





from __future__ import annotations

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



def A_     (		)  ->		tuple[list[int], int]:
       a__						:     Tuple			=    [randint(-1000		,				1000    ) for i in range(10    )]
       a__						:     Dict			=    randint(-5000		,				5000    )
       return (arr, r)


lowercase				:	Dict      						=  make_dataset()



def A_     (		A__		,				A__    )  ->		tuple[int, ...]:
       for triplet in permutations(A__		,				3    ):
              if sum(A__    ) == target:
                     return tuple(sorted(A__    )    )
       return (0, 0, 0)



def A_     (		A__		,				A__    )  ->		tuple[int, int, int]:
       arr.sort()
       a__						:     Optional[Any]			=    len(A__    )
       for i in range(n - 1    ):
              a__						:     Dict			=    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 A_     (		)  ->		tuple[float, float]:
       a__						:     int			=    '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
       a__						:     List[Any]			=    '\ntriplet_sum1(*dataset)\n'
       a__						:     Optional[Any]			=    '\ntriplet_sum2(*dataset)\n'
       a__						:     Union[str, Any]			=    repeat(setup=A__		,				stmt=A__		,				repeat=5		,				number=1_0000    )
       a__						:     Optional[int]			=    repeat(setup=A__		,				stmt=A__		,				repeat=5		,				number=1_0000    )
       return (min(A__    ), min(A__    ))


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

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









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


lowercase				:	Union[str, Any]      						=  logging.getLogger(__name__)





@dataclass
class 			A__   :




   """simple docstring"""



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





@dataclass
class 			A__   :




   """simple docstring"""



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



def A_     (		)  ->		Dict:
       # 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.

       a__						:     List[str]			=    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.
              a__			,							a__			,							a__						:     int			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a__			,							a__			,							a__						:     List[Any]			=    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.'    )

       a__						:     Optional[Any]			=    import_module('tasks'    )
       try:
              a__						:     List[Any]			=    getattr(A__		,				model_args.task_type    )
              a__						:     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
       a__						:     Tuple			=    token_classification_task.get_labels(data_args.labels    )
       a__						:     Dict[int, str]			=    dict(enumerate(A__    )    )
       a__						:     Union[str, 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.

       a__						:     Optional[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		,				)
       a__						:     str			=    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		,				)
       a__						:     List[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
       a__						:     int			=    (
           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
       )
       a__						:     Optional[int]			=    (
           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__		,				A__    ) -> Tuple[List[int], List[int]]:
              a__						:     Union[str, Any]			=    np.argmax(A__		,				axis=2    )

              a__			,							a__						:     Dict			=    preds.shape

              a__						:     Union[str, Any]			=    [[] for _ in range(A__    )]
              a__						:     Optional[int]			=    [[] 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__    ) -> Dict:
              a__			,							a__						:     Union[str, Any]			=    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
       a__						:     Union[str, Any]			=    DataCollatorWithPadding(A__		,				pad_to_multiple_of=8    ) if training_args.fpaa else None

       # Initialize our Trainer
       a__						:     List[str]			=    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
       a__						:     Any			=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'    )

              a__						:     Optional[Any]			=    trainer.evaluate()

              a__						:     List[Any]			=    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:
              a__						:     Optional[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		,				)

              a__			,							a__			,							a__						:     Any			=    trainer.predict(A__    )
              a__			,							a__						:     Union[str, Any]			=    align_predictions(A__		,				A__    )

              a__						:     Optional[int]			=    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
              a__						:     Tuple			=    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 A_     (		A__    )  ->		Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()


