


'''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


__snake_case    						=logging.getLogger(__name__)





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict				):
   return (preds == labels).mean()

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




@dataclass
class 		UpperCAmelCase_      :
     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=128       ,	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=__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.

   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'	, lowerCamelCase				)

   # Set seed
   set_seed(training_args.seed				)

   try:
      lowerCAmelCase     =			processors[data_args.task_name]()
      lowerCAmelCase     =			processor.get_labels()
      lowerCAmelCase     =			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.

   lowerCAmelCase     =			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	, )
   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=lowerCamelCase	, cache_dir=model_args.cache_dir	, )

   # Get datasets
   lowerCAmelCase     =			(
       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
   )
   lowerCAmelCase     =			(
       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	:     EvalPrediction				) -> Dict:
      lowerCAmelCase     =			np.argmax(p.predictions	, axis=1				)
      return {"acc": simple_accuracy(lowerCamelCase	, p.label_ids				)}

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

   # Initialize our Trainer
   lowerCAmelCase     =			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
   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(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	:     Dict				):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()









'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





