"""simple docstring"""




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

import pytest

from .sola import PokerHand

_a   :	List[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)
)

_a   :	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'),
)

_a   :	Dict 					=     (
    ('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   :	Optional[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),
)

_a   :	Dict 					=     (
    ('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]),
)

_a   :	Dict 					=     (
    ('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   :	str 					=     (
    ('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 							SCREAMING_SNAKE_CASE  (    ) ->   Tuple:
  _lowerCAmelCase		: Optional[Any]						    =  randrange(len(_lowerCamelCase			)			), randrange(len(_lowerCamelCase			)			)
  _lowerCAmelCase		: List[Any]						    =  ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
  _lowerCAmelCase		: int						    =  SORTED_HANDS[play], SORTED_HANDS[oppo]
  return hand, other, expected





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any] = 100			) ->   List[Any]:
  return (generate_random_hand() for _ in range(_lowerCamelCase			))





@pytest.mark.parametrize("""hand, expected"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[Any]			) ->   str:
  assert PokerHand(_lowerCamelCase			)._is_flush() == expected





@pytest.mark.parametrize("""hand, expected"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str			) ->   Tuple:
  assert PokerHand(_lowerCamelCase			)._is_straight() == expected





@pytest.mark.parametrize("""hand, expected, card_values"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: int						    =  PokerHand(_lowerCamelCase			)
  assert player._is_five_high_straight() == expected
  assert player._card_values == card_values





@pytest.mark.parametrize("""hand, expected"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str			) ->   List[str]:
  assert PokerHand(_lowerCamelCase			)._is_same_kind() == expected





@pytest.mark.parametrize("""hand, expected"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: Tuple			) ->   Any:
  assert PokerHand(_lowerCamelCase			)._hand_type == expected





@pytest.mark.parametrize("""hand, other, expected"""    ,_lowerCamelCase			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: Union[str, Any]			) ->   Any:
  assert PokerHand(_lowerCamelCase			).compare_with(PokerHand(_lowerCamelCase			)			) == expected





@pytest.mark.parametrize("""hand, other, expected"""    ,generate_random_hands()			)
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Optional[Any]			) ->   Union[str, Any]:
  assert PokerHand(_lowerCamelCase			).compare_with(PokerHand(_lowerCamelCase			)			) == expected





def 							SCREAMING_SNAKE_CASE  (    ) ->   Dict:
  _lowerCAmelCase		: Optional[Any]						    =  [PokerHand(_lowerCamelCase			) for hand in SORTED_HANDS]
  _lowerCAmelCase		: List[str]						    =  poker_hands.copy()
  shuffle(_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  chain(sorted(_lowerCamelCase			)			)
  for index, hand in enumerate(_lowerCamelCase			):
    assert hand == poker_hands[index]





def 							SCREAMING_SNAKE_CASE  (    ) ->   Any:
  _lowerCAmelCase		: Tuple						    =  [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 							SCREAMING_SNAKE_CASE  (    ) ->   int:
  _lowerCAmelCase		: Union[str, Any]						    =  PokerHand("""2C 4S AS 3D 5C"""			)
  _lowerCAmelCase		: List[Any]						    =  True
  _lowerCAmelCase		: Optional[int]						    =  [5, 4, 3, 2, 14]
  for _ in range(10			):
    assert pokerhand._is_five_high_straight() == expected
    assert pokerhand._card_values == expected_card_values






def 							SCREAMING_SNAKE_CASE  (    ) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  0
  _lowerCAmelCase		: Optional[Any]						    =  os.path.abspath(os.path.dirname(_lowerCamelCase			)			)
  _lowerCAmelCase		: Optional[Any]						    =  os.path.join(_lowerCamelCase    ,"""poker_hands.txt"""			)
  with open(_lowerCamelCase			) as file_hand:
    for line in file_hand:
      _lowerCAmelCase		: Dict						    =  line[:14].strip()
      _lowerCAmelCase		: List[str]						    =  line[15:].strip()
      _lowerCAmelCase		: List[str]						    =  PokerHand(_lowerCamelCase			), PokerHand(_lowerCamelCase			)
      _lowerCAmelCase		: Dict						    =  player.compare_with(_lowerCamelCase			)
      if output == "Win":
        answer += 1
  assert answer == 376





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







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






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




