'''simple docstring'''




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCAmelCase     							=logging.getLogger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    return (preds == labels).mean()




@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=UpperCAmelCase__							,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
     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 name of the task to train on: " + ", ".join(processors.keys() )} )
     lowerCamelCase  :     str							=field(metadata={"help": "Should contain the data files for the task."} )
     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 (							)	->    List[Any]:
    # 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)				)
    __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.'''				)

    # 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				)

    try:
        __lowerCamelCase		=  processors[data_args.task_name]()
        __lowerCamelCase		=  processor.get_labels()
        __lowerCamelCase		=  len(UpperCamelCase__				)
    except KeyError:
        raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

    # 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__				,  finetuning_task=data_args.task_name				,  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				,  )
    __lowerCamelCase		=  AutoModelForMultipleChoice.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		=  (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir				,  tokenizer=UpperCamelCase__				,  task=data_args.task_name				,  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		=  (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir				,  tokenizer=UpperCamelCase__				,  task=data_args.task_name				,  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 compute_metrics(UpperCamelCase__				) -> Dict:
        __lowerCamelCase		=  np.argmax(p.predictions				,  axis=1				)
        return {"acc": simple_accuracy(UpperCamelCase__				,  p.label_ids				)}

    # 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_master():
            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_master():
            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__				)

    return results






def __lowerCAmelCase (							UpperCamelCase__				)	->    List[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__  ,					lowerCamelCase__:      int			  =9, 14  # noqa: F841
  lowerCamelCase__:      List[Any]			  =[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowerCamelCase__:      List[str]			  =defaultdict(__a		)
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]		)
    adjancency[nodea].append([nodea, cost]		)

  lowerCamelCase__:      List[str]			  =mst(__a		)

  lowerCamelCase__:      Union[str, Any]			  =[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowerCamelCase__:      Optional[int]			  =tuple(answer[:2]		)
    lowerCamelCase__:      List[Any]			  =tuple(edge[::-1]		)
    assert edge in result or reverse in result

