

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     =      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def     UpperCAmelCase__       (    _A   :				str							):





  '''simple docstring'''







  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__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				str							):





  '''simple docstring'''







  return max(metric_fn(_A			,   _A							) for gt in ground_truths							)




def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[Any]			,   _A   :				Dict							):





  '''simple docstring'''







  a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
  a__						=[]

  if args.gold_data_mode == "qa":
    a__						=pd.read_csv(_A			,   sep='''\t'''			,   header=_A							)
    for answer_list in data[1]:
      a__						=ast.literal_eval(_A							)
      answers.append(_A							)
  else:
    a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
    a__						=[[reference] for reference in references]

  a__						=a__						=a__						=0
  for prediction, ground_truths in zip(_A			,   _A							):
    total += 1
    em += metric_max_over_ground_truths(_A			,   _A			,   _A							)
    fa += metric_max_over_ground_truths(_A			,   _A			,   _A							)

  a__						=100.0 * em / total
  a__						=100.0 * fa / total

  logger.info(F"""F1: {fa:.2f}"""							)
  logger.info(F"""EM: {em:.2f}"""							)




def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Any			,   _A   :				Optional[int]							):





  '''simple docstring'''







  a__						=args.k
  a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]
  a__						=[line.strip() for line in open(_A			,   '''r'''							).readlines()]

  a__						=a__						=0
  for hypo, reference in zip(_A			,   _A							):
    a__						=set(hypo.split('''\t'''							)[:k]							)
    a__						=set(reference.split('''\t'''							)							)
    total += 1
    em += len(hypo_provenance & ref_provenance							) / k

  a__						=100.0 * em / total
  logger.info(F"""Precision@{k}: {em: .2f}"""							)




def     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				str			,   _A   :				List[Any]							):





  '''simple docstring'''







  def strip_title(_A   :				List[str]							):
    if title.startswith('''"'''							):
      a__						=title[1:]
    if title.endswith('''"'''							):
      a__						=title[:-1]
    return title

  a__						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      _A			,   return_tensors='''pt'''			,   padding=_A			,   truncation=_A			,   )['''input_ids'''].to(args.device							)

  a__						=rag_model.rag.question_encoder(_A							)
  a__						=question_enc_outputs[0]

  a__						=rag_model.retriever(
      _A			,   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'''			,   )
  a__						=rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
  a__						=[]
  for docs in all_docs:
    a__						=[strip_title(_A							) for title in docs['''title''']]
    provenance_strings.append('''\t'''.join(_A							)							)
  return provenance_strings




def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Union[str, Any]			,   _A   :				Dict							):





  '''simple docstring'''







  with torch.no_grad():
    a__						=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _A			,   return_tensors='''pt'''			,   padding=_A			,   truncation=_A							)

    a__						=inputs_dict.input_ids.to(args.device							)
    a__						=inputs_dict.attention_mask.to(args.device							)
    a__						=rag_model.generate(  # rag_model overwrites generate
        _A			,   attention_mask=_A			,   num_beams=args.num_beams			,   min_length=args.min_length			,   max_length=args.max_length			,   early_stopping=_A			,   num_return_sequences=1			,   bad_words_ids=[[0, 0]]			,   )
    a__						=rag_model.retriever.generator_tokenizer.batch_decode(_A			,   skip_special_tokens=_A							)

    if args.print_predictions:
      for q, a in zip(_A			,   _A							):
        logger.info('''Q: {} - A: {}'''.format(_A			,   _A							)							)

    return answers




def     UpperCAmelCase__       (    ):





  '''simple docstring'''







  a__						=argparse.ArgumentParser()
  parser.add_argument(
      '''--model_type'''			,   choices=['''rag_sequence''', '''rag_token''', '''bart''']			,   type=_A			,   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=_A			,   choices=['''exact''', '''compressed''', '''legacy''']			,   type=_A			,   help='''RAG model retriever type'''			,   )
  parser.add_argument(
      '''--index_path'''			,   default=_A			,   type=_A			,   help='''Path to the retrieval index'''			,   )
  parser.add_argument('''--n_docs'''			,   default=5			,   type=_A			,   help='''Number of retrieved docs'''							)
  parser.add_argument(
      '''--model_name_or_path'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,   )
  parser.add_argument(
      '''--eval_mode'''			,   choices=['''e2e''', '''retrieval''']			,   default='''e2e'''			,   type=_A			,   help=(
          '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
          ''' precision@k.'''
      )			,   )
  parser.add_argument('''--k'''			,   default=1			,   type=_A			,   help='''k for the precision@k calculation'''							)
  parser.add_argument(
      '''--evaluation_set'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to a file containing evaluation samples'''			,   )
  parser.add_argument(
      '''--gold_data_path'''			,   default=_A			,   type=_A			,   required=_A			,   help='''Path to a tab-separated file with gold samples'''			,   )
  parser.add_argument(
      '''--gold_data_mode'''			,   default='''qa'''			,   type=_A			,   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=_A			,   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=_A			,   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=_A			,   help='''Number of beams to be used when generating answers'''			,   )
  parser.add_argument('''--min_length'''			,   default=1			,   type=_A			,   help='''Min length of the generated answers'''							)
  parser.add_argument('''--max_length'''			,   default=50			,   type=_A			,   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.'''			,   )
  a__						=parser.parse_args()
  a__						=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
  return args




def     UpperCAmelCase__       (    _A   :				Any							):





  '''simple docstring'''







  a__						={}
  if args.model_type is None:
    a__						=infer_model_type(args.model_name_or_path							)
    assert args.model_type is not None
  if args.model_type.startswith('''rag'''							):
    a__						=RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
    a__						=args.n_docs
    if args.index_name is not None:
      a__						=args.index_name
    if args.index_path is not None:
      a__						=args.index_path
  else:
    a__						=BartForConditionalGeneration

  a__						=(
      [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'''			,   _A							)

  a__						=get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
  a__						=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(_A			,   args.predictions_path			,   args.gold_data_path							)
      continue

    logger.info('''***** Running evaluation for {} *****'''.format(_A							)							)
    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'''							):
      a__						=RagRetriever.from_pretrained(_A			,   **_A							)
      a__						=model_class.from_pretrained(_A			,   retriever=_A			,   **_A							)
      model.retriever.init_retrieval()
    else:
      a__						=model_class.from_pretrained(_A			,   **_A							)
    model.to(args.device							)

    with open(args.evaluation_set			,   '''r'''							) as eval_file, open(args.predictions_path			,   '''w'''							) as preds_file:
      a__						=[]
      for line in tqdm(_A							):
        questions.append(line.strip()							)
        if len(_A							) == args.eval_batch_size:
          a__						=evaluate_batch_fn(_A			,   _A			,   _A							)
          preds_file.write('''\n'''.join(_A							) + '''\n'''							)
          preds_file.flush()
          a__						=[]
      if len(_A							) > 0:
        a__						=evaluate_batch_fn(_A			,   _A			,   _A							)
        preds_file.write('''\n'''.join(_A							)							)
        preds_file.flush()

      score_fn(_A			,   args.predictions_path			,   args.gold_data_path							)


if __name__ == "__main__":
       lowerCamelCase     =      get_args()
       main(args)



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.''' )

     if not os.path.isdir(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)
