code: 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     :			Optional[Any]					    =						logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  def 		snake_case__  (		lowercase				):
     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 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     return max(metric_fn(lowercase       ,							lowercase				) for gt in ground_truths				)
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     lowerCAmelCase_:       Optional[Any]         =  [line.strip() for line in open(lowercase       ,							"r"				).readlines()]
     lowerCAmelCase_:       str         =  []
  
     if args.gold_data_mode == "qa":
        lowerCAmelCase_:       Optional[Any]         =  pd.read_csv(lowercase       ,							sep="\t"       ,							header=lowercase				)
        for answer_list in data[1]:
           lowerCAmelCase_:       Tuple         =  ast.literal_eval(lowercase				)
           answers.append(lowercase				)
     else:
        lowerCAmelCase_:       Tuple         =  [line.strip() for line in open(lowercase       ,							"r"				).readlines()]
        lowerCAmelCase_:       List[str]         =  [[reference] for reference in references]
  
     lowerCAmelCase_:       int         =  0
     for prediction, ground_truths in zip(lowercase       ,							lowercase				):
        total += 1
        em += metric_max_over_ground_truths(lowercase       ,							lowercase       ,							lowercase				)
        fa += metric_max_over_ground_truths(lowercase       ,							lowercase       ,							lowercase				)
  
     lowerCAmelCase_:       List[Any]         =  100.0 * em / total
     lowerCAmelCase_:       Optional[Any]         =  100.0 * fa / total
  
     logger.info(F'''F1: {fa:.2f}'''				)
     logger.info(F'''EM: {em:.2f}'''				)
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     lowerCAmelCase_:       List[Any]         =  args.k
     lowerCAmelCase_:       Dict         =  [line.strip() for line in open(lowercase       ,							"r"				).readlines()]
     lowerCAmelCase_:       Optional[Any]         =  [line.strip() for line in open(lowercase       ,							"r"				).readlines()]
  
     lowerCAmelCase_:       Optional[Any]         =  0
     for hypo, reference in zip(lowercase       ,							lowercase				):
        lowerCAmelCase_:       Dict         =  set(hypo.split("\t"				)[:k]				)
        lowerCAmelCase_:       int         =  set(reference.split("\t"				)				)
        total += 1
        em += len(hypo_provenance & ref_provenance				) / k
  
     lowerCAmelCase_:       Optional[int]         =  100.0 * em / total
     logger.info(F'''Precision@{k}: {em: .2f}'''				)
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     def strip_title(lowercase				):
        if title.startswith("\""				):
           lowerCAmelCase_:       List[str]         =  title[1:]
        if title.endswith("\""				):
           lowerCAmelCase_:       Dict         =  title[:-1]
        return title
  
     lowerCAmelCase_:       int         =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         lowercase       ,							return_tensors="pt"       ,							padding=lowercase       ,							truncation=lowercase       ,							)["input_ids"].to(args.device				)
  
     lowerCAmelCase_:       Union[str, Any]         =  rag_model.rag.question_encoder(lowercase				)
     lowerCAmelCase_:       Tuple         =  question_enc_outputs[0]
  
     lowerCAmelCase_:       str         =  rag_model.retriever(
         lowercase       ,							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_:       Optional[int]         =  rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
     lowerCAmelCase_:       Union[str, Any]         =  []
     for docs in all_docs:
        lowerCAmelCase_:       Dict         =  [strip_title(lowercase				) for title in docs["title"]]
        provenance_strings.append("\t".join(lowercase				)				)
     return provenance_strings
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     with torch.no_grad():
        lowerCAmelCase_:       Union[str, Any]         =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            lowercase       ,							return_tensors="pt"       ,							padding=lowercase       ,							truncation=lowercase				)
  
        lowerCAmelCase_:       Any         =  inputs_dict.input_ids.to(args.device				)
        lowerCAmelCase_:       List[Any]         =  inputs_dict.attention_mask.to(args.device				)
        lowerCAmelCase_:       List[str]         =  rag_model.generate(  # rag_model overwrites generate
            lowercase       ,							attention_mask=lowercase       ,							num_beams=args.num_beams       ,							min_length=args.min_length       ,							max_length=args.max_length       ,							early_stopping=lowercase       ,							num_return_sequences=1       ,							bad_words_ids=[[0, 0]]       ,							)
        lowerCAmelCase_:       Dict         =  rag_model.retriever.generator_tokenizer.batch_decode(lowercase       ,							skip_special_tokens=lowercase				)
  
        if args.print_predictions:
           for q, a in zip(lowercase       ,							lowercase				):
              logger.info("Q: {} - A: {}".format(lowercase       ,							lowercase				)				)
  
