





'''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:					List[str]							=				logging.getLogger(__name__)







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     int:
 return (preds == labels).mean()






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






@dataclass
class     UpperCAmelCase							:
    _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=UpperCAmelCase_					,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)







def _lowerCAmelCase     (      )->     List[str]:
 # 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					=   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'       ,    _lowerCAmelCase    )

 # Set seed
 set_seed(training_args.seed    )

 try:
  __UpperCAmelCase					=   processors[data_args.task_name]()
  __UpperCAmelCase					=   processor.get_labels()
  __UpperCAmelCase					=   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					=   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					=   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=_lowerCAmelCase       ,    cache_dir=model_args.cache_dir       ,    )

 # Get datasets
 __UpperCAmelCase					=   (
     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					=   (
     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					=   np.argmax(p.predictions       ,    axis=1    )
  return {"acc": simple_accuracy(_lowerCAmelCase       ,    p.label_ids    )}

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

 # Initialize our Trainer
 __UpperCAmelCase					=   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					=   {}
 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(_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 _lowerCAmelCase     (      _lowerCAmelCase    )->     Union[str, Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
    main()












'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 0:
      raise ValueError(
          'When using Ray for distributed fine-tuning, '
          'you\'ll need to provide the paths instead, '
          'as the dataset and the index are loaded '
          'separately. More info in examples/rag/use_own_knowledge_dataset.py '					)
     super().__init__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



    def 					__lowerCamelCase						(  self					):
     logger.info('initializing retrieval'					)

     if len(self.retrieval_workers					) > 0:
      ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
     else:
      # Non-distributed training. Load index into this same process.
      self.index.init_index()



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if len(self.retrieval_workers					) > 0:
      # Select a random retrieval actor.
      __UpperCAmelCase					=   self.retrieval_workers[random.randint(0     ,			len(self.retrieval_workers					) - 1					)]
      __UpperCAmelCase					,    __UpperCAmelCase					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





