





def  a__    (    lowercase :					int, lowercase :					Tuple, lowercase :					List[Any], lowercase :					Dict				)		->		Union[str, Any]:





     """simple docstring"""

     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               _UpperCamelCase       =	mf_knapsack(i - 1, lowercase, lowercase, lowercase				)
          else:
               _UpperCamelCase       =	max(
                   mf_knapsack(i - 1, lowercase, lowercase, lowercase				), mf_knapsack(i - 1, lowercase, lowercase, j - wt[i - 1]				) + val[i - 1], )
          _UpperCamelCase       =	val
     return f[i][j]







def  a__    (    lowercase :					Dict, lowercase :					int, lowercase :					Optional[int], lowercase :					Dict				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	[[0] * (w + 1) for _ in range(n + 1				)]

     for i in range(1, n + 1				):
          for w_ in range(1, w + 1				):
               if wt[i - 1] <= w_:
                    _UpperCamelCase       =	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]], dp[i - 1][w_]				)
               else:
                    _UpperCamelCase       =	dp[i - 1][w_]

     return dp[n][w_], dp







def  a__    (    lowercase :					List[str], lowercase :					Union[str, Any], lowercase :					List[Any]				)		->		Tuple:





     """simple docstring"""

     if not (isinstance(lowercase, (list, tuple)				) and isinstance(lowercase, (list, tuple)				)):
          raise ValueError(
              '''Both the weights and values vectors must be either lists or tuples'''				)

     _UpperCamelCase       =	len(lowercase				)
     if num_items != len(lowercase				):
          _UpperCamelCase       =	(
              '''The number of weights must be the same as the number of values.\n'''
              F"""But got {num_items} weights and {len(lowercase				)} values"""
          )
          raise ValueError(lowercase				)
     for i in range(lowercase				):
          if not isinstance(wt[i], lowercase				):
               _UpperCamelCase       =	(
                   '''All weights must be integers but got weight of '''
                   F"""type {type(wt[i]				)} at index {i}"""
               )
               raise TypeError(lowercase				)

     _UpperCamelCase       =	knapsack(lowercase, lowercase, lowercase, lowercase				)
     _UpperCamelCase       =	set()
     _construct_solution(lowercase, lowercase, lowercase, lowercase, lowercase				)

     return optimal_val, example_optional_set







def  a__    (    lowercase :					List[str], lowercase :					Optional[Any], lowercase :					int, lowercase :					Union[str, Any], lowercase :					int				)		->		Dict:





     """simple docstring"""

     if i > 0 and j > 0:
          if dp[i - 1][j] == dp[i][j]:
               _construct_solution(lowercase, lowercase, i - 1, lowercase, lowercase				)
          else:
               optimal_set.add(lowercase				)
               _construct_solution(lowercase, lowercase, i - 1, j - wt[i - 1], lowercase				)


if __name__ == "__main__":
   lowercase__						:      Optional[Any]	=    [3, 2, 4, 4]
   lowercase__						:      str	=    [4, 3, 2, 3]
   lowercase__						:      str	=    4
   lowercase__						:      Tuple	=    6
   lowercase__						:      Union[str, Any]	=    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   lowercase__	,       lowercase__						:      int	=    knapsack(w, wt, val, n)
   print(optimal_solution)
   print(mf_knapsack(n, wt, val, w))  # switched the n and w

   # testing the dynamic programming problem with example
   # the optimal subset for the above example are items 3 and 4
   lowercase__	,       lowercase__						:      Tuple	=    knapsack_with_example_solution(w, wt, val)
   assert optimal_solution == 8
   assert optimal_subset == {3, 4}
   print('optimal_value = ', optimal_solution)
   print('An optimal subset corresponding to the optimal value', optimal_subset)










'''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


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _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=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	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''', lowercase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     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.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	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, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path				), config=lowercase, cache_dir=model_args.cache_dir, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, 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
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, 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(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

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

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

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

          _UpperCamelCase       =	trainer.evaluate()

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

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



