

from __future__ import annotations

import os



class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			Union[str, Any]						      =(
      "High card",
      "One pair",
      "Two pairs",
      "Three of a kind",
      "Straight",
      "Flush",
      "Full house",
      "Four of a kind",
      "Straight flush",
      "Royal flush",
  )

  lowerCamelCase    :			Dict						      =(
      "",  # placeholder as tuples are zero-indexed
      "One",
      "Two",
      "Three",
      "Four",
      "Five",
      "Six",
      "Seven",
      "Eight",
      "Nine",
      "Ten",
      "Jack",
      "Queen",
      "King",
      "Ace",
  )







  def __init__(			self :    Tuple       ,							lowerCAmelCase :    str    )							->	None:






     """simple docstring"""







     if not isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        __lowerCAmelCase  :			Tuple					    =						f'''Hand should be of type \'str\': {hand!r}'''
        raise TypeError(lowerCAmelCase    )
     # split removes duplicate whitespaces so no need of strip
     if len(hand.split(""" """    )    ) != 5:
        __lowerCAmelCase  :			Any					    =						f'''Hand should contain only 5 cards: {hand!r}'''
        raise ValueError(lowerCAmelCase    )
     __lowerCAmelCase  :			int					    =						hand
     __lowerCAmelCase  :			Any					    =						0
     __lowerCAmelCase  :			str					    =						0
     __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						self._internal_state()
     __lowerCAmelCase  :			int					    =						self._get_hand_type()
     __lowerCAmelCase  :			Any					    =						self._card_values[0]







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Optional[int]:






     """simple docstring"""







     return self._hand







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PokerHand    )							->	str:






     """simple docstring"""







     if self._hand_type > other._hand_type:
        return "Win"
     elif self._hand_type < other._hand_type:
        return "Loss"
     elif self._first_pair == other._first_pair:
        if self._second_pair == other._second_pair:
           return self._compare_cards(lowerCAmelCase    )
        else:
           return "Win" if self._second_pair > other._second_pair else "Loss"
     return "Win" if self._first_pair > other._first_pair else "Loss"







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	str:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						PokerHand._HAND_NAME[self._hand_type - 14]
     __lowerCAmelCase  :			str					    =						PokerHand._CARD_NAME[self._high_card]
     __lowerCAmelCase  :			Optional[Any]					    =						PokerHand._CARD_NAME[self._first_pair]
     __lowerCAmelCase  :			Any					    =						PokerHand._CARD_NAME[self._second_pair]
     if self._hand_type in [22, 19, 18]:
        return name + f''', {high}-high'''
     elif self._hand_type in [21, 17, 15]:
        return name + f''', {paira}s'''
     elif self._hand_type in [20, 16]:
        __lowerCAmelCase  :			List[str]					    =						"""over""" if self._hand_type == 20 else """and"""
        return name + f''', {paira}s {join} {paira}s'''
     elif self._hand_type == 23:
        return name
     else:
        return name + f''', {high}'''







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PokerHand    )							->	str:






     """simple docstring"""







     for index, card_value in enumerate(self._card_values    ):
        if card_value != other._card_values[index]:
           return "Win" if card_value > other._card_values[index] else "Loss"
     return "Tie"







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







     if self._is_flush():
        if self._is_five_high_straight() or self._is_straight():
           return 23 if sum(self._card_values    ) == 60 else 22
        return 19
     elif self._is_five_high_straight() or self._is_straight():
        return 18
     return 14 + self._is_same_kind()







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	bool:






     """simple docstring"""







     return len(self._card_suit    ) == 1







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	bool:






     """simple docstring"""







     if sorted(self._card_values    ) == [2, 3, 4, 5, 14]:
        if self._card_values[0] == 14:
           # Remember, our list is sorted in reverse order
           __lowerCAmelCase  :			Optional[Any]					    =						self._card_values.pop(0    )
           self._card_values.append(lowerCAmelCase    )
        return True
     return False







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	bool:






     """simple docstring"""







     for i in range(4    ):
        if self._card_values[i] - self._card_values[i + 1] != 1:
           return False
     return True







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			str					    =						0
     for i in range(4    ):
        # Compare two cards at a time, if they are same increase 'kind',
        # add the value of the card to val1, if it is repeating again we
        # will add 2 to 'kind' as there are now 3 cards with same value.
        # If we get card of different value than val1, we will do the same
        # thing with val2
        if self._card_values[i] == self._card_values[i + 1]:
           if not vala:
              __lowerCAmelCase  :			List[str]					    =						self._card_values[i]
              kind += 1
           elif vala == self._card_values[i]:
              kind += 2
           elif not vala:
              __lowerCAmelCase  :			Optional[Any]					    =						self._card_values[i]
              kind += 1
           elif vala == self._card_values[i]:
              kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
     __lowerCAmelCase  :			List[Any]					    =						kind + 2 if kind in [4, 5] else kind
     # first meaning first pair to compare in 'compare_with'
     __lowerCAmelCase  :			Optional[int]					    =						max(lowerCAmelCase       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Optional[Any]					    =						min(lowerCAmelCase       ,							lowerCAmelCase    )
     # If it's full house (three count pair + two count pair), make sure
     # first pair is three count and if not then switch them both.
     if kind == 6 and self._card_values.count(lowerCAmelCase    ) != 3:
        __lowerCAmelCase    ,__lowerCAmelCase  :			Tuple					    =						second, first
     __lowerCAmelCase  :			Optional[Any]					    =						first
     __lowerCAmelCase  :			Union[str, Any]					    =						second
     return kind







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	tuple[list[int], set[str]]:






     """simple docstring"""







