'''simple docstring'''




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

import pytest

from .sola import PokerHand

_a   :	Tuple 							=   (
    "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   :	Dict 							=   (
    ("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   :	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),
)

_a   :	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   :	Tuple 							=   (
    ("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   :	Union[str, Any] 							=   (
    ("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 _lowercase	(      )					->       Dict:





							"""simple docstring"""


							__UpperCAmelCase  :				Optional[Any]       				=		randrange(len(_lowerCamelCase						)						), randrange(len(_lowerCamelCase						)						)
							__UpperCAmelCase  :				Optional[Any]       				=		["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
							__UpperCAmelCase  :				List[str]       				=		SORTED_HANDS[play], SORTED_HANDS[oppo]
							return hand, other, expected

def _lowercase	(      lowerCamelCase__ = 100						)					->       List[str]:





							"""simple docstring"""


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

@pytest.mark.parametrize("hand, expected"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[str]:





							"""simple docstring"""


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

@pytest.mark.parametrize("hand, expected"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





							"""simple docstring"""


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

@pytest.mark.parametrize("hand, expected, card_values"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





							"""simple docstring"""


							__UpperCAmelCase  :				Tuple       				=		PokerHand(_lowerCamelCase						)
							assert player._is_five_high_straight() == expected
							assert player._card_values == card_values

@pytest.mark.parametrize("hand, expected"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Dict:





							"""simple docstring"""


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

@pytest.mark.parametrize("hand, expected"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





							"""simple docstring"""


							assert PokerHand(_lowerCamelCase						)._hand_type == expected

@pytest.mark.parametrize("hand, other, expected"							,    _lowerCamelCase						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





							"""simple docstring"""


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

@pytest.mark.parametrize("hand, other, expected"							,    generate_random_hands()						)
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





							"""simple docstring"""


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

def _lowercase	(      )					->       List[str]:





							"""simple docstring"""


							__UpperCAmelCase  :				List[str]       				=		[PokerHand(_lowerCamelCase						) for hand in SORTED_HANDS]
							__UpperCAmelCase  :				Dict       				=		poker_hands.copy()
							shuffle(_lowerCamelCase						)
							__UpperCAmelCase  :				Any       				=		chain(sorted(_lowerCamelCase						)						)
							for index, hand in enumerate(_lowerCamelCase						):
														assert hand == poker_hands[index]

def _lowercase	(      )					->       Any:





							"""simple docstring"""


							__UpperCAmelCase  :				Union[str, Any]       				=		[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 _lowercase	(      )					->       Union[str, Any]:





							"""simple docstring"""


							__UpperCAmelCase  :				str       				=		PokerHand("2C 4S AS 3D 5C"						)
							__UpperCAmelCase  :				str       				=		True
							__UpperCAmelCase  :				List[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 _lowercase	(      )					->       Union[str, Any]:





							"""simple docstring"""


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







'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]						)






class 		__A       (__magic_name__ ,					__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					StableDiffusionLatentUpscalePipeline
       snake_case       :Optional[int]				    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       snake_case       :List[str]				    =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       snake_case       :Optional[Any]				    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       snake_case       :Optional[Any]				    =					frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       snake_case       :Any				    =					frozenset([]   )

       snake_case       :Optional[int]				    =					True






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		1
              __UpperCAmelCase  :				Dict       				=		4
              __UpperCAmelCase  :				List[str]       				=		(16, 16)

              __UpperCAmelCase  :				Dict       				=		floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0      )      ).to(UpperCamelCase_      )
              return image






