




from __future__ import annotations

import os


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

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

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




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

					@property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self._hand

					def 	lowercase_							(				self ,       __lowercase					):







						'''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(__lowercase					)
							else:
								return "Win" if self._second_pair > other._second_pair else "Loss"
						return "Win" if self._first_pair > other._first_pair else "Loss"

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							PokerHand._HAND_NAME[self._hand_type - 14]
						lowerCamelCase_							:		Optional[int]							   =							PokerHand._CARD_NAME[self._high_card]
						lowerCamelCase_							:		Optional[Any]							   =							PokerHand._CARD_NAME[self._first_pair]
						lowerCamelCase_							:		Dict							   =							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_							:		Union[str, Any]							   =							'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 	lowercase_							(				self ,       __lowercase					):







						'''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 	lowercase_							(				self					):







						'''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 	lowercase_							(				self					):







						'''simple docstring'''




						return len(self._card_suit					) == 1

					def 	lowercase_							(				self					):







						'''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_							:		int							   =							self._card_values.pop(0					)
								self._card_values.append(__lowercase					)
							return True
						return False

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							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_							:		str							   =							self._card_values[i]
									kind += 1
								elif vala == self._card_values[i]:
									kind += 2
								elif not vala:
									lowerCamelCase_							:		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_							:		Union[str, Any]							   =							kind + 2 if kind in [4, 5] else kind
						# first meaning first pair to compare in 'compare_with'
						lowerCamelCase_							:		int							   =							max(__lowercase ,       __lowercase					)
						lowerCamelCase_							:		List[str]							   =							min(__lowercase ,       __lowercase					)
						# 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(__lowercase					) != 3:
							lowerCamelCase_   ,			lowerCamelCase_							:		List[str]							   =							second, first
						lowerCamelCase_							:		Optional[int]							   =							first
						lowerCamelCase_							:		Optional[Any]							   =							second
						return kind

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

					def __repr__(				self					):







						'''simple docstring'''




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

					def __str__(				self					):







						'''simple docstring'''




						return self._hand

					def __eq__(				self ,       __lowercase					):







						'''simple docstring'''




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

					def __lt__(				self ,       __lowercase					):







						'''simple docstring'''




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

					def __le__(				self ,       __lowercase					):







						'''simple docstring'''




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

					def __gt__(				self ,       __lowercase					):







						'''simple docstring'''




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

					def __ge__(				self ,       __lowercase					):







						'''simple docstring'''




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





					def __hash__(				self					):







						'''simple docstring'''




						return object.__hash__(self					)






def 					snake_case					(							)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							0
	lowerCamelCase_							:		int							   =							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_							:		Optional[Any]							   =							line[:14].strip()
			lowerCamelCase_							:		Dict							   =							line[15:].strip()
			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							PokerHand(A			), PokerHand(A			)
			lowerCamelCase_							:		Dict							   =							player.compare_with(A			)
			if output == "Win":
				answer += 1
	return answer


if __name__ == "__main__":
							solution()






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import TvltImageProcessor






def 					snake_case					(							A						,   A=10						,   A=10						,   A=False						,   A=False			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							[]
	for i in range(image_processor_tester.num_frames			):
		video.append(np.random.randint(255						,   size=(image_processor_tester.num_channels, width, height)						,   dtype=np.uinta			)			)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		lowerCamelCase_							:		Any							   =							[Image.fromarray(np.moveaxis(A						,   0						,   -1			)			) for frame in video]

	if torchify:
		lowerCamelCase_							:		List[str]							   =							[torch.from_numpy(A			) for frame in video]

	return video






def 					snake_case					(							A						,   A=False						,   A=False						,   A=False			)							->							Dict:




	'''simple docstring'''


