'''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


__UpperCAmelCase     							=logging.getLogger(__name__)




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




@dataclass
class     a__      :
     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=UpperCAmelCase__							,   metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							,   )
     lowerCamelCase  :     int							=field(
         default=1_2_8							,   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=UpperCAmelCase__							,   metadata={"help": "Overwrite the cached training and evaluation sets"} )






def __lowerCAmelCase (							)	->    int:
    # 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		=  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		=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  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		=  import_module('''tasks'''				)
    try:
        __lowerCamelCase		=  getattr(UpperCamelCase__				,  model_args.task_type				)
        __lowerCamelCase		=  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'''				,  UpperCamelCase__				)

    # Set seed
    set_seed(training_args.seed				)

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

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

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

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

        __lowerCamelCase	,  __lowerCamelCase		=  preds.shape

        __lowerCamelCase		=  [[] for _ in range(UpperCamelCase__				)]
        __lowerCamelCase		=  [[] for _ in range(UpperCamelCase__				)]

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

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

    # Initialize our Trainer
    __lowerCamelCase		=  Trainer(
        model=UpperCamelCase__				,  args=UpperCamelCase__				,  train_dataset=UpperCamelCase__				,  eval_dataset=UpperCamelCase__				,  compute_metrics=UpperCamelCase__				,  data_collator=UpperCamelCase__				,  )

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

        __lowerCamelCase		=  trainer.evaluate()

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

            results.update(UpperCamelCase__				)

    # Predict
    if training_args.do_predict:
        __lowerCamelCase		=  TokenClassificationDataset(
            token_classification_task=UpperCamelCase__				,  data_dir=data_args.data_dir				,  tokenizer=UpperCamelCase__				,  labels=UpperCamelCase__				,  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		=  trainer.predict(UpperCamelCase__				)
        __lowerCamelCase	,  __lowerCamelCase		=  align_predictions(UpperCamelCase__				,  UpperCamelCase__				)

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

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

    return results






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()




'''simple docstring'''

from __future__ import annotations





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  2
 lowerCamelCase_      =  []
 while i * i <= n:
  if n % i:
   i += 1
  else:
   n //= i
   factors.append(UpperCAmelCase_		)
 if n > 1:
  factors.append(UpperCAmelCase_		)
 return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()



