





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


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

transformers_logging.set_verbosity_info()



def        __lowercase       (				_SCREAMING_SNAKE_CASE     ):


      '''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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[line.strip() for line in open(snake_case_			,						"""r"""     ).readlines()]
      SCREAMING_SNAKE_CASE		=						[]

      if args.gold_data_mode == "qa":
            SCREAMING_SNAKE_CASE		=						pd.read_csv(snake_case_			,						sep="""\t"""			,						header=snake_case_     )
            for answer_list in data[1]:
                  SCREAMING_SNAKE_CASE		=						ast.literal_eval(snake_case_     )
                  answers.append(snake_case_     )
      else:
            SCREAMING_SNAKE_CASE		=						[line.strip() for line in open(snake_case_			,						"""r"""     ).readlines()]
            SCREAMING_SNAKE_CASE		=						[[reference] for reference in references]

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

      SCREAMING_SNAKE_CASE		=						100.0 * em / total
      SCREAMING_SNAKE_CASE		=						100.0 * fa / total

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						args.k
      SCREAMING_SNAKE_CASE		=						[line.strip() for line in open(snake_case_			,						"""r"""     ).readlines()]
      SCREAMING_SNAKE_CASE		=						[line.strip() for line in open(snake_case_			,						"""r"""     ).readlines()]

      SCREAMING_SNAKE_CASE		=						SCREAMING_SNAKE_CASE		=						0
      for hypo, reference in zip(snake_case_			,						snake_case_     ):
            SCREAMING_SNAKE_CASE		=						set(hypo.split("""\t"""     )[:k]     )
            SCREAMING_SNAKE_CASE		=						set(reference.split("""\t"""     )     )
            total += 1
            em += len(hypo_provenance & ref_provenance     ) / k

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      def strip_title(_SCREAMING_SNAKE_CASE     ):
            if title.startswith("""\""""     ):
                  SCREAMING_SNAKE_CASE		=						title[1:]
            if title.endswith("""\""""     ):
                  SCREAMING_SNAKE_CASE		=						title[:-1]
            return title

      SCREAMING_SNAKE_CASE		=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          snake_case_			,						return_tensors="""pt"""			,						padding=snake_case_			,						truncation=snake_case_			,						)["""input_ids"""].to(args.device     )

      SCREAMING_SNAKE_CASE		=						rag_model.rag.question_encoder(snake_case_     )
      SCREAMING_SNAKE_CASE		=						question_enc_outputs[0]

      SCREAMING_SNAKE_CASE		=						rag_model.retriever(
          snake_case_			,						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"""			,						)
      SCREAMING_SNAKE_CASE		=						rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
      SCREAMING_SNAKE_CASE		=						[]
      for docs in all_docs:
            SCREAMING_SNAKE_CASE		=						[strip_title(snake_case_     ) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(snake_case_     )     )
      return provenance_strings



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      with torch.no_grad():
            SCREAMING_SNAKE_CASE		=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                snake_case_			,						return_tensors="""pt"""			,						padding=snake_case_			,						truncation=snake_case_     )

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

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

            return answers



def        __lowercase       (				):


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						(
          [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"""			,						snake_case_     )

      SCREAMING_SNAKE_CASE		=						get_scores if args.eval_mode == """e2e""" else get_precision_at_k
      SCREAMING_SNAKE_CASE		=						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(snake_case_			,						args.predictions_path			,						args.gold_data_path     )
                  continue

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

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

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


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









import logging
import random

import ray

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


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict=None	)					->		Any:




           '''simple docstring'''


           if index is not None and index.is_initialized() and len(lowerCamelCase__	) > 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__(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						retrieval_workers
           if len(self.retrieval_workers	) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                         for worker in self.retrieval_workers
                     ]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           return super(lowerCamelCase__						,cls	).get_tokenizers(lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.generator
           if indexed_dataset is not None:
                 SCREAMING_SNAKE_CASE		=						"""custom"""
                 SCREAMING_SNAKE_CASE		=						CustomHFIndex(config.retrieval_vector_size						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