        return answers
  def 		snake_case__  (		):
     lowerCAmelCase_:       Dict         =  argparse.ArgumentParser()
     parser.add_argument(
         "--model_type"       ,							choices=["rag_sequence", "rag_token", "bart"]       ,							type=lowercase       ,							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=lowercase       ,							choices=["exact", "compressed", "legacy"]       ,							type=lowercase       ,							help="RAG model retriever type"       ,							)
     parser.add_argument(
         "--index_path"       ,							default=lowercase       ,							type=lowercase       ,							help="Path to the retrieval index"       ,							)
     parser.add_argument("--n_docs"       ,							default=5       ,							type=lowercase       ,							help="Number of retrieved docs"				)
     parser.add_argument(
         "--model_name_or_path"       ,							default=lowercase       ,							type=lowercase       ,							required=lowercase       ,							help="Path to pretrained checkpoints or model identifier from huggingface.co/models"       ,							)
     parser.add_argument(
         "--eval_mode"       ,							choices=["e2e", "retrieval"]       ,							default="e2e"       ,							type=lowercase       ,							help=(
             "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
             " precision@k."
         )       ,							)
     parser.add_argument("--k"       ,							default=1       ,							type=lowercase       ,							help="k for the precision@k calculation"				)
     parser.add_argument(
         "--evaluation_set"       ,							default=lowercase       ,							type=lowercase       ,							required=lowercase       ,							help="Path to a file containing evaluation samples"       ,							)
     parser.add_argument(
         "--gold_data_path"       ,							default=lowercase       ,							type=lowercase       ,							required=lowercase       ,							help="Path to a tab-separated file with gold samples"       ,							)
     parser.add_argument(
         "--gold_data_mode"       ,							default="qa"       ,							type=lowercase       ,							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=lowercase       ,							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=lowercase       ,							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=lowercase       ,							help="Number of beams to be used when generating answers"       ,							)
     parser.add_argument("--min_length"       ,							default=1       ,							type=lowercase       ,							help="Min length of the generated answers"				)
     parser.add_argument("--max_length"       ,							default=50       ,							type=lowercase       ,							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[str]         =  parser.parse_args()
     lowerCAmelCase_:       Tuple         =  torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
     return args
  def 		snake_case__  (		lowercase				):
     lowerCAmelCase_:       Optional[int]         =  {}
     if args.model_type is None:
        lowerCAmelCase_:       Union[str, Any]         =  infer_model_type(args.model_name_or_path				)
        assert args.model_type is not None
     if args.model_type.startswith("rag"				):
        lowerCAmelCase_:       Optional[int]         =  RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
        lowerCAmelCase_:       Tuple         =  args.n_docs
        if args.index_name is not None:
           lowerCAmelCase_:       List[str]         =  args.index_name
        if args.index_path is not None:
           lowerCAmelCase_:       List[str]         =  args.index_path
     else:
        lowerCAmelCase_:       str         =  BartForConditionalGeneration
  
     lowerCAmelCase_:       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"       ,							lowercase				)
  
     lowerCAmelCase_:       Dict         =  get_scores if args.eval_mode == "e2e" else get_precision_at_k
     lowerCAmelCase_:       Optional[int]         =  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(lowercase       ,							args.predictions_path       ,							args.gold_data_path				)
           continue
  
        logger.info("***** Running evaluation for {} *****".format(lowercase				)				)
        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_:       Optional[int]         =  RagRetriever.from_pretrained(lowercase       ,							**lowercase				)
           lowerCAmelCase_:       Tuple         =  model_class.from_pretrained(lowercase       ,							retriever=lowercase       ,							**lowercase				)
           model.retriever.init_retrieval()
        else:
           lowerCAmelCase_:       List[str]         =  model_class.from_pretrained(lowercase       ,							**lowercase				)
        model.to(args.device				)
  
        with open(args.evaluation_set       ,							"r"				) as eval_file, open(args.predictions_path       ,							"w"				) as preds_file:
           lowerCAmelCase_:       List[Any]         =  []
           for line in tqdm(lowercase				):
              questions.append(line.strip()				)
              if len(lowercase				) == args.eval_batch_size:
                 lowerCAmelCase_:       Optional[Any]         =  evaluate_batch_fn(lowercase       ,							lowercase       ,							lowercase				)
                 preds_file.write("\n".join(lowercase				) + "\n"				)
                 preds_file.flush()
                 lowerCAmelCase_:       Optional[int]         =  []
           if len(lowercase				) > 0:
              lowerCAmelCase_:       int         =  evaluate_batch_fn(lowercase       ,							lowercase       ,							lowercase				)
              preds_file.write("\n".join(lowercase				)				)
              preds_file.flush()
  
           score_fn(lowercase       ,							args.predictions_path       ,							args.gold_data_path				)
  
  
  if __name__ == "__main__":
   a     :			Union[str, Any]					    =						get_args()
   main(args)
style_context: from .balanced_parentheses import balanced_parentheses
  from .stack import Stack
  def 		snake_case__  (		lowercase				):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase       ,							-1				)
  def 		snake_case__  (		lowercase				):
     if not balanced_parentheses(lowercase				):
        raise ValueError("Mismatched parentheses"				)
     lowerCAmelCase_:       Stack[str]         =  Stack()
     lowerCAmelCase_:       Union[str, Any]         =  []
     for char in expression_str:
        if char.isalpha() or char.isdigit():
           postfix.append(lowercase				)
        elif char == "(":
           stack.push(lowercase				)
        elif char == ")":
           while not stack.is_empty() and stack.peek() != "(":
              postfix.append(stack.pop()				)
           stack.pop()
        else:
           while not stack.is_empty() and precedence(lowercase				) <= precedence(stack.peek()				):
              postfix.append(stack.pop()				)
           stack.push(lowercase				)
     while not stack.is_empty():
        postfix.append(stack.pop()				)
     return " ".join(lowercase				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   a     :			int					    =						"""a+b*(c^d-e)^(f+g*h)-i"""
  
   print("""Infix to Postfix Notation demonstration:\n""")
   print("""Infix notation: """ + expression)
   print("""Postfix notation: """ + infix_to_postfix(expression))