



import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

__A   			=(
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

__A   			=(
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

__A   			=(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

__A   			=(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

__A   			=(
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 1_4]),
    ("2H 5D 3C AS 5S", False, [1_4, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

__A   			=(
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

__A   			=(
    ("JH AH TH KH QH", 2_3),
    ("JH 9H TH KH QH", 2_2),
    ("JC KH JS JD JH", 2_1),
    ("KH KC 3S 3H 3D", 2_0),
    ("8C 9C 5C 3C TC", 1_9),
    ("JS QS 9H TS KH", 1_8),
    ("7C 7S KH 2H 7H", 1_7),
    ("3C KH 5D 5S KH", 1_6),
    ("QH 8H KD JH 8S", 1_5),
    ("2D 6D 9D TH 7D", 1_4),
)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	randrange(len(_UpperCAmelCase						)						), randrange(len(_UpperCAmelCase						)						)
		__UpperCAmelCase    :     Optional[int]													=	['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	SORTED_HANDS[play], SORTED_HANDS[oppo]
		return hand, other, expected

def        a						(      _UpperCAmelCase							:					int = 1_00						):







		'''simple docstring'''





		return (generate_random_hand() for _ in range(_UpperCAmelCase						))

@pytest.mark.parametrize('''hand, expected''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						)._is_flush() == expected

@pytest.mark.parametrize('''hand, expected''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						)._is_straight() == expected

@pytest.mark.parametrize('''hand, expected, card_values''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	PokerHand(_UpperCAmelCase						)
		assert player._is_five_high_straight() == expected
		assert player._card_values == card_values

@pytest.mark.parametrize('''hand, expected''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						)._is_same_kind() == expected

@pytest.mark.parametrize('''hand, expected''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						)._hand_type == expected

@pytest.mark.parametrize('''hand, other, expected''' ,   _UpperCAmelCase						)
def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						).compare_with(PokerHand(_UpperCAmelCase						)						) == expected

@pytest.mark.parametrize('''hand, other, expected''' ,   generate_random_hands()						)
def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		assert PokerHand(_UpperCAmelCase						).compare_with(PokerHand(_UpperCAmelCase						)						) == expected

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	[PokerHand(_UpperCAmelCase						) for hand in SORTED_HANDS]
		__UpperCAmelCase    :     Any													=	poker_hands.copy()
		shuffle(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	chain(sorted(_UpperCAmelCase						)						)
		for index, hand in enumerate(_UpperCAmelCase						):
				assert hand == poker_hands[index]

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[PokerHand('''2D AC 3H 4H 5S'''						), PokerHand('''2S 3H 4H 5S 6C'''						)]
		pokerhands.sort(reverse=_UpperCAmelCase						)
		assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	PokerHand('''2C 4S AS 3D 5C'''						)
		__UpperCAmelCase    :     Optional[Any]													=	True
		__UpperCAmelCase    :     str													=	[5, 4, 3, 2, 14]
		for _ in range(10						):
				assert pokerhand._is_five_high_straight() == expected
				assert pokerhand._card_values == expected_card_values

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     List[str]													=	os.path.abspath(os.path.dirname(_UpperCAmelCase						)						)
		__UpperCAmelCase    :     Optional[int]													=	os.path.join(_UpperCAmelCase ,   '''poker_hands.txt'''						)
		with open(_UpperCAmelCase						) as file_hand:
				for line in file_hand:
						__UpperCAmelCase    :     Optional[Any]													=	line[:14].strip()
						__UpperCAmelCase    :     Any													=	line[15:].strip()
						__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	PokerHand(_UpperCAmelCase						), PokerHand(_UpperCAmelCase						)
						__UpperCAmelCase    :     Union[str, Any]													=	player.compare_with(_UpperCAmelCase						)
						if output == "Win":
								answer += 1
		assert answer == 3_76










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__A   			=logging.get_logger(__name__)


if is_vision_available():
				import PIL

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""pixel_values"""]




			def __init__(   self							: Tuple       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = PILImageResampling.BICUBIC       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: bool = True       ,       a_							: Union[int, float] = 1 / 2_55       ,       a_							: bool = True       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: bool = True       ,       **a_							: str       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )
					__UpperCAmelCase    :     List[Any]													=	size if size is not None else {'''shortest_edge''': 2_24}
					__UpperCAmelCase    :     List[str]													=	get_size_dict(a_       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__UpperCAmelCase    :     Optional[int]													=	get_size_dict(a_       ,       default_to_square=a_       ,       param_name='''crop_size''' )

