code: 
  '''simple docstring'''
  
  import argparse
  import ast
  import logging
  import os
  import sys
  
  import pandas as pd
  import torch
  from tqdm import tqdm
  
  from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
  from transformers import logging as transformers_logging
  
  
  sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
  from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip
  
  
  UpperCamelCase_     			=       logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]		)    ->						Union[str, Any]:
    if "token" in model_name_or_path:
      return "rag_token"
    if "sequence" in model_name_or_path:
      return "rag_sequence"
    if "bart" in model_name_or_path:
      return "bart"
    return None
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       Optional[Any]		)    ->						List[str]:
    return max(metric_fn(_lowerCamelCase  ,      _lowerCamelCase		) for gt in ground_truths		)
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Union[str, Any]		)    ->						Optional[Any]:
    _lowerCAmelCase       :  Optional[int]   				=					[line.strip() for line in open(_lowerCamelCase  ,      """r"""		).readlines()]
    _lowerCAmelCase       :  Tuple   				=					[]
  
    if args.gold_data_mode == "qa":
      _lowerCAmelCase       :  Union[str, Any]   				=					pd.read_csv(_lowerCamelCase  ,      sep="""\t"""  ,      header=_lowerCamelCase		)
      for answer_list in data[1]:
        _lowerCAmelCase       :  List[str]   				=					ast.literal_eval(_lowerCamelCase		)
        answers.append(_lowerCamelCase		)
    else:
      _lowerCAmelCase       :  Optional[Any]   				=					[line.strip() for line in open(_lowerCamelCase  ,      """r"""		).readlines()]
      _lowerCAmelCase       :  Optional[int]   				=					[[reference] for reference in references]
  
    _lowerCAmelCase       :  str   				=					0
    for prediction, ground_truths in zip(_lowerCamelCase  ,      _lowerCamelCase		):
      total += 1
      em += metric_max_over_ground_truths(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      fa += metric_max_over_ground_truths(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
  
    _lowerCAmelCase       :  Optional[Any]   				=					100.0 * em / total
    _lowerCAmelCase       :  Optional[int]   				=					100.0 * fa / total
  
    logger.info(f'F1: {fa:.2f}'		)
    logger.info(f'EM: {em:.2f}'		)
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       Optional[int]		)    ->						Any:
    _lowerCAmelCase       :  Union[str, Any]   				=					args.k
    _lowerCAmelCase       :  Optional[int]   				=					[line.strip() for line in open(_lowerCamelCase  ,      """r"""		).readlines()]
    _lowerCAmelCase       :  Dict   				=					[line.strip() for line in open(_lowerCamelCase  ,      """r"""		).readlines()]
  
    _lowerCAmelCase       :  Optional[Any]   				=					0
    for hypo, reference in zip(_lowerCamelCase  ,      _lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					set(hypo.split("""\t"""		)[:k]		)
      _lowerCAmelCase       :  List[str]   				=					set(reference.split("""\t"""		)		)
      total += 1
      em += len(hypo_provenance & ref_provenance		) / k
  
    _lowerCAmelCase       :  Tuple   				=					100.0 * em / total
    logger.info(f'Precision@{k}: {em: .2f}'		)
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Union[str, Any]		)    ->						Tuple:
    def strip_title(_lowerCamelCase						:       List[Any]		):
      if title.startswith("""\""""		):
        _lowerCAmelCase       :  Dict   				=					title[1:]
      if title.endswith("""\""""		):
        _lowerCAmelCase       :  Optional[Any]   				=					title[:-1]
      return title
  
    _lowerCAmelCase       :  Optional[int]   				=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _lowerCamelCase  ,      return_tensors="""pt"""  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase  ,      )["""input_ids"""].to(args.device		)
  
    _lowerCAmelCase       :  List[str]   				=					rag_model.rag.question_encoder(_lowerCamelCase		)
    _lowerCAmelCase       :  Optional[int]   				=					question_enc_outputs[0]
  
