



'''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


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

transformers_logging.set_verbosity_info()




def lowerCAmelCase  (__A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     def strip_title(__A):
          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 lowerCAmelCase  (__A  ,     __A  ,     __A):



     """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 lowerCAmelCase  ():



     """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 lowerCAmelCase  (__A):



     """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__":
  lowercase_			=  get_args()
  main(args)








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



