"""simple docstring"""



from __future__ import annotations

import os

class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 a_       :	int  			=(
     "High card",
     "One pair",
     "Two pairs",
     "Three of a kind",
     "Straight",
     "Flush",
     "Full house",
     "Four of a kind",
     "Straight flush",
     "Royal flush",
 )

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


 def __init__(   self		:      Any					,   _snake_case		:      str				)				->			None:




    '''simple docstring'''






    if not isinstance(_snake_case					,   _snake_case				):
       a__     				=		F'''Hand should be of type \'str\': {hand!r}'''
       raise TypeError(_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(_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        _lowerCAmelCase				(   self		:      Tuple				)				->			str:




    '''simple docstring'''






    return self._hand


 def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      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(_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        _lowerCAmelCase				(   self		:      List[Any]				)				->			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        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      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        _lowerCAmelCase				(   self		:      List[str]				)				->			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        _lowerCAmelCase				(   self		:      Dict				)				->			bool:




    '''simple docstring'''






    return len(self._card_suit				) == 1


 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			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(_snake_case				)
       return True
    return False


 def        _lowerCAmelCase				(   self		:      int				)				->			bool:




    '''simple docstring'''






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


 def        _lowerCAmelCase				(   self		:      Any				)				->			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(_snake_case					,   _snake_case				)
    a__     				=		min(_snake_case					,   _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(_snake_case				) != 3:
       a__				,       a__     				=		second, first
    a__     				=		first
    a__     				=		second
    return kind


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




    '''simple docstring'''






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


 def __repr__(   self		:      Optional[int]				)				->			Dict:




    '''simple docstring'''






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


 def __str__(   self		:      Dict				)				->			Tuple:




    '''simple docstring'''






    return self._hand


 def __eq__(   self		:      Any					,   _snake_case		:      int				)				->			Any:




    '''simple docstring'''






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


 def __lt__(   self		:      Optional[int]					,   _snake_case		:      Dict				)				->			Any:




    '''simple docstring'''






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


 def __le__(   self		:      Union[str, Any]					,   _snake_case		:      Tuple				)				->			Tuple:




    '''simple docstring'''






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


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




    '''simple docstring'''






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


 def __ge__(   self		:      str					,   _snake_case		:      Optional[int]				)				->			str:




    '''simple docstring'''






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



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




    '''simple docstring'''






    return object.__hash__(self				)





def   _lowerCamelCase ( )							->     int:


   '''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"""



from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__magic_name__    				=					{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}





@pytest.mark.parametrize(
    'task_cls',[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],)
def   _lowerCamelCase ( UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		task_cls()
   a__     				=		asdict(UpperCAmelCase__					)
   a__     				=		task_template_from_dict(UpperCAmelCase__					)
   assert task == reloaded

class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		LanguageModeling(text_column='input_text'				)
    assert {"input_text": "text"} == task.column_mapping



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




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    a__     				=		{'text_column': 'input_text'}
    a__     				=		LanguageModeling.from_dict(_snake_case				)
    assert "language-modeling" == task.task
    assert input_schema == task.input_schema

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Dict				)				->			Tuple:




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		TextClassification(text_column='input_text'					,   label_column='input_label'				)
    self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}					,   task.column_mapping				)


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{'text_column': 'input_text', 'label_column': 'input_labels'}
    a__     				=		TextClassification.from_dict(_snake_case				)
    self.assertEqual('text-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		TextClassification(text_column='input_text'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		QuestionAnsweringExtractive(
        context_column='input_context'					,   question_column='input_question'					,   answers_column='input_answers'				)
    self.assertDictEqual(
        {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}					,   task.column_mapping				)



 def        _lowerCAmelCase				(   self		:      Tuple				)				->			str:




