
"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :      List[str]       =				logging.get_logger(__name__)

_a   :      List[Any]       =				{}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """fsmt_decoder"""
    def __init__(     self    ,     snake_case_=0    ,     snake_case_=0  ):
      super().__init__()
      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    List[Any]            = bos_token_id






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[str]	=       """fsmt"""
    __lowercase				:			Optional[Any]	=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
    def __init__(     self    ,     snake_case_=["en", "de"]    ,     snake_case_=4_20_24    ,     snake_case_=4_20_24    ,     snake_case_="relu"    ,     snake_case_=10_24    ,     snake_case_=2_00    ,     snake_case_=10_24    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_=0.0    ,     snake_case_=0.02    ,     snake_case_=2    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=False    ,     snake_case_=5    ,     snake_case_=1.0    ,     snake_case_=False    ,     snake_case_=True    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     snake_case_=2    ,     **snake_case_    ,     ):
      _snake_case  :    str            = langs
      _snake_case  :    Tuple            = src_vocab_size
      _snake_case  :    Tuple            = tgt_vocab_size
      _snake_case  :    Optional[Any]            = d_model  # encoder_embed_dim and decoder_embed_dim

      _snake_case  :    Any            = encoder_ffn_dim
      _snake_case  :    Optional[int]            = encoder_layers
      _snake_case  :    Union[str, Any]            = encoder_attention_heads
      _snake_case  :    List[Any]            = encoder_layerdrop
      _snake_case  :    List[Any]            = decoder_layerdrop
      _snake_case  :    Optional[Any]            = decoder_ffn_dim
      _snake_case  :    Tuple            = decoder_layers
      _snake_case  :    Tuple            = decoder_attention_heads
      _snake_case  :    str            = max_position_embeddings
      _snake_case  :    List[Any]            = init_std  # Normal(0, this parameter)
      _snake_case  :    Optional[Any]            = activation_function

      _snake_case  :    int            = DecoderConfig(vocab_size=snake_case_    ,     bos_token_id=snake_case_  )
      if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

      _snake_case  :    List[str]            = scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      _snake_case  :    Any            = attention_dropout
      _snake_case  :    Optional[int]            = activation_dropout
      _snake_case  :    Optional[Any]            = dropout

      _snake_case  :    List[Any]            = use_cache
      super().__init__(
          pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     decoder_start_token_id=snake_case_    ,     is_encoder_decoder=snake_case_    ,     tie_word_embeddings=snake_case_    ,     forced_eos_token_id=snake_case_    ,     max_length=snake_case_    ,     num_beams=snake_case_    ,     length_penalty=snake_case_    ,     early_stopping=snake_case_    ,     **snake_case_    ,     )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = copy.deepcopy(self.__dict__  )
      _snake_case  :    str            = self.decoder.to_dict()
      _snake_case  :    int            = self.__class__.model_type
      return output




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__			:  Union[str, Any]					     =     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def        __lowercase   (  _A      )  ->    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        __lowercase   (  _A    ,     _A    ,     _A      )  ->    str:
      return max(metric_fn(_A    ,     _A      ) for gt in ground_truths      )






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    int:
      SCREAMING_SNAKE_CASE			:   int	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]
      SCREAMING_SNAKE_CASE			:   List[Any]	  = []

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

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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      )

      SCREAMING_SNAKE_CASE			:   List[str]	  = 100.0 * em / total
      SCREAMING_SNAKE_CASE			:   Dict	  = 100.0 * fa / total

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






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Any:
      SCREAMING_SNAKE_CASE			:   str	  = args.k
      SCREAMING_SNAKE_CASE			:   int	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]
      SCREAMING_SNAKE_CASE			:   List[str]	  = [line.strip() for line in open(_A    ,     """r"""      ).readlines()]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
      for hypo, reference in zip(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = set(hypo.split("""\t"""      )[:k]      )
            SCREAMING_SNAKE_CASE			:   List[Any]	  = set(reference.split("""\t"""      )      )
            total += 1
            em += len(hypo_provenance & ref_provenance      ) / k

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






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Optional[int]:
      def strip_title(_A      ):
            if title.startswith("""\""""      ):
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = title[1:]
            if title.endswith("""\""""      ):
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = title[:-1]
            return title

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

      SCREAMING_SNAKE_CASE			:   str	  = rag_model.rag.question_encoder(_A      )
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = question_enc_outputs[0]

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






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Dict:
      with torch.no_grad():
            SCREAMING_SNAKE_CASE			:   Dict	  = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                _A    ,     return_tensors="""pt"""    ,     padding=_A    ,     truncation=_A      )

            SCREAMING_SNAKE_CASE			:   List[Any]	  = inputs_dict.input_ids.to(args.device      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = inputs_dict.attention_mask.to(args.device      )
            SCREAMING_SNAKE_CASE			:   Tuple	  = 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]]    ,     )
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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        __lowercase   (  )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Tuple	  = 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."""    ,     )
      SCREAMING_SNAKE_CASE			:   Dict	  = parser.parse_args()
      SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      return args






def        __lowercase   (  _A      )  ->    Union[str, Any]:
      SCREAMING_SNAKE_CASE			:   str	  = {}
      if args.model_type is None:
            SCREAMING_SNAKE_CASE			:   List[Any]	  = infer_model_type(args.model_name_or_path      )
            assert args.model_type is not None
      if args.model_type.startswith("""rag"""      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = args.n_docs
            if args.index_name is not None:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = args.index_name
            if args.index_path is not None:
                  SCREAMING_SNAKE_CASE			:   Tuple	  = args.index_path
      else:
            SCREAMING_SNAKE_CASE			:   Any	  = BartForConditionalGeneration

      SCREAMING_SNAKE_CASE			:   Dict	  = (
          [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      )

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
      SCREAMING_SNAKE_CASE			:   List[str]	  = 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"""      ):
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = RagRetriever.from_pretrained(_A    ,     **_A      )
                  SCREAMING_SNAKE_CASE			:   List[str]	  = model_class.from_pretrained(_A    ,     retriever=_A    ,     **_A      )
                  model.retriever.init_retrieval()
            else:
                  SCREAMING_SNAKE_CASE			:   Any	  = 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:
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = []
                  for line in tqdm(_A      ):
                        questions.append(line.strip()      )
                        if len(_A      ) == args.eval_batch_size:
                              SCREAMING_SNAKE_CASE			:   List[Any]	  = evaluate_batch_fn(_A    ,     _A    ,     _A      )
                              preds_file.write("""\n""".join(_A      ) + """\n"""      )
                              preds_file.flush()
                              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = []
                  if len(_A      ) > 0:
                        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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__":
  UpperCAmelCase__			:  str					     =     get_args()
  main(args)




