
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  :					List[Any]						    =							logging.getLogger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       return (preds == labels).mean()

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






@dataclass
class __snake_case		:
    _a		:						str=				field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()     )}     )
    _a		:						str=				field(metadata={"help": "Should contain the data files for the task."}     )
    _a		:						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."
            )
        }					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Overwrite the cached training and evaluation sets"}     )







def 						_snake_case(  ) ->    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.

       lowercase							:     Any       	=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
       lowercase	,			lowercase	,			lowercase							:     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."""				)

       # 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"""					,	SCREAMING_SNAKE_CASE__				)

       # Set seed
       set_seed(training_args.seed				)

       try:
              lowercase							:     Union[str, Any]       	=      processors[data_args.task_name]()
              lowercase							:     List[Any]       	=      processor.get_labels()
              lowercase							:     Dict       	=      len(SCREAMING_SNAKE_CASE__				)
       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.

       lowercase							:     Any       	=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path					,	num_labels=SCREAMING_SNAKE_CASE__					,	finetuning_task=data_args.task_name					,	cache_dir=model_args.cache_dir					,	)
       lowercase							:     Union[str, 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					,	)
       lowercase							:     Optional[int]       	=      AutoModelForMultipleChoice.from_pretrained(
           model_args.model_name_or_path					,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)					,	config=SCREAMING_SNAKE_CASE__					,	cache_dir=model_args.cache_dir					,	)

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

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

       # Initialize our Trainer
       lowercase							:     List[Any]       	=      Trainer(
           model=SCREAMING_SNAKE_CASE__					,	args=SCREAMING_SNAKE_CASE__					,	train_dataset=SCREAMING_SNAKE_CASE__					,	eval_dataset=SCREAMING_SNAKE_CASE__					,	compute_metrics=SCREAMING_SNAKE_CASE__					,	data_collator=SCREAMING_SNAKE_CASE__					,	)

       # 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
       lowercase							:     List[Any]       	=      {}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""				)

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

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

                            results.update(SCREAMING_SNAKE_CASE__				)

       return results







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()







from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

