


"""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


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

transformers_logging.set_verbosity_info()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				[line.strip() for line in open(_snake_case , '''r''' ).readlines()]
      snake_case_	=				[]

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

      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 )

      snake_case_	=				1_0_0.0 * em / total
      snake_case_	=				1_0_0.0 * fa / total

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

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

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

      snake_case_	=				1_0_0.0 * em / total
      logger.info(f'''Precision@{k}: {em: .2f}''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

      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 )

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

      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''' , )
      snake_case_	=				rag_model.retriever.index.get_doc_dicts(result.doc_ids )
      snake_case_	=				[]
      for docs in all_docs:
            snake_case_	=				[strip_title(_snake_case ) for title in docs['''title''']]
            provenance_strings.append('''\t'''.join(_snake_case ) )
      return provenance_strings






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

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

            snake_case_	=				inputs_dict.input_ids.to(args.device )
            snake_case_	=				inputs_dict.attention_mask.to(args.device )
            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]] , )
            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 	__lowerCAmelCase   ()->  int:




      """simple docstring"""

      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.''' , )
      snake_case_	=				parser.parse_args()
      snake_case_	=				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
      return args






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

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

      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 )

      snake_case_	=				get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
      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''' ):
                  snake_case_	=				RagRetriever.from_pretrained(_snake_case , **_snake_case )
                  snake_case_	=				model_class.from_pretrained(_snake_case , retriever=_snake_case , **_snake_case )
                  model.retriever.init_retrieval()
            else:
                  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:
                  snake_case_	=				[]
                  for line in tqdm(_snake_case ):
                        questions.append(line.strip() )
                        if len(_snake_case ) == args.eval_batch_size:
                              snake_case_	=				evaluate_batch_fn(_snake_case , _snake_case , _snake_case )
                              preds_file.write('''\n'''.join(_snake_case ) + '''\n''' )
                              preds_file.flush()
                              snake_case_	=				[]
                  if len(_snake_case ) > 0:
                        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__":
   UpperCAmelCase      =   get_args()
   main(args)



from __future__ import annotations

import time

UpperCAmelCase      =   list[tuple[int, int]]

UpperCAmelCase      =   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCAmelCase      =   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				pos_x
       snake_case_	=				pos_y
       snake_case_	=				(pos_y, pos_x)
       snake_case_	=				goal_x
       snake_case_	=				goal_y
       snake_case_	=				parent







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				Node(start[1]		,				start[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )
       snake_case_	=				Node(goal[1]		,				goal[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )

       snake_case_	=				[self.start]
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.node_queue:
             snake_case_	=				self.node_queue.pop(0 )

             if current_node.pos == self.target.pos:
                   snake_case_	=				True
                   return self.retrace_path(_UpperCAmelCase )

             snake_case_	=				self.get_successors(_UpperCAmelCase )

             for node in successors:
                   self.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.start.pos]
       return None



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				[]
       for action in delta:
             snake_case_	=				parent.pos_x + action[1]
             snake_case_	=				parent.pos_y + action[0]
             if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_UpperCAmelCase ) - 1):
                   continue

             if grid[pos_y][pos_x] != 0:
                   continue

             successors.append(
                 Node(_UpperCAmelCase		,				_UpperCAmelCase		,				self.target.pos_y		,				self.target.pos_x		,				_UpperCAmelCase ) )
       return successors





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				node
       snake_case_	=				[]
       while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x) )
             snake_case_	=				current_node.parent
       path.reverse()
       return path







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             snake_case_	=				self.fwd_bfs.node_queue.pop(0 )
             snake_case_	=				self.bwd_bfs.node_queue.pop(0 )

             if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case_	=				True
                   return self.retrace_bidirectional_path(
                       _UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				current_bwd_node
             snake_case_	=				current_fwd_node

             snake_case_	=				{
                 self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase ),
                 self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase ),
             }

             for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                         bfs.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.fwd_bfs.start.pos]
       return None





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.fwd_bfs.retrace_path(_UpperCAmelCase )
       snake_case_	=				self.bwd_bfs.retrace_path(_UpperCAmelCase )
       bwd_path.pop()
       bwd_path.reverse()
       snake_case_	=				fwd_path + bwd_path
       return path


if __name__ == "__main__":
   # all coordinates are given in format [y,x]
   import doctest

   doctest.testmod()
   UpperCAmelCase      =   (0, 0)
   UpperCAmelCase      =   (len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bfs.search()
   UpperCAmelCase      =   time.time() - start_bfs_time

   print("""Unidirectional BFS computation time : """, bfs_time)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BidirectionalBreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bd_bfs.search()
   UpperCAmelCase      =   time.time() - start_bd_bfs_time

   print("""Bidirectional BFS computation time : """, bd_bfs_time)