

from __future__ import annotations

import os







class 		__magic_name__     :

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  (
       'High card',
       'One pair',
       'Two pairs',
       'Three of a kind',
       'Straight',
       'Flush',
       'Full house',
       'Four of a kind',
       'Straight flush',
       'Royal flush',
   )

   lowerCamelCase__					:      Optional[int]        =  (
       '',  # placeholder as tuples are zero-indexed
       'One',
       'Two',
       'Three',
       'Four',
       'Five',
       'Six',
       'Seven',
       'Eight',
       'Nine',
       'Ten',
       'Jack',
       'Queen',
       'King',
       'Ace',
   )



   def __init__(							self,       lowercase_			)				->       None:






     """simple docstring"""






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



   @property
   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     return self._hand



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       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(_SCREAMING_SNAKE_CASE			)
       else:
         return "Win" if self._second_pair > other._second_pair else "Loss"
     return "Win" if self._first_pair > other._first_pair else "Loss"



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    =PokerHand._HAND_NAME[self._hand_type - 14]
     a__    =PokerHand._CARD_NAME[self._high_card]
     a__    =PokerHand._CARD_NAME[self._first_pair]
     a__    =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]:
       a__    ='''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 					_UpperCAmelCase					(							self,       lowercase_			)				->       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 					_UpperCAmelCase					(							self			)				->       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 					_UpperCAmelCase					(							self			)				->       bool:






     """simple docstring"""






     return len(self._card_suit			) == 1



   def 					_UpperCAmelCase					(							self			)				->       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
         a__    =self._card_values.pop(0			)
         self._card_values.append(_SCREAMING_SNAKE_CASE			)
       return True
     return False



   def 					_UpperCAmelCase					(							self			)				->       bool:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =a__    =a__    =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:
           a__    =self._card_values[i]
           kind += 1
         elif vala == self._card_values[i]:
           kind += 2
         elif not vala:
           a__    =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)
     a__    =kind + 2 if kind in [4, 5] else kind
     # first meaning first pair to compare in 'compare_with'
     a__    =max(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE			)
     a__    =min(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE			)
     # 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(_SCREAMING_SNAKE_CASE			) != 3:
       a__, a__    =second, first
     a__    =first
     a__    =second
     return kind



   def 					_UpperCAmelCase					(							self			)				->       tuple[list[int], set[str]]:






     """simple docstring"""






     a__    ={'''T''': '''10''', '''J''': '''11''', '''Q''': '''12''', '''K''': '''13''', '''A''': '''14'''}
     a__    =self._hand.translate(str.maketrans(_SCREAMING_SNAKE_CASE			)			).split()
     a__    =[int(card[:-1]			) for card in new_hand]
     a__    ={card[-1] for card in new_hand}
     return sorted(_SCREAMING_SNAKE_CASE,       reverse=_SCREAMING_SNAKE_CASE			), card_suit



   def __repr__(							self			)				->       Any:






     """simple docstring"""






     return F"""{self.__class__}(\"{self._hand}\")"""



   def __str__(							self			)				->       Tuple:






     """simple docstring"""






     return self._hand



   def __eq__(							self,       lowercase_			)				->       str:






     """simple docstring"""






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



   def __lt__(							self,       lowercase_			)				->       Tuple:






     """simple docstring"""






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



   def __le__(							self,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






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



   def __gt__(							self,       lowercase_			)				->       Tuple:






     """simple docstring"""






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



   def __ge__(							self,       lowercase_			)				->       Any:






     """simple docstring"""






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



   def __hash__(							self			)				->       str:






     """simple docstring"""






     return object.__hash__(self			)





def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =0
  a__    =os.path.abspath(os.path.dirname(_UpperCamelCase				)				)
  a__    =os.path.join(_UpperCamelCase      , '''poker_hands.txt'''				)
  with open(_UpperCamelCase				) as file_hand:
    for line in file_hand:
      a__    =line[:14].strip()
      a__    =line[15:].strip()
      a__, a__    =PokerHand(_UpperCamelCase				), PokerHand(_UpperCamelCase				)
      a__    =player.compare_with(_UpperCamelCase				)
      if output == "Win":
        answer += 1
  return answer


if __name__ == "__main__":
       solution()

'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
  __snake_case  : Any		       =    argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('--epochs', type=int, default=1)
  parser.add_argument('--per_device_train_batch_size', type=int, default=16)
  parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
  parser.add_argument('--model_name_or_path', type=str)
  parser.add_argument('--learning_rate', type=str, default=5E-5)
  parser.add_argument('--do_train', type=bool, default=True)
  parser.add_argument('--do_eval', type=bool, default=True)
  parser.add_argument('--output_dir', type=str)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('INFO'),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  )

  # Load model and tokenizer
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    train_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Dict		       =    {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    test_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  __snake_case  : Optional[int]		       =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, Any]		       =    time.time() - start_train_time

  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")
  for key, value in train_results.history.items():
    logger.info(F"""  {key} = {value}""")






