

"""simple docstring"""





import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[Any]	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 @property
 def 					_lowercase (			self							:			str	):
    __lowercase								=						1
    __lowercase								=						3
    __lowercase								=						(3_2, 3_2)

    __lowercase								=						floats_tensor((batch_size, num_channels) + sizes,					rng=random.Random(0	)	).to(UpperCAmelCase__	)
    return image


 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    torch.manual_seed(0	)
    __lowercase								=						UNetaDConditionModel(
        block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),					cross_attention_dim=3_2,					)
    return model


 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    torch.manual_seed(0	)
    __lowercase								=						AutoencoderKL(
        block_out_channels=[3_2, 6_4],					in_channels=3,					out_channels=3,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],					latent_channels=4,					)
    return model


 @property
 def 					_lowercase (			self							:			Dict	):
    torch.manual_seed(0	)
    __lowercase								=						CLIPTextConfig(
        bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
    return CLIPTextModel(UpperCAmelCase__	)


 @property
 def 					_lowercase (			self							:			str	):
    def extract(*UpperCAmelCase__							:			Tuple,					**UpperCAmelCase__							:			str	):




       class 					_lowerCAmelCase  :







        """simple docstring"""







        def __init__(			self							:			int	):
           __lowercase								=						torch.ones([0]	)



        def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Dict	):
           self.pixel_values.to(UpperCAmelCase__	)
           return self







       return Out()

    return extract


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						DDIMScheduler(
        beta_start=0.00_085,					beta_end=0.012,					beta_schedule="scaled_linear",					clip_sample=UpperCAmelCase__,					set_alpha_to_one=UpperCAmelCase__,					)

    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=self.dummy_extractor,					)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A painting of a squirrel eating a burger"

    __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(0	)
    __lowercase								=						sd_pipe([prompt],					generator=UpperCAmelCase__,					guidance_scale=6.0,					num_inference_steps=2,					output_type="np"	)
    __lowercase								=						output.images

    __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(0	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=6.0,					num_inference_steps=2,					output_type="np",					return_dict=UpperCAmelCase__,					)[0]

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

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=						np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]	)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						PNDMScheduler(skip_prk_steps=UpperCAmelCase__	)
    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=self.dummy_extractor,					)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A painting of a squirrel eating a burger"
    __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(0	)
    __lowercase								=						sd_pipe([prompt],					generator=UpperCAmelCase__,					guidance_scale=6.0,					num_inference_steps=2,					output_type="np"	)

    __lowercase								=						output.images

    __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(0	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=6.0,					num_inference_steps=2,					output_type="np",					return_dict=UpperCAmelCase__,					)[0]

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

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=						np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]	)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2


 def 					_lowercase (			self							:			int	):
    __lowercase								=						StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-lms-pipe",					safety_checker=UpperCAmelCase__	)
    assert isinstance(UpperCAmelCase__,					UpperCAmelCase__	)
    assert isinstance(pipe.scheduler,					UpperCAmelCase__	)
    assert pipe.safety_checker is None

    __lowercase								=						pipe("example prompt",					num_inference_steps=2	).images[0]
    assert image is not None

    # check that there's no error when saving a pipeline with one of the models being None
    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(UpperCAmelCase__	)
       __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)

    # sanity check that the pipeline still works
    assert pipe.safety_checker is None
    __lowercase								=						pipe("example prompt",					num_inference_steps=2	).images[0]
    assert image is not None



 @unittest.skipIf(torch_device != "cuda",					"This test requires a GPU"	)
 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						PNDMScheduler(skip_prk_steps=UpperCAmelCase__	)
    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    # put models in fp16
    __lowercase								=						unet.half()
    __lowercase								=						vae.half()
    __lowercase								=						bert.half()

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=self.dummy_extractor,					)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A painting of a squirrel eating a burger"
    __lowercase								=						sd_pipe([prompt],					num_inference_steps=2,					output_type="np"	).images

    assert image.shape == (1, 6_4, 6_4, 3)







@nightly
@require_torch_gpu
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			int	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",					safety_checker=UpperCAmelCase__	)
    __lowercase								=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						(
        "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
        " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
        " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
        " children from bahnhof zoo, detailed "
    )
    __lowercase								=						4_0_0_3_6_6_0_3_4_6
    __lowercase								=						7

    # without safety guidance (sld_guidance_scale = 0)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=0,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

    # without safety guidance (strong configuration)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=2_0_0_0,					sld_warmup_steps=7,					sld_threshold=0.025,					sld_momentum_scale=0.5,					sld_mom_beta=0.7,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2


 def 					_lowercase (			self							:			str	):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",					safety_checker=UpperCAmelCase__	)
    __lowercase								=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"padme amidala taking a bath artwork, safe for work, no nudity"
    __lowercase								=						2_7_3_4_9_7_1_7_5_5
    __lowercase								=						7

    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=0,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=2_0_0_0,					sld_warmup_steps=7,					sld_threshold=0.025,					sld_momentum_scale=0.5,					sld_mom_beta=0.7,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
    __lowercase								=						sd_pipe.to(UpperCAmelCase__	)
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						(
        "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
        " leyendecker"
    )
    __lowercase								=						1_0_4_4_3_5_5_2_3_4
    __lowercase								=						1_2

    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=0,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-7

    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						sd_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					num_inference_steps=5_0,					output_type="np",					width=5_1_2,					height=5_1_2,					sld_guidance_scale=2_0_0_0,					sld_warmup_steps=7,					sld_threshold=0.025,					sld_momentum_scale=0.5,					sld_mom_beta=0.7,					)

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]	)
    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	MCLIPConfig(
            numDims=self.cross_attention_dim    ,			transformerDimensions=self.text_embedder_hidden_size    ,			hidden_size=self.text_embedder_hidden_size    ,			intermediate_size=37    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			vocab_size=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            'in_channels': 9,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'text_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': 'text_image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

        print(f'image.shape {image.shape}')

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

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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()}'



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

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

        assert_mean_pixel_difference(lowercase    ,			lowercase)


