




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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       A   (		_a					,unittest.TestCase   ):
							lowercase_          =    KandinskyVaaControlnetImgaImgPipeline
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowercase_          =    [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_          =    False







							@property
							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							str:


														"""simple docstring"""


														return 32







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


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							int:


														"""simple docstring"""


														return 1_00







							@property
							def        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[Any]:


														"""simple docstring"""


														torch.manual_seed(0 )

														_a     					=    {
														    '''in_channels''': 8,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 8,
														    '''addition_embed_type''': '''image_hint''',
														    '''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,
														}

														_a     					=    UNetaDConditionModel(**lowerCAmelCase_ )
														return model







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


														"""simple docstring"""


														return {
														    "block_out_channels": [32, 32, 64, 64],
														    "down_block_types": [
														        "DownEncoderBlock2D",
														        "DownEncoderBlock2D",
														        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
														    "vq_embed_dim": 4,
														}







							@property
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Dict:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							str:


														"""simple docstring"""


														_a     					=    self.dummy_unet
														_a     					=    self.dummy_movq

														_a     					=    {
														    '''num_train_timesteps''': 10_00,
														    '''beta_schedule''': '''linear''',
														    '''beta_start''': 0.0_0_0_8_5,
														    '''beta_end''': 0.0_1_2,
														    '''clip_sample''': False,
														    '''set_alpha_to_one''': False,
														    '''steps_offset''': 0,
														    '''prediction_type''': '''epsilon''',
														    '''thresholding''': False,
														}

														_a     					=    DDIMScheduler(**lowerCAmelCase_ )

														_a     					=    {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[Any]=0 )      ->							str:


														"""simple docstring"""


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

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







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    '''cpu'''

														_a     					=    self.get_dummy_components()

														_a     					=    self.pipeline_class(**lowerCAmelCase_ )
														_a     					=    pipe.to(lowerCAmelCase_ )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    pipe(**self.get_dummy_inputs(lowerCAmelCase_ ) )
														_a     					=    output.images

														_a     					=    pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_ )							,      return_dict=lowerCAmelCase_							,      )[0]

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

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

														_a     					=    np.array(
														    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
														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       A   (		unittest.TestCase   ):







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


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Any:


														"""simple docstring"""


														_a     					=    load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )

														_a     					=    load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
														_a     					=    init_image.resize((5_12, 5_12) )

														_a     					=    load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/hint_image_cat.png''' )
														_a     					=    torch.from_numpy(np.array(lowerCAmelCase_ ) ).float() / 2_55.0
														_a     					=    hint.permute(2							,      0							,      1 ).unsqueeze(0 )

														_a     					=    '''A robot, 4k photo'''

														_a     					=    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''							,      torch_dtype=torch.floataa )
														pipe_prior.to(lowerCAmelCase_ )

														_a     					=    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''							,      torch_dtype=torch.floataa )
														_a     					=    pipeline.to(lowerCAmelCase_ )

														pipeline.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    torch.Generator(device='''cpu''' ).manual_seed(0 )

														_a					,							_a     					=    pipe_prior(
														    lowerCAmelCase_							,      image=lowerCAmelCase_							,      strength=0.8_5							,      generator=lowerCAmelCase_							,      negative_prompt=''''''							,      ).to_tuple()

														_a     					=    pipeline(
														    image=lowerCAmelCase_							,      image_embeds=lowerCAmelCase_							,      negative_image_embeds=lowerCAmelCase_							,      hint=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      num_inference_steps=1_00							,      height=5_12							,      width=5_12							,      strength=0.5							,      output_type='''np'''							,      )

														_a     					=    output.images[0]

														assert image.shape == (5_12, 5_12, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


from ..utils import DummyObject, requires_backends

class       A   (		metaclass=_a   ):
							lowercase_          =    ['torch', 'scipy']







							def __init__(  self	:  Tuple							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Any )      ->							Tuple:


														"""simple docstring"""


														requires_backends(self							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Dict							,      *lowerCAmelCase_	:  Dict							,      **lowerCAmelCase_	:  str )      ->							Tuple:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Optional[int]							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  List[str] )      ->							str:


														"""simple docstring"""


														requires_backends(cls							,      ['''torch''', '''scipy'''] )

