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

import pytest

from .sola import PokerHand

__UpperCamelCase		:       Optional[int]									=     (
    '''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		:       int									=     (
    ('''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		:       List[str]									=     (
    ('''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		:       Tuple									=     (
    ('''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		:       int									=     (
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
    ('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

__UpperCamelCase		:       str									=     (
    ('''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		:       Optional[int]									=     (
    ('''JH AH TH KH QH''', 23),
    ('''JH 9H TH KH QH''', 22),
    ('''JC KH JS JD JH''', 21),
    ('''KH KC 3S 3H 3D''', 20),
    ('''8C 9C 5C 3C TC''', 19),
    ('''JS QS 9H TS KH''', 18),
    ('''7C 7S KH 2H 7H''', 17),
    ('''3C KH 5D 5S KH''', 16),
    ('''QH 8H KD JH 8S''', 15),
    ('''2D 6D 9D TH 7D''', 14),
)
def       A	(    ):
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							randrange(len(__UpperCamelCase						)						), randrange(len(__UpperCamelCase						)						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Any							=							SORTED_HANDS[play], SORTED_HANDS[oppo]
						return hand, other, expected
def       A	(    _lowercase = 100						):
						return (generate_random_hand() for _ in range(__UpperCamelCase						))
@pytest.mark.parametrize('''hand, expected'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						)._is_flush() == expected
@pytest.mark.parametrize('''hand, expected'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						)._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							PokerHand(__UpperCamelCase						)
						assert player._is_five_high_straight() == expected
						assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						)._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						)._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected'''		,  __UpperCamelCase						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						).compare_with(PokerHand(__UpperCamelCase						)						) == expected
@pytest.mark.parametrize('''hand, other, expected'''		,  generate_random_hands()						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						assert PokerHand(__UpperCamelCase						).compare_with(PokerHand(__UpperCamelCase						)						) == expected
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    Dict							=							[PokerHand(__UpperCamelCase						) for hand in SORTED_HANDS]
						SCREAMING_SNAKE_CASE							:    Dict							=							poker_hands.copy()
						shuffle(__UpperCamelCase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							chain(sorted(__UpperCamelCase						)						)
						for index, hand in enumerate(__UpperCamelCase						):
												assert hand == poker_hands[index]
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    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	(    ):
						SCREAMING_SNAKE_CASE							:    Any							=							PokerHand('''2C 4S AS 3D 5C'''						)
						SCREAMING_SNAKE_CASE							:    int							=							True
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[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	(    ):
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							0
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							os.path.abspath(os.path.dirname(__UpperCamelCase						)						)
						SCREAMING_SNAKE_CASE							:    Any							=							os.path.join(__UpperCamelCase		,  '''poker_hands.txt'''						)
						with open(__UpperCamelCase						) as file_hand:
												for line in file_hand:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							line[:14].strip()
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							line[15:].strip()
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							PokerHand(__UpperCamelCase						), PokerHand(__UpperCamelCase						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							player.compare_with(__UpperCamelCase						)
																		if output == "Win":
																								answer += 1
						assert answer == 376






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaImgaImgPipeline
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "image"]
   lowerCAmelCase_														=							[
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   lowerCAmelCase_														=							[
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCAmelCase_														=							False




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return 1_00




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )

     SCREAMING_SNAKE_CASE__										=      {
         """in_channels""": 4,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image""",
         """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
         """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
         """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
         """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
         """layers_per_block""": 1,
         """encoder_hid_dim""": self.text_embedder_hidden_size,
         """encoder_hid_dim_type""": """image_proj""",
         """cross_attention_dim""": self.cross_attention_dim,
         """attention_head_dim""": 4,
         """resnet_time_scale_shift""": """scale_shift""",
         """class_embed_type""": None,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_lowercase      )
     return model




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      VQModel(**self.dummy_movq_kwargs      )
     return model




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      {
         """num_train_timesteps""": 10_00,
         """beta_schedule""": """linear""",
         """beta_start""": 0.0_00_85,
         """beta_end""": 0.0_12,
         """clip_sample""": False,
         """set_alpha_to_one""": False,
         """steps_offset""": 0,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
     }

     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _lowercase      )
     # create init_image
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((2_56, 2_56)      )

     if str(_lowercase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {
         """image""": init_image,
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """num_inference_steps""": 10,
         """guidance_scale""": 7.0,
         """strength""": 0.2,
         """output_type""": """np""",
     }
     return inputs




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """cpu"""

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_lowercase      )

     pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_lowercase      )       ,	return_dict=_lowercase       ,	)[0]

     SCREAMING_SNAKE_CASE__										=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__										=      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__										=      np.array(
         [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
     assert (
         np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







@slow
@require_torch_gpu
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
     SCREAMING_SNAKE_CASE__										=      """A red cartoon frog, 4k"""

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""       ,	torch_dtype=torch.floataa      )
     pipe_prior.to(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaImgaImgPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_lowercase      )

     pipeline.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cpu"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      pipe_prior(
         _lowercase       ,	generator=_lowercase       ,	num_inference_steps=5       ,	negative_prompt=""""""       ,	).to_tuple()

     SCREAMING_SNAKE_CASE__										=      pipeline(
         image=_lowercase       ,	image_embeds=_lowercase       ,	negative_image_embeds=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(_lowercase       ,	_lowercase      )