       def 			_snake_case						(      self      ):
              torch.manual_seed(0      )
              __UpperCAmelCase  :				List[str]       				=		UNetaDConditionModel(
                  act_fn="gelu"				,     attention_head_dim=8				,     norm_num_groups=UpperCamelCase_				,     block_out_channels=[32, 32, 64, 64]				,     time_cond_proj_dim=1_60				,     conv_in_kernel=1				,     conv_out_kernel=1				,     cross_attention_dim=32				,     down_block_types=(
                      "KDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                  )				,     in_channels=8				,     mid_block_type=UpperCamelCase_				,     only_cross_attention=UpperCamelCase_				,     out_channels=5				,     resnet_time_scale_shift="scale_shift"				,     time_embedding_type="fourier"				,     timestep_post_act="gelu"				,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,     )
              __UpperCAmelCase  :				int       				=		AutoencoderKL(
                  block_out_channels=[32, 32, 64, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=[
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                  ]				,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]				,     latent_channels=4				,     )
              __UpperCAmelCase  :				Optional[int]       				=		EulerDiscreteScheduler(prediction_type="sample"      )
              __UpperCAmelCase  :				int       				=		CLIPTextConfig(
                  bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     hidden_act="quick_gelu"				,     projection_dim=5_12				,     )
              __UpperCAmelCase  :				List[str]       				=		CLIPTextModel(UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		{
                  "unet": model.eval(),
                  "vae": vae.eval(),
                  "scheduler": scheduler,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
              }

              return components






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=0      ):
              if str(UpperCamelCase_      ).startswith("mps"      ):
                     __UpperCAmelCase  :				str       				=		torch.manual_seed(UpperCamelCase_      )
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{
                  "prompt": "A painting of a squirrel eating a burger",
                  "image": self.dummy_image.cpu(),
                  "generator": generator,
                  "num_inference_steps": 2,
                  "output_type": "numpy",
              }
              return inputs






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		"cpu"

              __UpperCAmelCase  :				List[str]       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Tuple       				=		self.pipeline_class(**UpperCamelCase_      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				Any       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		pipe(**UpperCamelCase_      ).images
              __UpperCAmelCase  :				Any       				=		image[0, -3:, -3:, -1]

              self.assertEqual(image.shape				,     (1, 2_56, 2_56, 3)      )
              __UpperCAmelCase  :				Tuple       				=		np.array(
                  [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
              __UpperCAmelCase  :				List[str]       				=		np.abs(image_slice.flatten() - expected_slice      ).max()
              self.assertLessEqual(UpperCamelCase_				,     1E-3      )






       def 			_snake_case						(      self      ):
              super().test_attention_slicing_forward_pass(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_cpu_offload_forward_pass(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_inference_batch_single_identical(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_local(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_optional_components(expected_max_difference=3E-3      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		[
                  "DDIMScheduler",
                  "DDPMScheduler",
                  "PNDMScheduler",
                  "HeunDiscreteScheduler",
                  "EulerAncestralDiscreteScheduler",
                  "KDPM2DiscreteScheduler",
                  "KDPM2AncestralDiscreteScheduler",
                  "DPMSolverSDEScheduler",
              ]
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Union[str, Any]       				=		self.pipeline_class(**UpperCamelCase_      )

              # make sure that PNDM does not need warm-up
              pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_      )

              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		2

              __UpperCAmelCase  :				List[str]       				=		[]
              for scheduler_enum in KarrasDiffusionSchedulers:
                     if scheduler_enum.name in skip_schedulers:
                            # no sigma schedulers are not supported
                            # no schedulers
                            continue

                     __UpperCAmelCase  :				Optional[int]       				=		getattr(UpperCamelCase_				,     scheduler_enum.name      )
                     __UpperCAmelCase  :				List[str]       				=		scheduler_cls.from_config(pipe.scheduler.config      )
                     __UpperCAmelCase  :				Optional[int]       				=		pipe(**UpperCamelCase_      )[0]
                     outputs.append(UpperCamelCase_      )

              assert check_same_shape(UpperCamelCase_      )






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






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				str       				=		StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"				,     torch_dtype=torch.floataa      )
              pipe.to("cuda"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[int]       				=		"a photo of an astronaut high resolution, unreal engine, ultra realistic"

              __UpperCAmelCase  :				Any       				=		pipe(UpperCamelCase_				,     generator=UpperCamelCase_				,     output_type="latent"      ).images

              __UpperCAmelCase  :				int       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Optional[Any]       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
              assert np.abs((expected_image - image).mean()      ) < 5E-2







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[Any]       				=		"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

              __UpperCAmelCase  :				str       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

              __UpperCAmelCase  :				Dict       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Tuple       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
              assert np.abs((expected_image - image).max()      ) < 5E-2