    '''simple docstring'''






    a__     				=		Features({'question': Value('string'				), 'context': Value('string'				)}				)
    a__     				=		Features(
        {
            'answers': Sequence(
                {
                    'text': Value('string'				),
                    'answer_start': Value('int32'				),
                }				)
        }				)
    a__     				=		{
        'context_column': 'input_input_context',
        'question_column': 'input_question',
        'answers_column': 'input_answers',
    }
    a__     				=		QuestionAnsweringExtractive.from_dict(_snake_case				)
    self.assertEqual('question-answering-extractive'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		Summarization(text_column='input_text'					,   summary_column='input_summary'				)
    self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}					,   task.column_mapping				)



 def        _lowerCAmelCase				(   self		:      str				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				)}				)
    a__     				=		Features({'summary': Value('string'				)}				)
    a__     				=		{'text_column': 'input_text', 'summary_column': 'input_summary'}
    a__     				=		Summarization.from_dict(_snake_case				)
    self.assertEqual('summarization'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Tuple:




    '''simple docstring'''






    a__     				=		AutomaticSpeechRecognition(audio_column='input_audio'					,   transcription_column='input_transcription'				)
    self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}					,   task.column_mapping				)



 def        _lowerCAmelCase				(   self		:      Dict				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		Features({'audio': Audio()}				)
    a__     				=		Features({'transcription': Value('string'				)}				)
    a__     				=		{
        'audio_column': 'input_audio',
        'transcription_column': 'input_transcription',
    }
    a__     				=		AutomaticSpeechRecognition.from_dict(_snake_case				)
    self.assertEqual('automatic-speech-recognition'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			int:




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		AudioClassification(audio_column='input_audio'					,   label_column='input_label'				)
    self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}					,   task.column_mapping				)


 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Optional[int]:




    '''simple docstring'''






    a__     				=		Features({'audio': Audio()}				)
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{
        'audio_column': 'input_image',
        'label_column': 'input_label',
    }
    a__     				=		AudioClassification.from_dict(_snake_case				)
    self.assertEqual('audio-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



 def        _lowerCAmelCase				(   self		:      str				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		AudioClassification(audio_column='input_audio'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		sorted(['pos', 'neg']				)


 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			str:




    '''simple docstring'''






    a__     				=		ImageClassification(image_column='input_image'					,   label_column='input_label'				)
    self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}					,   task.column_mapping				)


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




    '''simple docstring'''






    a__     				=		Features({'image': Image()}				)
    a__     				=		Features({'labels': ClassLabel}				)
    a__     				=		{
        'image_column': 'input_image',
        'label_column': 'input_label',
    }
    a__     				=		ImageClassification.from_dict(_snake_case				)
    self.assertEqual('image-classification'					,   task.task				)
    self.assertEqual(_snake_case					,   task.input_schema				)
    self.assertEqual(_snake_case					,   task.label_schema				)



 def        _lowerCAmelCase				(   self		:      Tuple				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		ImageClassification(image_column='input_image'					,   label_column='input_label'				)
    self.assertEqual(task.label_schema['labels']					,   _snake_case				)
    a__     				=		task.align_with_features(Features({'input_label': ClassLabel(names=self.labels				)}				)				)
    self.assertEqual(task.label_schema['labels']					,   ClassLabel(names=self.labels				)				)

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    a__     				=		DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
    a__     				=		Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}					,   info=_snake_case				)
    assert isinstance(dataset.info.task_templates					,   _snake_case				)
    assert len(dataset.info.task_templates				) == 1

    def keep_task(_snake_case		:      Tuple				):
       return x

    def dont_keep_task(_snake_case		:      Any				):
       a__     				=		deepcopy(_snake_case				)
       a__     				=		0
       return out

    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates
    # reload from cache
    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == dataset.info.task_templates

    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []
    # reload from cache
    a__     				=		dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []



 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		Features({'text': Value('string'				), 'label': ClassLabel(names=('pos', 'neg')				)}				)
    a__     				=		TextClassification(text_column='text'					,   label_column='label'				)
    a__     				=		DatasetInfo(features=_snake_case					,   task_templates=_snake_case				)
    a__     				=		Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}					,   info=_snake_case				)

    def process(_snake_case		:      List[Any]				):
       return example

    a__     				=		dataset.remove_columns('label'				)
    a__     				=		modified_dataset.map(_snake_case				)
    assert mapped_dataset.info.task_templates == []


