


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


snake_case__				:	Optional[int]					       =					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def    __lowerCamelCase   (      A__						: int					)     ->    Tuple:
   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__						: str						,						A__						: List[Any]						,						A__						: Any					)     ->    List[Any]:
   return max(metric_fn(A__						,						A__					) for gt in ground_truths					)


def    __lowerCamelCase   (      A__						: Any						,						A__						: Optional[Any]						,						A__						: List[Any]					)     ->    Tuple:
   lowerCamelCase_						:   List[Any]		= [line.strip() for line in open(A__						,						"""r"""					).readlines()]
   lowerCamelCase_						:   Tuple		= []

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

   lowerCamelCase_						:   List[str]		= 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__					)

   lowerCamelCase_						:   Optional[int]		= 100.0 * em / total
   lowerCamelCase_						:   Optional[int]		= 100.0 * fa / total

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


def    __lowerCamelCase   (      A__						: str						,						A__						: str						,						A__						: Tuple					)     ->    Any:
   lowerCamelCase_						:   Optional[int]		= args.k
   lowerCamelCase_						:   Union[str, Any]		= [line.strip() for line in open(A__						,						"""r"""					).readlines()]
   lowerCamelCase_						:   int		= [line.strip() for line in open(A__						,						"""r"""					).readlines()]

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

   lowerCamelCase_						:   List[str]		= 100.0 * em / total
   logger.info(f'''Precision@{k}: {em: .2f}'''					)


def    __lowerCamelCase   (      A__						: int						,						A__						: List[str]						,						A__						: Optional[int]					)     ->    Optional[int]:
   def strip_title(A__						: int					):
      if title.startswith("""\""""					):
         lowerCamelCase_						:   int		= title[1:]
      if title.endswith("""\""""					):
         lowerCamelCase_						:   Optional[int]		= title[:-1]
      return title

   lowerCamelCase_						:   Optional[Any]		= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       A__						,						return_tensors="""pt"""						,						padding=A__						,						truncation=A__						,						)["""input_ids"""].to(args.device					)

   lowerCamelCase_						:   str		= rag_model.rag.question_encoder(A__					)
   lowerCamelCase_						:   Union[str, Any]		= question_enc_outputs[0]

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


def    __lowerCamelCase   (      A__						: Union[str, Any]						,						A__						: Optional[int]						,						A__						: int					)     ->    str:
   with torch.no_grad():
      lowerCamelCase_						:   Dict		= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          A__						,						return_tensors="""pt"""						,						padding=A__						,						truncation=A__					)

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


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

   lowerCamelCase_						:   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__					)

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









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