	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	lowerCamelCase_							:		int							   =							[]
	for i in range(image_processor_tester.batch_size			):
		if equal_resolution:
			lowerCamelCase_							:		Union[str, Any]							   =							image_processor_tester.max_resolution
		else:
			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							np.random.choice(
			    np.arange(image_processor_tester.min_resolution						,   image_processor_tester.max_resolution			)						,   2			)
			lowerCamelCase_							:		str							   =							prepare_video(
			    image_processor_tester=A						,   width=A						,   height=A						,   numpify=A						,   torchify=A						,   )
		video_inputs.append(A			)

	return video_inputs


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=7 ,       __lowercase=3 ,       __lowercase=4 ,       __lowercase=18 ,       __lowercase=30 ,       __lowercase=400 ,       __lowercase=True ,       __lowercase=None ,       __lowercase=True ,       __lowercase=[0.5, 0.5, 0.5] ,       __lowercase=[0.5, 0.5, 0.5] ,       __lowercase=True ,       __lowercase=None ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							size if size is not None else {'shortest_edge': 18}
						lowerCamelCase_							:		Union[str, Any]							   =							crop_size if crop_size is not None else {'height': 18, 'width': 18}

						lowerCamelCase_							:		List[Any]							   =							parent
						lowerCamelCase_							:		Optional[Any]							   =							batch_size
						lowerCamelCase_							:		Tuple							   =							num_channels
						lowerCamelCase_							:		Dict							   =							num_frames
						lowerCamelCase_							:		Dict							   =							image_size
						lowerCamelCase_							:		Optional[Any]							   =							min_resolution
						lowerCamelCase_							:		List[Any]							   =							max_resolution
						lowerCamelCase_							:		Dict							   =							do_resize
						lowerCamelCase_							:		List[Any]							   =							size
						lowerCamelCase_							:		Dict							   =							do_normalize
						lowerCamelCase_							:		Optional[Any]							   =							image_mean
						lowerCamelCase_							:		List[str]							   =							image_std
						lowerCamelCase_							:		Tuple							   =							do_center_crop
						lowerCamelCase_							:		List[Any]							   =							crop_size





					def 	lowercase_							(				self					):







						'''simple docstring'''




						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						}


@require_torch
@require_vision
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = TvltImageProcessor if is_vision_available() else None

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							TvltImageProcessorTester(self					)

					@property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.image_processor_tester.prepare_image_processor_dict()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							self.image_processing_class(**self.image_processor_dict					)
						self.assertTrue(hasattr(__lowercase ,       'image_mean'					)					)
						self.assertTrue(hasattr(__lowercase ,       'image_std'					)					)
						self.assertTrue(hasattr(__lowercase ,       'do_normalize'					)					)
						self.assertTrue(hasattr(__lowercase ,       'do_resize'					)					)
						self.assertTrue(hasattr(__lowercase ,       'do_center_crop'					)					)
						self.assertTrue(hasattr(__lowercase ,       'size'					)					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random PIL videos
						lowerCamelCase_							:		Dict							   =							prepare_video_inputs(self.image_processor_tester ,       equal_resolution=__lowercase					)
						for video in video_inputs:
							self.assertIsInstance(__lowercase ,       __lowercase					)
							self.assertIsInstance(video[0] ,       Image.Image					)

						# Test not batched input
						lowerCamelCase_							:		Any							   =							image_processor(video_inputs[0] ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )

						# Test batched
						lowerCamelCase_							:		List[Any]							   =							image_processor(__lowercase ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random numpy tensors
						lowerCamelCase_							:		Union[str, Any]							   =							prepare_video_inputs(self.image_processor_tester ,       equal_resolution=__lowercase ,       numpify=__lowercase					)
						for video in video_inputs:
							self.assertIsInstance(__lowercase ,       __lowercase					)
							self.assertIsInstance(video[0] ,       np.ndarray					)

						# Test not batched input
						lowerCamelCase_							:		Union[str, Any]							   =							image_processor(video_inputs[0] ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )

						# Test batched
						lowerCamelCase_							:		Tuple							   =							image_processor(__lowercase ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random PyTorch tensors
						lowerCamelCase_							:		Union[str, Any]							   =							prepare_video_inputs(self.image_processor_tester ,       equal_resolution=__lowercase ,       torchify=__lowercase					)
						for video in video_inputs:
							self.assertIsInstance(__lowercase ,       __lowercase					)
							self.assertIsInstance(video[0] ,       torch.Tensor					)

						# Test not batched input
						lowerCamelCase_							:		Optional[Any]							   =							image_processor(video_inputs[0] ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )

						# Test batched
						lowerCamelCase_							:		Tuple							   =							image_processor(__lowercase ,       return_tensors='pt'					).pixel_values
						self.assertEqual(
						    encoded_videos.shape ,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    ) ,       )