    _lowerCAmelCase       :  int   				=					rag_model.retriever(
        _lowerCamelCase  ,      question_enc_pool_output.cpu().detach().to(torch.floataa		).numpy()  ,      prefix=rag_model.rag.generator.config.prefix  ,      n_docs=rag_model.config.n_docs  ,      return_tensors="""pt"""  ,      )
    _lowerCAmelCase       :  Optional[Any]   				=					rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
    _lowerCAmelCase       :  int   				=					[]
    for docs in all_docs:
      _lowerCAmelCase       :  Union[str, Any]   				=					[strip_title(_lowerCamelCase		) for title in docs["""title"""]]
      provenance_strings.append("""\t""".join(_lowerCamelCase		)		)
    return provenance_strings
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       Union[str, Any]  ,      _lowerCamelCase						:       Tuple		)    ->						Any:
    with torch.no_grad():
      _lowerCAmelCase       :  Optional[int]   				=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          _lowerCamelCase  ,      return_tensors="""pt"""  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase		)
  
      _lowerCAmelCase       :  str   				=					inputs_dict.input_ids.to(args.device		)
      _lowerCAmelCase       :  Optional[int]   				=					inputs_dict.attention_mask.to(args.device		)
      _lowerCAmelCase       :  Union[str, Any]   				=					rag_model.generate(  # rag_model overwrites generate
          _lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      num_beams=args.num_beams  ,      min_length=args.min_length  ,      max_length=args.max_length  ,      early_stopping=_lowerCamelCase  ,      num_return_sequences=1  ,      bad_words_ids=[[0, 0]]  ,      )
      _lowerCAmelCase       :  Tuple   				=					rag_model.retriever.generator_tokenizer.batch_decode(_lowerCamelCase  ,      skip_special_tokens=_lowerCamelCase		)
  
      if args.print_predictions:
        for q, a in zip(_lowerCamelCase  ,      _lowerCamelCase		):
          logger.info("""Q: {} - A: {}""".format(_lowerCamelCase  ,      _lowerCamelCase		)		)
  
      return answers
  
  
  def 	_UpperCAmelCase				(  )    ->						Any:
    _lowerCAmelCase       :  Tuple   				=					argparse.ArgumentParser()
    parser.add_argument(
        """--model_type"""  ,      choices=["""rag_sequence""", """rag_token""", """bart"""]  ,      type=_lowerCamelCase  ,      help=(
            """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
            """ model_name_or_path"""
        )  ,      )
    parser.add_argument(
        """--index_name"""  ,      default=_lowerCamelCase  ,      choices=["""exact""", """compressed""", """legacy"""]  ,      type=_lowerCamelCase  ,      help="""RAG model retriever type"""  ,      )
    parser.add_argument(
        """--index_path"""  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      help="""Path to the retrieval index"""  ,      )
    parser.add_argument("""--n_docs"""  ,      default=5  ,      type=_lowerCamelCase  ,      help="""Number of retrieved docs"""		)
    parser.add_argument(
        """--model_name_or_path"""  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      required=_lowerCamelCase  ,      help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""  ,      )
    parser.add_argument(
        """--eval_mode"""  ,      choices=["""e2e""", """retrieval"""]  ,      default="""e2e"""  ,      type=_lowerCamelCase  ,      help=(
            """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
            """ precision@k."""
        )  ,      )
    parser.add_argument("""--k"""  ,      default=1  ,      type=_lowerCamelCase  ,      help="""k for the precision@k calculation"""		)
    parser.add_argument(
        """--evaluation_set"""  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      required=_lowerCamelCase  ,      help="""Path to a file containing evaluation samples"""  ,      )
    parser.add_argument(
        """--gold_data_path"""  ,      default=_lowerCamelCase  ,      type=_lowerCamelCase  ,      required=_lowerCamelCase  ,      help="""Path to a tab-separated file with gold samples"""  ,      )
    parser.add_argument(
        """--gold_data_mode"""  ,      default="""qa"""  ,      type=_lowerCamelCase  ,      choices=["""qa""", """ans"""]  ,      help=(
            """Format of the gold data file"""
            """qa - a single line in the following format: question [tab] answer_list"""
            """ans - a single line of the gold file contains the expected answer string"""
        )  ,      )
    parser.add_argument(
        """--predictions_path"""  ,      type=_lowerCamelCase  ,      default="""predictions.txt"""  ,      help="""Name of the predictions file, to be stored in the checkpoints directory"""  ,      )
    parser.add_argument(
        """--eval_all_checkpoints"""  ,      action="""store_true"""  ,      help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""  ,      )
    parser.add_argument(
        """--eval_batch_size"""  ,      default=8  ,      type=_lowerCamelCase  ,      help="""Batch size per GPU/CPU for evaluation."""  ,      )
    parser.add_argument(
        """--recalculate"""  ,      help="""Recalculate predictions even if the prediction file exists"""  ,      action="""store_true"""  ,      )
    parser.add_argument(
        """--num_beams"""  ,      default=4  ,      type=_lowerCamelCase  ,      help="""Number of beams to be used when generating answers"""  ,      )
    parser.add_argument("""--min_length"""  ,      default=1  ,      type=_lowerCamelCase  ,      help="""Min length of the generated answers"""		)
    parser.add_argument("""--max_length"""  ,      default=50  ,      type=_lowerCamelCase  ,      help="""Max length of the generated answers"""		)
  
