"""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


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

transformers_logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:
   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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Any:
   return max(metric_fn(__snake_case ,					__snake_case				) for gt in ground_truths				)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Optional[Any]:
   lowercase__:     int      =   [line.strip() for line in open(__snake_case ,					'''r'''				).readlines()]
   lowercase__:     Any      =   []

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

   lowercase__:     Optional[Any]      =   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				)

   lowercase__:     Any      =   1_0_0.0 * em / total
   lowercase__:     List[str]      =   1_0_0.0 * fa / total

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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Union[str, Any]:
   lowercase__:     str      =   args.k
   lowercase__:     Tuple      =   [line.strip() for line in open(__snake_case ,					'''r'''				).readlines()]
   lowercase__:     str      =   [line.strip() for line in open(__snake_case ,					'''r'''				).readlines()]

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

   lowercase__:     List[Any]      =   1_0_0.0 * em / total
   logger.info(F"""Precision@{k}: {em: .2f}"""				)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Optional[int]:
   def strip_title(__UpperCAmelCase				):
      if title.startswith('''"'''				):
         lowercase__:     Optional[Any]      =   title[1:]
      if title.endswith('''"'''				):
         lowercase__:     str      =   title[:-1]
      return title

   lowercase__:     int      =   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				)

   lowercase__:     Tuple      =   rag_model.rag.question_encoder(__snake_case				)
   lowercase__:     Optional[int]      =   question_enc_outputs[0]

   lowercase__:     int      =   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''' ,					)
   lowercase__:     List[str]      =   rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
   lowercase__:     Optional[int]      =   []
   for docs in all_docs:
      lowercase__:     Tuple      =   [strip_title(__snake_case				) for title in docs['''title''']]
      provenance_strings.append('''\t'''.join(__snake_case				)				)
   return provenance_strings



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Optional[Any]:
   with torch.no_grad():
      lowercase__:     str      =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __snake_case ,					return_tensors='''pt''' ,					padding=__snake_case ,					truncation=__snake_case				)

      lowercase__:     Optional[int]      =   inputs_dict.input_ids.to(args.device				)
      lowercase__:     List[str]      =   inputs_dict.attention_mask.to(args.device				)
      lowercase__:     Tuple      =   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]] ,					)
      lowercase__:     Dict      =   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 SCREAMING_SNAKE_CASE__    (     )						->						List[Any]:
   lowercase__:     List[str]      =   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=5_0 ,					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.''' ,					)
   lowercase__:     Optional[Any]      =   parser.parse_args()
   lowercase__:     str      =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
   return args



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						int:
   lowercase__:     Dict      =   {}
   if args.model_type is None:
      lowercase__:     Union[str, Any]      =   infer_model_type(args.model_name_or_path				)
      assert args.model_type is not None
   if args.model_type.startswith('''rag'''				):
      lowercase__:     Tuple      =   RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
      lowercase__:     Optional[int]      =   args.n_docs
      if args.index_name is not None:
         lowercase__:     Tuple      =   args.index_name
      if args.index_path is not None:
         lowercase__:     Any      =   args.index_path
   else:
      lowercase__:     Any      =   BartForConditionalGeneration

   lowercase__:     Union[str, Any]      =   (
       [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				)

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

"""simple docstring"""






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A         =				TypeVar("T", bound=bool)


class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase							):
       lowercase__:     T      =   value
       lowercase__:     RandomizedHeapNode[T] | None      =   None
       lowercase__:     RandomizedHeapNode[T] | None      =   None






    @property
    def _snake_case    (     self							):
       return self._value






    @staticmethod
    def _snake_case    (     _UpperCAmelCase					,    _UpperCAmelCase							):
       if not roota:
          return roota

       if not roota:
          return roota

       if roota.value > roota.value:
          lowercase__,				lowercase__:     List[str]      =   roota, roota

       if random.choice([True, False]							):
          lowercase__,				lowercase__:     Any      =   roota.right, roota.left

       lowercase__:     Optional[int]      =   RandomizedHeapNode.merge(roota.left					,    _UpperCAmelCase							)

       return roota



class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase = ()							):
       lowercase__:     RandomizedHeapNode[T] | None      =   None

       if data:
          for item in data:
             self.insert(_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Any      =   RandomizedHeapNode.merge(self._root					,    RandomizedHeapNode(_UpperCAmelCase							)							)






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   self.top()

       if self._root is None:
          return None

       lowercase__:     int      =   RandomizedHeapNode.merge(self._root.left					,    self._root.right							)

       return result






    def _snake_case    (     self							):
       if not self._root:
          raise IndexError('''Can\'t get top element for the empty heap.'''							)
       return self._root.value






    def _snake_case    (     self							):
       lowercase__:     Dict      =   None






    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   []
       while self:
          result.append(self.pop()							)

       return result






    def __bool__(     self							):
       return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()
