),
                "recall": recall_score(lowerCAmelCase__   ,						lowerCAmelCase__						),
                "f1": fa_score(lowerCAmelCase__   ,						lowerCAmelCase__						),
            }

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

      # Initialize our Trainer
      lowercase								=      Trainer(
          model=lowerCAmelCase__   ,						args=lowerCAmelCase__   ,						train_dataset=lowerCAmelCase__   ,						eval_dataset=lowerCAmelCase__   ,						compute_metrics=lowerCAmelCase__   ,						data_collator=lowerCAmelCase__   ,						)

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

            lowercase								=      trainer.evaluate()

            lowercase								=      os.path.join(training_args.output_dir   ,						"""eval_results.txt"""						)
            if trainer.is_world_process_zero():
                  with open(lowerCAmelCase__   ,						"""w"""						) as writer:
                        logger.info("""***** Eval results *****"""						)
                        for key, value in result.items():
                              logger.info("""  %s = %s"""   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
                              writer.write("""%s = %s\n""" % (key, value)						)

                  results.update(lowerCAmelCase__						)

    # Predict
      if training_args.do_predict:
            lowercase								=      TokenClassificationDataset(
                token_classification_task=lowerCAmelCase__   ,						data_dir=data_args.data_dir   ,						tokenizer=lowerCAmelCase__   ,						labels=lowerCAmelCase__   ,						model_type=config.model_type   ,						max_seq_length=data_args.max_seq_length   ,						overwrite_cache=data_args.overwrite_cache   ,						mode=Split.test   ,						)

            lowercase				,				lowercase				,				lowercase								=      trainer.predict(lowerCAmelCase__						)
            lowercase				,				lowercase								=      align_predictions(lowerCAmelCase__   ,						lowerCAmelCase__						)

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

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

      return results


def      UpperCAmelCase__			(							lowerCAmelCase__	:str						)					->       Optional[int]:
      '''simple docstring'''


      main()


if __name__ == "__main__":
       main()




"""simple docstring"""






def      UpperCAmelCase__			(							lowerCAmelCase__	:int = 5_0						)					->       int:
      '''simple docstring'''


      lowercase								=      [1] * (length + 1)

      for row_length in range(3   ,						length + 1						):
            for block_length in range(3   ,						row_length + 1						):
                  for block_start in range(row_length - block_length						):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
       print(F"""{solution() = }""")