    parser.add_argument(
        """--print_predictions"""  ,      action="""store_true"""  ,      help="""If True, prints predictions while evaluating."""  ,      )
    parser.add_argument(
        """--print_docs"""  ,      action="""store_true"""  ,      help="""If True, prints docs retried while generating."""  ,      )
    _lowerCAmelCase       :  Dict   				=					parser.parse_args()
    _lowerCAmelCase       :  List[str]   				=					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
    return args
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Any		)    ->						str:
    _lowerCAmelCase       :  Dict   				=					{}
    if args.model_type is None:
      _lowerCAmelCase       :  List[Any]   				=					infer_model_type(args.model_name_or_path		)
      assert args.model_type is not None
    if args.model_type.startswith("""rag"""		):
      _lowerCAmelCase       :  Union[str, Any]   				=					RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
      _lowerCAmelCase       :  List[Any]   				=					args.n_docs
      if args.index_name is not None:
        _lowerCAmelCase       :  Union[str, Any]   				=					args.index_name
      if args.index_path is not None:
        _lowerCAmelCase       :  str   				=					args.index_path
    else:
      _lowerCAmelCase       :  Optional[int]   				=					BartForConditionalGeneration
  
    _lowerCAmelCase       :  Any   				=					(
        [f.path for f in os.scandir(args.model_name_or_path		) if f.is_dir()]
        if args.eval_all_checkpoints
        else [args.model_name_or_path]
    )
  
    logger.info("""Evaluate the following checkpoints: %s"""  ,      _lowerCamelCase		)
  
    _lowerCAmelCase       :  str   				=					get_scores if args.eval_mode == """e2e""" else get_precision_at_k
    _lowerCAmelCase       :  Any   				=					evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
  
    for checkpoint in checkpoints:
      if os.path.exists(args.predictions_path		) and (not args.recalculate):
        logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path		)		)
        score_fn(_lowerCamelCase  ,      args.predictions_path  ,      args.gold_data_path		)
        continue
  
      logger.info("""***** Running evaluation for {} *****""".format(_lowerCamelCase		)		)
      logger.info("""  Batch size = %d"""  ,      args.eval_batch_size		)
      logger.info("""  Predictions will be stored under {}""".format(args.predictions_path		)		)
  
      if args.model_type.startswith("""rag"""		):
        _lowerCAmelCase       :  Tuple   				=					RagRetriever.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase		)
        _lowerCAmelCase       :  Optional[int]   				=					model_class.from_pretrained(_lowerCamelCase  ,      retriever=_lowerCamelCase  ,      **_lowerCamelCase		)
        model.retriever.init_retrieval()
      else:
        _lowerCAmelCase       :  List[Any]   				=					model_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase		)
      model.to(args.device		)
  
      with open(args.evaluation_set  ,      """r"""		) as eval_file, open(args.predictions_path  ,      """w"""		) as preds_file:
        _lowerCAmelCase       :  str   				=					[]
        for line in tqdm(_lowerCamelCase		):
          questions.append(line.strip()		)
          if len(_lowerCamelCase		) == args.eval_batch_size:
            _lowerCAmelCase       :  int   				=					evaluate_batch_fn(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
            preds_file.write("""\n""".join(_lowerCamelCase		) + """\n"""		)
            preds_file.flush()
            _lowerCAmelCase       :  List[Any]   				=					[]
        if len(_lowerCamelCase		) > 0:
          _lowerCAmelCase       :  str   				=					evaluate_batch_fn(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
          preds_file.write("""\n""".join(_lowerCamelCase		)		)
          preds_file.flush()
  
        score_fn(_lowerCamelCase  ,      args.predictions_path  ,      args.gold_data_path		)
  
  
  if __name__ == "__main__":
       UpperCamelCase_     			=       get_args()
       main(args)
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  