					__UpperCAmelCase    :     int													=	do_resize
					__UpperCAmelCase    :     Union[str, Any]													=	size
					__UpperCAmelCase    :     Union[str, Any]													=	resample
					__UpperCAmelCase    :     Any													=	do_center_crop
					__UpperCAmelCase    :     Any													=	crop_size
					__UpperCAmelCase    :     Any													=	do_rescale
					__UpperCAmelCase    :     Dict													=	rescale_factor
					__UpperCAmelCase    :     Union[str, Any]													=	do_normalize
					__UpperCAmelCase    :     Any													=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					__UpperCAmelCase    :     int													=	image_std if image_std is not None else OPENAI_CLIP_STD
					__UpperCAmelCase    :     List[str]													=	do_convert_rgb




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: PILImageResampling = PILImageResampling.BICUBIC       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Optional[int]       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       default_to_square=a_ )
					if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
					__UpperCAmelCase    :     Optional[int]													=	get_resize_output_image_size(a_       ,       size=size['''shortest_edge''']       ,       default_to_square=a_ )
					return resize(a_       ,       size=a_       ,       resample=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	get_size_dict(a_ )
					if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
					return center_crop(a_       ,       size=(size['''height'''], size['''width'''])       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: np.ndarray       ,       a_							: Union[int, float]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					return rescale(a_       ,       scale=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Union[float, List[float]]       ,       a_							: Union[float, List[float]]       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					return normalize(a_       ,       mean=a_       ,       std=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Any       ,       a_							: ImageInput       ,       a_							: bool = None       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = None       ,       a_							: bool = None       ,       a_							: int = None       ,       a_							: bool = None       ,       a_							: float = None       ,       a_							: bool = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: Optional[Union[float, List[float]]] = None       ,       a_							: bool = None       ,       a_							: Optional[Union[str, TensorType]] = None       ,       a_							: Optional[ChannelDimension] = ChannelDimension.FIRST       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase    :     Optional[Any]													=	size if size is not None else self.size
					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       param_name='''size'''       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	resample if resample is not None else self.resample
					__UpperCAmelCase    :     Optional[Any]													=	do_center_crop if do_center_crop is not None else self.do_center_crop
					__UpperCAmelCase    :     Any													=	crop_size if crop_size is not None else self.crop_size
					__UpperCAmelCase    :     Dict													=	get_size_dict(a_       ,       param_name='''crop_size'''       ,       default_to_square=a_ )
					__UpperCAmelCase    :     int													=	do_rescale if do_rescale is not None else self.do_rescale
					__UpperCAmelCase    :     Optional[int]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
					__UpperCAmelCase    :     List[str]													=	do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase    :     Optional[int]													=	image_mean if image_mean is not None else self.image_mean
					__UpperCAmelCase    :     Tuple													=	image_std if image_std is not None else self.image_std
					__UpperCAmelCase    :     List[str]													=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__UpperCAmelCase    :     List[str]													=	make_list_of_images(a_ )

					if not valid_images(a_ ):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

					if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.''' )

					if do_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

					if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							__UpperCAmelCase    :     Optional[Any]													=	[convert_to_rgb(a_ ) for image in images]

					# All transformations expect numpy arrays.
					__UpperCAmelCase    :     int													=	[to_numpy_array(a_ ) for image in images]

					if do_resize:
							__UpperCAmelCase    :     int													=	[self.resize(image=a_       ,       size=a_       ,       resample=a_ ) for image in images]

					if do_center_crop:
							__UpperCAmelCase    :     List[str]													=	[self.center_crop(image=a_       ,       size=a_ ) for image in images]

					if do_rescale:
							__UpperCAmelCase    :     Dict													=	[self.rescale(image=a_       ,       scale=a_ ) for image in images]

					if do_normalize:
							__UpperCAmelCase    :     Optional[int]													=	[self.normalize(image=a_       ,       mean=a_       ,       std=a_ ) for image in images]

					__UpperCAmelCase    :     Optional[int]													=	[to_channel_dimension_format(a_       ,       a_ ) for image in images]

					__UpperCAmelCase    :     Union[str, Any]													=	{'''pixel_values''': images}
					return BatchFeature(data=a_       ,       tensor_type=a_ )





