





import math
import sys





class A_   :




   def __init__(self      :int					)->   str:
         __A			  =							0
         __A			  =							[]
         __A			  =							{}  # To store the pos of node in array




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         return self.cur_size == 0




   def _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :Dict					)->   List[Any]:
         __A			  =							self.left(_UpperCamelCase					)
         __A			  =							self.right(_UpperCamelCase					)
         if lc < self.cur_size and self.array(_UpperCamelCase					)[0] < self.array(_UpperCamelCase					)[0]:
               __A			  =							lc
         else:
               __A			  =							idx
         if rc < self.cur_size and self.array(_UpperCamelCase					)[0] < self.array(_UpperCamelCase					)[0]:
               __A			  =							rc
         if smallest != idx:
               self.swap(_UpperCamelCase       ,  _UpperCamelCase					)
               self.min_heapify(_UpperCamelCase					)




   def _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :str					)->   Dict:
         # Inserts a node into the Priority Queue
         __A			  =							self.cur_size
         self.cur_size += 1
         self.array.append((sys.maxsize, tup[1])					)
         self.decrease_key((sys.maxsize, tup[1])       ,  tup[0]					)




   def _lowerCAmelCase (self      :Tuple					)->   List[str]:
         # Removes and returns the min element at top of priority queue
         __A			  =							self.array[0][1]
         __A			  =							self.array[self.cur_size - 1]
         self.cur_size -= 1
         self.min_heapify(1					)
         del self.pos[min_node]
         return min_node




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :Optional[int]					)->   Optional[Any]:
         # returns the index of left child
         return 2 * i + 1




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :Dict					)->   int:
         # returns the index of right child
         return 2 * i + 2




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :int					)->   str:
         # returns the index of parent
         return math.floor(i / 2					)




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Tuple       ,  _UpperCamelCase      :List[Any]					)->   Any:
         # swaps array elements at indices i and j
         # update the pos{}
         __A			  =							j
         __A			  =							i
         __A			  =							self.array[i]
         __A			  =							self.array[j]
         __A			  =							temp






   def _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :List[str]       ,  _UpperCamelCase      :Tuple					)->   int:
         __A			  =							self.pos[tup[1]]
         # assuming the new_d is atmost old_d
         __A			  =							(new_d, tup[1])
         while idx > 0 and self.array[self.par(_UpperCamelCase					)][0] > self.array[idx][0]:
               self.swap(_UpperCamelCase       ,  self.par(_UpperCamelCase					)					)
               __A			  =							self.par(_UpperCamelCase					)





class A_   :




   def __init__(self      :Tuple       ,  _UpperCamelCase      :Optional[int]					)->   Optional[Any]:
         __A			  =							{}  # To store graph: u -> (v,w)
         __A			  =							num  # Number of nodes in graph
         # To store the distance from source vertex
         __A			  =							[0] * self.num_nodes
         __A			  =							[-1] * self.num_nodes  # To store the path




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :List[Any]       ,  _UpperCamelCase      :int					)->   Dict:
         #  Edge going from node u to v and v to u with weight w
         # u (w)-> v, v (w) -> u
         # Check if u already in graph
         if u in self.adjList:
               self.adjList[u].append((v, w)					)
         else:
               __A			  =							[(v, w)]

         # Assuming undirected graph
         if v in self.adjList:
               self.adjList[v].append((u, w)					)
         else:
               __A			  =							[(u, w)]




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         # u -> v(w)
         for u in self.adjList:
               print(_UpperCamelCase       ,  '''->'''       ,  ''' -> '''.join(str(f"""{v}({w})"""					) for v, w in self.adjList[u]					)					)




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :Dict					)->   List[str]:
         # Flush old junk values in par[]
         __A			  =							[-1] * self.num_nodes
         # src is the source node
         __A			  =							0
         __A			  =							PriorityQueue()
         q.insert((0, src)					)  # (dist from src, node)
         for u in self.adjList:
               if u != src:
                     __A			  =							sys.maxsize  # Infinity
                     __A			  =							-1

         while not q.is_empty():
               __A			  =							q.extract_min()  # Returns node with the min dist from source
               # Update the distance of all the neighbours of u and
               # if their prev dist was INFINITY then push them in Q
               for v, w in self.adjList[u]:
                     __A			  =							self.dist[u] + w
                     if self.dist[v] > new_dist:
                           if self.dist[v] == sys.maxsize:
                                 q.insert((new_dist, v)					)
                           else:
                                 q.decrease_key((self.dist[v], v)       ,  _UpperCamelCase					)
                           __A			  =							new_dist
                           __A			  =							u

