
"""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


_a		       =							logging.getLogger(__name__)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   return (preds == labels).mean()


@dataclass
class A_	:




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




@dataclass
class A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
  SCREAMING_SNAKE_CASE__			:						str       =		field(metadata={"""help""": """Should contain the data files for the task."""}      )
  SCREAMING_SNAKE_CASE__			:						int       =		field(
      default=128				,metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )







def 	__a     (		):
   # 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.

   UpperCAmelCase_					:					int			     =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    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",		__lowerCamelCase  )

   # Set seed
   set_seed(training_args.seed  )

   try:
      UpperCAmelCase_					:					Optional[int]			     =    processors[data_args.task_name]()
      UpperCAmelCase_					:					str			     =    processor.get_labels()
      UpperCAmelCase_					:					str			     =    len(__lowerCamelCase  )
   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_					:					Optional[int]			     =    AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,		num_labels=__lowerCamelCase,		finetuning_task=data_args.task_name,		cache_dir=model_args.cache_dir,		)
   UpperCAmelCase_					:					List[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,		)
   UpperCAmelCase_					:					Optional[int]			     =    AutoModelForMultipleChoice.from_pretrained(
       model_args.model_name_or_path,		from_tf=bool(".ckpt" in model_args.model_name_or_path  ),		config=__lowerCamelCase,		cache_dir=model_args.cache_dir,		)

   # Get datasets
   UpperCAmelCase_					:					Dict			     =    (
       MultipleChoiceDataset(
           data_dir=data_args.data_dir,		tokenizer=__lowerCamelCase,		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_					:					List[str]			     =    (
       MultipleChoiceDataset(
           data_dir=data_args.data_dir,		tokenizer=__lowerCamelCase,		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(__lowerCamelCase  ) -> Dict:
      UpperCAmelCase_					:					int			     =    np.argmax(p.predictions,		axis=1  )
      return {"acc": simple_accuracy(__lowerCamelCase,		p.label_ids  )}

   # Data collator
   UpperCAmelCase_					:					List[Any]			     =    DataCollatorWithPadding(__lowerCamelCase,		pad_to_multiple_of=8  ) if training_args.fpaa else None

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

   # 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_					:					Tuple			     =    {}
   if training_args.do_eval:
      logger.info("*** Evaluate ***"  )

      UpperCAmelCase_					:					List[Any]			     =    trainer.evaluate()

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

            results.update(__lowerCamelCase  )

   return results







def 	__a     (		__lowerCamelCase  ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()









from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   if depth < 0:
      raise ValueError('Depth cannot be less than 0'	)

   if len(lowerCAmelCase_	) == 0:
      raise ValueError('Scores cannot be empty'	)

   if depth == height:
      return scores[node_index]

   if is_max:
      return max(
          minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

   return min(
       minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

