


"""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


snake_case__		:     Tuple    				=				logging.getLogger(__name__)



@dataclass
class 		snake_case_:
     __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 		snake_case_:
     __UpperCamelCase  =					field(
         metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					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						(						):
      # 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					:	Dict       =   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.
            lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            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					:	int       =   import_module('''tasks'''			)
      try:
            lowerCAmelCase					:	Optional[Any]       =   getattr(_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 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''' ,   _snake_case			)

      # Set seed
      set_seed(training_args.seed			)

      # Prepare CONLL-2003 task
      lowerCAmelCase					:	str       =   token_classification_task.get_labels(data_args.labels			)
      lowerCAmelCase					:	Dict[int, str]       =   dict(enumerate(_snake_case			)			)
      lowerCAmelCase					:	str       =   len(_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					:	Union[str, Any]       =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path ,   num_labels=_snake_case ,   idalabel=_snake_case ,   labelaid={label: i for i, label in enumerate(_snake_case			)} ,   cache_dir=model_args.cache_dir ,   )
      lowerCAmelCase					:	Any       =   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 ,   )
      lowerCAmelCase					:	int       =   AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,   config=_snake_case ,   cache_dir=model_args.cache_dir ,   )

      # Get datasets
      lowerCAmelCase					:	Any       =   (
          TokenClassificationDataset(
              token_classification_task=_snake_case ,   data_dir=data_args.data_dir ,   tokenizer=_snake_case ,   labels=_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					:	Dict       =   (
          TokenClassificationDataset(
              token_classification_task=_snake_case ,   data_dir=data_args.data_dir ,   tokenizer=_snake_case ,   labels=_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(_snake_case				:			np.ndarray ,   _snake_case				:			np.ndarray			) -> Tuple[List[int], List[int]]:
            lowerCAmelCase					:	List[str]       =   np.argmax(_snake_case ,   axis=2			)

            lowerCAmelCase,      lowerCAmelCase					:	str       =   preds.shape

            lowerCAmelCase					:	Union[str, Any]       =   [[] for _ in range(_snake_case			)]
            lowerCAmelCase					:	Optional[Any]       =   [[] for _ in range(_snake_case			)]

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

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

      # Initialize our Trainer
      lowerCAmelCase					:	Any       =   Trainer(
          model=_snake_case ,   args=_snake_case ,   train_dataset=_snake_case ,   eval_dataset=_snake_case ,   compute_metrics=_snake_case ,   data_collator=_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
      lowerCAmelCase					:	Tuple       =   {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''			)

            lowerCAmelCase					:	Union[str, Any]       =   trainer.evaluate()

            lowerCAmelCase					:	List[Any]       =   os.path.join(training_args.output_dir ,   '''eval_results.txt'''			)
            if trainer.is_world_process_zero():
                  with open(_snake_case ,   '''w'''			) as writer:
                        logger.info('''***** Eval results *****'''			)
                        for key, value in result.items():
                              logger.info('''  %s = %s''' ,   _snake_case ,   _snake_case			)
                              writer.write('''%s = %s\n''' % (key, value)			)

                  results.update(_snake_case			)

    # Predict
      if training_args.do_predict:
            lowerCAmelCase					:	List[Any]       =   TokenClassificationDataset(
                token_classification_task=_snake_case ,   data_dir=data_args.data_dir ,   tokenizer=_snake_case ,   labels=_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					:	int       =   trainer.predict(_snake_case			)
            lowerCAmelCase,      lowerCAmelCase					:	Optional[Any]       =   align_predictions(_snake_case ,   _snake_case			)

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

        # Save predictions
            lowerCAmelCase					:	Any       =   os.path.join(training_args.output_dir ,   '''test_predictions.txt'''			)
            if trainer.is_world_process_zero():
                  with open(_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(_snake_case ,   _snake_case ,   _snake_case			)

      return results


def      _snake_case						(						_snake_case				:			Optional[int]			):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()






"""simple docstring"""





def      _snake_case						(						_snake_case				:			float ,   _snake_case				:			list[float]			):
      if discount_rate < 0:
            raise ValueError('''Discount rate cannot be negative'''			)
      if not cash_flows:
            raise ValueError('''Cash flows list cannot be empty'''			)
      lowerCAmelCase					:	List[str]       =   sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_snake_case			)			)
      return round(_snake_case ,   ndigits=2			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


