



"""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__      =    logging.getLogger(__name__)






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






@dataclass
class 	__snake_case	:
  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=_lowercase  ,					metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}  ,					)
  snake_case__    :       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."
          )
      }  ,					)
  snake_case__    :       bool        =						field(
      default=_lowercase  ,					metadata={"help": "Overwrite the cached training and evaluation sets"})






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			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						:    Optional[int]         =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              _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(A_,    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''',    A_						)

       # Set seed
       set_seed(training_args.seed						)

       # Prepare CONLL-2003 task
       _lowerCamelCase						:    List[str]         =			token_classification_task.get_labels(data_args.labels						)
       _lowerCamelCase						:    Dict[int, str]         =			dict(enumerate(A_						)						)
       _lowerCamelCase						:    List[Any]         =			len(A_						)

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

       _lowerCamelCase						:    Any         =			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,    )
       _lowerCamelCase						:    List[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						:    Any         =			AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path						),    config=A_,    cache_dir=model_args.cache_dir,    )

       # Get datasets
       _lowerCamelCase						:    List[str]         =			(
           TokenClassificationDataset(
               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
       )
       _lowerCamelCase						:    str         =			(
           TokenClassificationDataset(
               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(A_ :		np.ndarray,    A_ :		np.ndarray						) -> Tuple[List[int], List[int]]:
              _lowerCamelCase						:    Optional[int]         =			np.argmax(A_,    axis=2						)

              _lowerCamelCase						:    Tuple         =			preds.shape

              _lowerCamelCase						:    List[Any]         =			[[] for _ in range(A_						)]
              _lowerCamelCase						:    List[Any]         =			[[] for _ in range(A_						)]

              for i in range(A_						):
                     for j in range(A_						):
                            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(A_ :		EvalPrediction						) -> Dict:
              _lowerCamelCase						:    List[str]         =			align_predictions(p.predictions,    p.label_ids						)
              return {
                  "accuracy_score": accuracy_score(A_,    A_						),
                  "precision": precision_score(A_,    A_						),
                  "recall": recall_score(A_,    A_						),
                  "f1": fa_score(A_,    A_						),
              }

       # Data collator
       _lowerCamelCase						:    List[str]         =			DataCollatorWithPadding(A_,    pad_to_multiple_of=8						) if training_args.fpaa else None

       # Initialize our Trainer
       _lowerCamelCase						:    Any         =			Trainer(
           model=A_,    args=A_,    train_dataset=A_,    eval_dataset=A_,    compute_metrics=A_,    data_collator=A_,    )

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

              _lowerCamelCase						:    Any         =			trainer.evaluate()

              _lowerCamelCase						:    str         =			os.path.join(training_args.output_dir,    '''eval_results.txt'''						)
              if trainer.is_world_process_zero():
                     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:
              _lowerCamelCase						:    Any         =			TokenClassificationDataset(
                  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,    )

              _lowerCamelCase						:    List[str]         =			trainer.predict(A_						)
              _lowerCamelCase						:    List[str]         =			align_predictions(A_,    A_						)

              _lowerCamelCase						:    List[str]         =			os.path.join(training_args.output_dir,    '''test_results.txt'''						)
              if trainer.is_world_process_zero():
                     with open(A_,    '''w'''						) as writer:
                            for key, value in metrics.items():
                                   logger.info('''  %s = %s''',    A_,    A_						)
                                   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(A_,    '''w'''						) as writer:
                            with open(os.path.join(data_args.data_dir,    '''test.txt'''						),    '''r'''						) as f:
                                   token_classification_task.write_predictions_to_file(A_,    A_,    A_						)

       return results






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       main()


if __name__ == "__main__":
   main()







"""simple docstring"""






from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			[randint(-10_00,    10_00						) for i in range(10						)]
       _lowerCamelCase						:    Tuple         =			randint(-50_00,    50_00						)
       return (arr, r)


lowerCAmelCase__      =    make_dataset()






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       for triplet in permutations(A_,    3						):
              if sum(A_						) == target:
                     return tuple(sorted(A_						)						)
       return (0, 0, 0)






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       arr.sort()
       _lowerCamelCase						:    Optional[Any]         =			len(A_						)
       for i in range(n - 1						):
              _lowerCamelCase      ,   _lowerCamelCase						:    int         =			i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       _lowerCamelCase						:    List[Any]         =			'''
triplet_sum1(*dataset)
'''
       _lowerCamelCase						:    str         =			'''
triplet_sum2(*dataset)
'''
       _lowerCamelCase						:    Optional[int]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       _lowerCamelCase						:    Union[str, Any]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       return (min(A_						), min(A_						))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   lowerCAmelCase__      =    solution_times()
   print(F"""The time for naive implementation is {times[0]}.""")
   print(F"""The time for optimized implementation is {times[1]}.""")


