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
  
  
  _lowerCAmelCase					:List[Any]  =      logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]							):
      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 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	List[str]							):
      return max(metric_fn(UpperCamelCase__  , UpperCamelCase__							) for gt in ground_truths							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						List[str]       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
      _UpperCAmelCase							:						Dict       							=					[]
  
      if args.gold_data_mode == "qa":
          _UpperCAmelCase							:						Union[str, Any]       							=					pd.read_csv(UpperCamelCase__  , sep='''\t'''  , header=UpperCamelCase__							)
          for answer_list in data[1]:
              _UpperCAmelCase							:						Optional[int]       							=					ast.literal_eval(UpperCamelCase__							)
              answers.append(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
          _UpperCAmelCase							:						Tuple       							=					[[reference] for reference in references]
  
      _UpperCAmelCase							:						List[str]       							=					0
      for prediction, ground_truths in zip(UpperCamelCase__  , UpperCamelCase__							):
          total += 1
          em += metric_max_over_ground_truths(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
          fa += metric_max_over_ground_truths(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      _UpperCAmelCase							:						List[Any]       							=					100.0 * em / total
      _UpperCAmelCase							:						Tuple       							=					100.0 * fa / total
  
      logger.info(F'F1: {fa:.2f}'							)
      logger.info(F'EM: {em:.2f}'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	Optional[int]							):
      _UpperCAmelCase							:						Tuple       							=					args.k
      _UpperCAmelCase							:						List[Any]       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
      _UpperCAmelCase							:						int       							=					[line.strip() for line in open(UpperCamelCase__  , '''r'''							).readlines()]
  
      _UpperCAmelCase							:						Tuple       							=					0
      for hypo, reference in zip(UpperCamelCase__  , UpperCamelCase__							):
          _UpperCAmelCase							:						Union[str, Any]       							=					set(hypo.split('''\t'''							)[:k]							)
          _UpperCAmelCase							:						Dict       							=					set(reference.split('''\t'''							)							)
          total += 1
          em += len(hypo_provenance & ref_provenance							) / k
  
      _UpperCAmelCase							:						int       							=					100.0 * em / total
      logger.info(F'Precision@{k}: {em: .2f}'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	List[Any]							):
      def strip_title(UpperCamelCase__	:	Dict							):
          if title.startswith('''"'''							):
              _UpperCAmelCase							:						Any       							=					title[1:]
          if title.endswith('''"'''							):
              _UpperCAmelCase							:						Dict       							=					title[:-1]
          return title
  
      _UpperCAmelCase							:						List[Any]       							=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          UpperCamelCase__  , return_tensors='''pt'''  , padding=UpperCamelCase__  , truncation=UpperCamelCase__  , )['''input_ids'''].to(args.device							)
  
      _UpperCAmelCase							:						int       							=					rag_model.rag.question_encoder(UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					question_enc_outputs[0]
  
      _UpperCAmelCase							:						Tuple       							=					rag_model.retriever(
          UpperCamelCase__  , 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'''  , )
      _UpperCAmelCase							:						int       							=					rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
      _UpperCAmelCase							:						Optional[int]       							=					[]
      for docs in all_docs:
          _UpperCAmelCase							:						Union[str, Any]       							=					[strip_title(UpperCamelCase__							) for title in docs['''title''']]
          provenance_strings.append('''\t'''.join(UpperCamelCase__							)							)
      return provenance_strings
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Dict							):
      with torch.no_grad():
          _UpperCAmelCase							:						Any       							=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              UpperCamelCase__  , return_tensors='''pt'''  , padding=UpperCamelCase__  , truncation=UpperCamelCase__							)
  
          _UpperCAmelCase							:						Optional[int]       							=					inputs_dict.input_ids.to(args.device							)
          _UpperCAmelCase							:						Dict       							=					inputs_dict.attention_mask.to(args.device							)
          _UpperCAmelCase							:						Union[str, Any]       							=					rag_model.generate(  # rag_model overwrites generate
              UpperCamelCase__  , attention_mask=UpperCamelCase__  , num_beams=args.num_beams  , min_length=args.min_length  , max_length=args.max_length  , early_stopping=UpperCamelCase__  , num_return_sequences=1  , bad_words_ids=[[0, 0]]  , )
          _UpperCAmelCase							:						List[str]       							=					rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase__  , skip_special_tokens=UpperCamelCase__							)
  
          if args.print_predictions:
              for q, a in zip(UpperCamelCase__  , UpperCamelCase__							):
                  logger.info('''Q: {} - A: {}'''.format(UpperCamelCase__  , UpperCamelCase__							)							)
  