        # Show the shortest distances from src
         self.show_distances(_UpperCamelCase					)




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Optional[Any]					)->   int:
         print(f"""Distance from node: {src}"""					)
         for u in range(self.num_nodes					):
               print(f"""Node {u} has distance: {self.dist[u]}"""					)






   def _lowerCAmelCase (self      :Dict       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[Any]					)->   Tuple:
         # To show the shortest path from src to dest
         # WARNING: Use it *after* calling dijkstra
         __A			  =							[]
         __A			  =							0
         __A			  =							dest
         # Backtracking from dest to src
         while self.par[temp] != -1:
               path.append(_UpperCamelCase					)
               if temp != src:
                     for v, w in self.adjList[temp]:
                           if v == self.par[temp]:
                                 cost += w
                                 break
               __A			  =							self.par[temp]
         path.append(_UpperCamelCase					)
         path.reverse()

         print(f"""----Path to reach {dest} from {src}----"""					)
         for u in path:
               print(f"""{u}"""       ,  end=''' '''					)
               if u != dest:
                     print('''-> '''       ,  end=''''''					)

         print('''\nTotal cost of path: '''       ,  _UpperCamelCase					)


if __name__ == "__main__":
       snake_case__   :				Tuple						=	Graph(9)
       graph.add_edge(0, 1, 4)
       graph.add_edge(0, 7, 8)
       graph.add_edge(1, 2, 8)
       graph.add_edge(1, 7, 11)
       graph.add_edge(2, 3, 7)
       graph.add_edge(2, 8, 2)
       graph.add_edge(2, 5, 4)
       graph.add_edge(3, 4, 9)
       graph.add_edge(3, 5, 14)
       graph.add_edge(4, 5, 10)
       graph.add_edge(5, 6, 2)
       graph.add_edge(6, 7, 1)
       graph.add_edge(6, 8, 6)
       graph.add_edge(7, 8, 7)
       graph.show_graph()
       graph.dijkstra(0)
       graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21












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__   :				Tuple						=	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def _a							(      lowerCamelCase:  int					)      ->		List[str]:







      '''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 _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Tuple   ,							lowerCamelCase:  Any					)      ->		str:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  Any   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  List[str]					)      ->		Any:







      '''simple docstring'''






      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]
      __A			  =							[]

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

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

      __A			  =							100.0 * em / total
      __A			  =							100.0 * fa / total

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



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  int   ,							lowerCamelCase:  Tuple					)      ->		List[str]:







      '''simple docstring'''






      __A			  =							args.k
      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]
      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]

      __A			  =							__A			  =							0
      for hypo, reference in zip(lowerCamelCase   ,							lowerCamelCase					):
            __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 _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  List[Any]   ,							lowerCamelCase:  int					)      ->		int:







      '''simple docstring'''






      def strip_title(lowerCamelCase:  int					):
            if title.startswith('''"'''					):
                  __A			  =							title[1:]
            if title.endswith('''"'''					):
                  __A			  =							title[:-1]
            return title

      __A			  =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          lowerCamelCase   ,							return_tensors='''pt'''   ,							padding=lowerCamelCase   ,							truncation=lowerCamelCase   ,							)['''input_ids'''].to(args.device					)

      __A			  =							rag_model.rag.question_encoder(lowerCamelCase					)
      __A			  =							question_enc_outputs[0]

      __A			  =							rag_model.retriever(
          lowerCamelCase   ,							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(lowerCamelCase					) for title in docs['''title''']]
            provenance_strings.append('''\t'''.join(lowerCamelCase					)					)
      return provenance_strings



def _a							(      lowerCamelCase:  Tuple   ,							lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Union[str, Any]					)      ->		Dict:







      '''simple docstring'''






      with torch.no_grad():
            __A			  =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                lowerCamelCase   ,							return_tensors='''pt'''   ,							padding=lowerCamelCase   ,							truncation=lowerCamelCase					)

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

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

            return answers



def _a							(      )      ->		Optional[Any]:







      '''simple docstring'''






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







      '''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'''   ,							lowerCamelCase					)

      __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(lowerCamelCase   ,							args.predictions_path   ,							args.gold_data_path					)
                  continue

            logger.info('''***** Running evaluation for {} *****'''.format(lowerCamelCase					)					)
            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(lowerCamelCase   ,							**lowerCamelCase					)
                  __A			  =							model_class.from_pretrained(lowerCamelCase   ,							retriever=lowerCamelCase   ,							**lowerCamelCase					)
                  model.retriever.init_retrieval()
            else:
                  __A			  =							model_class.from_pretrained(lowerCamelCase   ,							**lowerCamelCase					)
            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(lowerCamelCase					):
                        questions.append(line.strip()					)
                        if len(lowerCamelCase					) == args.eval_batch_size:
                              __A			  =							evaluate_batch_fn(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
                              preds_file.write('''\n'''.join(lowerCamelCase					) + '''\n'''					)
                              preds_file.flush()
                              __A			  =							[]
                  if len(lowerCamelCase					) > 0:
                        __A			  =							evaluate_batch_fn(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
                        preds_file.write('''\n'''.join(lowerCamelCase					)					)
                        preds_file.flush()

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


if __name__ == "__main__":
       snake_case__   :				List[str]						=	get_args()
       main(args)





