

'''simple docstring'''

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

import pytest

from .sola import PokerHand

_UpperCAmelCase				: Any							    =				(
    """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)
)

_UpperCAmelCase				: str							    =				(
    ("""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"""),
)

_UpperCAmelCase				: Optional[Any]							    =				(
    ("""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),
)

_UpperCAmelCase				: Union[str, Any]							    =				(
    ("""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),
)

_UpperCAmelCase				: Optional[Any]							    =				(
    ("""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]),
)

_UpperCAmelCase				: Union[str, Any]							    =				(
    ("""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),
)

_UpperCAmelCase				: Dict							    =				(
    ("""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 							__magic_name__(						):
						__lowerCAmelCase							       =				randrange(len(lowerCamelCase)), randrange(len(lowerCamelCase))
						__lowerCAmelCase							       =				["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
						__lowerCAmelCase							       =				SORTED_HANDS[play], SORTED_HANDS[oppo]
						return hand, other, expected

def 							__magic_name__(						lowerCamelCase = 1_0_0):
						return (generate_random_hand() for _ in range(lowerCamelCase))

@pytest.mark.parametrize('''hand, expected''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase)._is_flush() == expected

@pytest.mark.parametrize('''hand, expected''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase)._is_straight() == expected

@pytest.mark.parametrize('''hand, expected, card_values''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				PokerHand(lowerCamelCase)
						assert player._is_five_high_straight() == expected
						assert player._card_values == card_values

@pytest.mark.parametrize('''hand, expected''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase)._is_same_kind() == expected

@pytest.mark.parametrize('''hand, expected''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase)._hand_type == expected

@pytest.mark.parametrize('''hand, other, expected''',				lowerCamelCase)
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase).compare_with(PokerHand(lowerCamelCase)) == expected

@pytest.mark.parametrize('''hand, other, expected''',				generate_random_hands())
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						assert PokerHand(lowerCamelCase).compare_with(PokerHand(lowerCamelCase)) == expected

def 							__magic_name__(						):
						__lowerCAmelCase							       =				[PokerHand(lowerCamelCase) for hand in SORTED_HANDS]
						__lowerCAmelCase							       =				poker_hands.copy()
						shuffle(lowerCamelCase)
						__lowerCAmelCase							       =				chain(sorted(lowerCamelCase))
						for index, hand in enumerate(lowerCamelCase):
												assert hand == poker_hands[index]

def 							__magic_name__(						):
						__lowerCAmelCase							       =				[PokerHand('''2D AC 3H 4H 5S'''), PokerHand('''2S 3H 4H 5S 6C''')]
						pokerhands.sort(reverse=lowerCamelCase)
						assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"

def 							__magic_name__(						):
						__lowerCAmelCase							       =				PokerHand('''2C 4S AS 3D 5C''')
						__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				[5, 4, 3, 2, 1_4]
						for _ in range(1_0):
												assert pokerhand._is_five_high_straight() == expected
												assert pokerhand._card_values == expected_card_values




def 							__magic_name__(						):
						__lowerCAmelCase							       =				0
						__lowerCAmelCase							       =				os.path.abspath(os.path.dirname(lowerCamelCase))
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				'''poker_hands.txt''')
						with open(lowerCamelCase) as file_hand:
												for line in file_hand:
																		__lowerCAmelCase							       =				line[:1_4].strip()
																		__lowerCAmelCase							       =				line[1_5:].strip()
																		__lowerCAmelCase							       =				PokerHand(lowerCamelCase), PokerHand(lowerCamelCase)
																		__lowerCAmelCase							       =				player.compare_with(lowerCamelCase)
																		if output == "Win":
																								answer += 1
						assert answer == 3_7_6





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