          return answers
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Any       							=					argparse.ArgumentParser()
      parser.add_argument(
          '''--model_type'''  , choices=['''rag_sequence''', '''rag_token''', '''bart''']  , type=UpperCamelCase__  , 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=UpperCamelCase__  , choices=['''exact''', '''compressed''', '''legacy''']  , type=UpperCamelCase__  , help='''RAG model retriever type'''  , )
      parser.add_argument(
          '''--index_path'''  , default=UpperCamelCase__  , type=UpperCamelCase__  , help='''Path to the retrieval index'''  , )
      parser.add_argument('''--n_docs'''  , default=5  , type=UpperCamelCase__  , help='''Number of retrieved docs'''							)
      parser.add_argument(
          '''--model_name_or_path'''  , default=UpperCamelCase__  , type=UpperCamelCase__  , required=UpperCamelCase__  , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''  , )
      parser.add_argument(
          '''--eval_mode'''  , choices=['''e2e''', '''retrieval''']  , default='''e2e'''  , type=UpperCamelCase__  , help=(
              '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
              ''' precision@k.'''
          )  , )
      parser.add_argument('''--k'''  , default=1  , type=UpperCamelCase__  , help='''k for the precision@k calculation'''							)
      parser.add_argument(
          '''--evaluation_set'''  , default=UpperCamelCase__  , type=UpperCamelCase__  , required=UpperCamelCase__  , help='''Path to a file containing evaluation samples'''  , )
      parser.add_argument(
          '''--gold_data_path'''  , default=UpperCamelCase__  , type=UpperCamelCase__  , required=UpperCamelCase__  , help='''Path to a tab-separated file with gold samples'''  , )
      parser.add_argument(
          '''--gold_data_mode'''  , default='''qa'''  , type=UpperCamelCase__  , 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=UpperCamelCase__  , 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=UpperCamelCase__  , 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=UpperCamelCase__  , help='''Number of beams to be used when generating answers'''  , )
      parser.add_argument('''--min_length'''  , default=1  , type=UpperCamelCase__  , help='''Min length of the generated answers'''							)
      parser.add_argument('''--max_length'''  , default=50  , type=UpperCamelCase__  , 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.'''  , )
      _UpperCAmelCase							:						Any       							=					parser.parse_args()
      _UpperCAmelCase							:						int       							=					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
      return args
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						str       							=					{}
      if args.model_type is None:
          _UpperCAmelCase							:						Any       							=					infer_model_type(args.model_name_or_path							)
          assert args.model_type is not None
      if args.model_type.startswith('''rag'''							):
          _UpperCAmelCase							:						Any       							=					RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
          _UpperCAmelCase							:						Any       							=					args.n_docs
          if args.index_name is not None:
              _UpperCAmelCase							:						Union[str, Any]       							=					args.index_name
          if args.index_path is not None:
              _UpperCAmelCase							:						Optional[Any]       							=					args.index_path
      else:
          _UpperCAmelCase							:						List[Any]       							=					BartForConditionalGeneration
  
      _UpperCAmelCase							:						Tuple       							=					(
          [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'''  , UpperCamelCase__							)
  
      _UpperCAmelCase							:						str       							=					get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
      _UpperCAmelCase							:						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(UpperCamelCase__  , args.predictions_path  , args.gold_data_path							)
              continue
  
          logger.info('''***** Running evaluation for {} *****'''.format(UpperCamelCase__							)							)
          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'''							):
              _UpperCAmelCase							:						Any       							=					RagRetriever.from_pretrained(UpperCamelCase__  , **UpperCamelCase__							)
              _UpperCAmelCase							:						List[Any]       							=					model_class.from_pretrained(UpperCamelCase__  , retriever=UpperCamelCase__  , **UpperCamelCase__							)
              model.retriever.init_retrieval()
          else:
              _UpperCAmelCase							:						str       							=					model_class.from_pretrained(UpperCamelCase__  , **UpperCamelCase__							)
          model.to(args.device							)
  
          with open(args.evaluation_set  , '''r'''							) as eval_file, open(args.predictions_path  , '''w'''							) as preds_file:
              _UpperCAmelCase							:						Optional[Any]       							=					[]
              for line in tqdm(UpperCamelCase__							):
                  questions.append(line.strip()							)
                  if len(UpperCamelCase__							) == args.eval_batch_size:
                      _UpperCAmelCase							:						Tuple       							=					evaluate_batch_fn(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
                      preds_file.write('''\n'''.join(UpperCamelCase__							) + '''\n'''							)
                      preds_file.flush()
                      _UpperCAmelCase							:						Tuple       							=					[]
              if len(UpperCamelCase__							) > 0:
                  _UpperCAmelCase							:						Dict       							=					evaluate_batch_fn(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
                  preds_file.write('''\n'''.join(UpperCamelCase__							)							)
                  preds_file.flush()
  
              score_fn(UpperCamelCase__  , args.predictions_path  , args.gold_data_path							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Optional[Any]  =      get_args()
    main(args)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCAmelCase					:Dict  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCAmelCase					:Any  =      {
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCAmelCase					:str  =      {
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_INIT_CONFIGURATION
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =MPNetTokenizer
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
           super().__init__(
               A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)
  
           _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
           if (
               pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
           ):
               _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
               _UpperCAmelCase							:						List[Any]       							=					do_lower_case
               _UpperCAmelCase							:						List[Any]       							=					strip_accents
               _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)
  
           _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           if self._mask_token is None:
               if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''	)
               return None
           return str(self._mask_token	)
  
  
  
  
  
       @mask_token.setter
       def 				__lowerCAmelCase			(      self ,			A	)							->      str:
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
           _UpperCAmelCase							:						List[Any]       							=					value
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
           _UpperCAmelCase							:						List[str]       							=					[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 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						int       							=					[self.sep_token_id]
           _UpperCAmelCase							:						int       							=					[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]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
           return tuple(A	)
  
  
  