     __lowerCAmelCase  :			dict					    =						{"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
     __lowerCAmelCase  :			Optional[Any]					    =						self._hand.translate(str.maketrans(lowerCAmelCase    )    ).split()
     __lowerCAmelCase  :			List[str]					    =						[int(card[:-1]    ) for card in new_hand]
     __lowerCAmelCase  :			str					    =						{card[-1] for card in new_hand}
     return sorted(lowerCAmelCase       ,							reverse=lowerCAmelCase    ), card_suit







  def __repr__(			self :    List[str]    )							->	Any:






     """simple docstring"""







     return f'''{self.__class__}("{self._hand}")'''







  def __str__(			self :    Any    )							->	Any:






     """simple docstring"""







     return self._hand







  def __eq__(			self :    Union[str, Any]       ,							lowerCAmelCase :    List[Any]    )							->	Union[str, Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self.compare_with(lowerCAmelCase    ) == "Tie"
     return NotImplemented







  def __lt__(			self :    Dict       ,							lowerCAmelCase :    str    )							->	List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self.compare_with(lowerCAmelCase    ) == "Loss"
     return NotImplemented







  def __le__(			self :    str       ,							lowerCAmelCase :    int    )							->	Union[str, Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self < other or self == other
     return NotImplemented







  def __gt__(			self :    Dict       ,							lowerCAmelCase :    str    )							->	Optional[Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return not self < other and self != other
     return NotImplemented







  def __ge__(			self :    Tuple       ,							lowerCAmelCase :    Tuple    )							->	Dict:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return not self < other
     return NotImplemented







  def __hash__(			self :    List[str]    )							->	str:






     """simple docstring"""







     return object.__hash__(self    )






def 	snake_case_      ()			->	int:
   # Solution for problem number 54 from Project Euler
   # Input from poker_hands.txt file
   __lowerCAmelCase  :			str					    =						0
   __lowerCAmelCase  :			List[str]					    =						os.path.abspath(os.path.dirname(__A     )     )
   __lowerCAmelCase  :			Optional[Any]					    =						os.path.join(__A					,   """poker_hands.txt"""     )
   with open(__A     ) as file_hand:
      for line in file_hand:
         __lowerCAmelCase  :			Union[str, Any]					    =						line[:1_4].strip()
         __lowerCAmelCase  :			Optional[int]					    =						line[1_5:].strip()
         __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						PokerHand(__A     ), PokerHand(__A     )
         __lowerCAmelCase  :			List[str]					    =						player.compare_with(__A     )
         if output == "Win":
            answer += 1
   return answer


if __name__ == "__main__":
   solution()





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 	snake_case_      (__A     :						Any     )			->	Any:
   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_      (__A     :						Optional[int]					,   __A     :						Optional[int]					,   __A     :						List[Any]     )			->	Dict:
   return max(metric_fn(__A					,   __A     ) for gt in ground_truths     )





def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						str					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   __lowerCAmelCase  :			int					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]
   __lowerCAmelCase  :			str					    =						[]

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

   __lowerCAmelCase  :			int					    =						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  :			Any					    =						100.0 * em / total
   __lowerCAmelCase  :			Union[str, Any]					    =						100.0 * fa / total

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





def 	snake_case_      (__A     :						str					,   __A     :						Any					,   __A     :						Dict     )			->	Optional[Any]:
   __lowerCAmelCase  :			Optional[int]					    =						args.k
   __lowerCAmelCase  :			List[Any]					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]
   __lowerCAmelCase  :			Any					    =						[line.strip() for line in open(__A					,   """r"""     ).readlines()]

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

   __lowerCAmelCase  :			int					    =						100.0 * em / total
   logger.info(f'''Precision@{k}: {em: .2f}'''     )





def 	snake_case_      (__A     :						List[Any]					,   __A     :						Optional[Any]					,   __A     :						Dict     )			->	int:
   def strip_title(__A     :						List[Any]     ):
      if title.startswith("""\""""     ):
         __lowerCAmelCase  :			Dict					    =						title[1:]
      if title.endswith("""\""""     ):
         __lowerCAmelCase  :			Optional[int]					    =						title[:-1]
      return title

   __lowerCAmelCase  :			Dict					    =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
       __A					,   return_tensors="""pt"""					,   padding=__A					,   truncation=__A					,   )["""input_ids"""].to(args.device     )

   __lowerCAmelCase  :			Any					    =						rag_model.rag.question_encoder(__A     )
   __lowerCAmelCase  :			Optional[int]					    =						question_enc_outputs[0]

   __lowerCAmelCase  :			Dict					    =						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  :			int					    =						rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
   __lowerCAmelCase  :			List[str]					    =						[]
   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 	snake_case_      (__A     :						Any					,   __A     :						Any					,   __A     :						List[str]     )			->	Tuple:
   with torch.no_grad():
      __lowerCAmelCase  :			Optional[Any]					    =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          __A					,   return_tensors="""pt"""					,   padding=__A					,   truncation=__A     )

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





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

   __lowerCAmelCase  :			List[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"""					,   __A     )

   __lowerCAmelCase  :			str					    =						get_scores if args.eval_mode == """e2e""" else get_precision_at_k
   __lowerCAmelCase  :			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  :			str					    =						RagRetriever.from_pretrained(__A					,   **__A     )
         __lowerCAmelCase  :			Optional[Any]					    =						model_class.from_pretrained(__A					,   retriever=__A					,   **__A     )
         model.retriever.init_retrieval()
      else:
         __lowerCAmelCase  :			int					    =						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  :			Optional[int]					    =						[]
         for line in tqdm(__A     ):
            questions.append(line.strip()     )
            if len(__A     ) == args.eval_batch_size:
               __lowerCAmelCase  :			Optional[int]					    =						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__":
   __UpperCAmelCase      = get_args()
   main(args)


