






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 __lowerCamelCase (      __a     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      KandinskyVaaImgaImgPipeline
	UpperCamelCase__								=      ["""image_embeds""", """negative_image_embeds""", """image"""]
	UpperCamelCase__								=      [
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	]
	UpperCamelCase__								=      [
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	UpperCamelCase__								=      False

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return 32

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return 32

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.time_input_dim

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.time_input_dim * 4

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return 100

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )

							_UpperCAmelCase									=			{
							    '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,
							}

							_UpperCAmelCase									=			UNetaDConditionModel(**UpperCAmelCase    )
							return model

	@property
	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.dummy_unet
							_UpperCAmelCase									=			self.dummy_movq

							_UpperCAmelCase									=			{
							    'num_train_timesteps': 1000,
							    '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,
							}

							_UpperCAmelCase									=			DDIMScheduler(**UpperCAmelCase    )

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

							return components

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, self.text_embedder_hidden_size)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			floats_tensor((1, self.text_embedder_hidden_size)       ,    rng=random.Random(seed + 1    )    ).to(
							    UpperCAmelCase    )
							# create init_image
							_UpperCAmelCase									=			floats_tensor((1, 3, 64, 64)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
							_UpperCAmelCase									=			Image.fromarray(np.uinta(UpperCAmelCase    )    ).convert('RGB'    ).resize((256, 256)    )

							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    '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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()

							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							_UpperCAmelCase									=			pipe.to(UpperCAmelCase    )

							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			pipe(**self.get_dummy_inputs(UpperCAmelCase    )    )
							_UpperCAmelCase									=			output.images

							_UpperCAmelCase									=			pipe(
							    **self.get_dummy_inputs(UpperCAmelCase    )       ,    return_dict=UpperCAmelCase       ,    )[0]

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

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

							_UpperCAmelCase									=			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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'       ,    torch_dtype=torch.floataa    )
							pipe_prior.to(UpperCAmelCase    )

							_UpperCAmelCase									=			KandinskyVaaImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder'       ,    torch_dtype=torch.floataa    )
							_UpperCAmelCase									=			pipeline.to(UpperCAmelCase    )

							pipeline.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			torch.Generator(device='cpu'    ).manual_seed(0    )
							_UpperCAmelCase					, _UpperCAmelCase									=			pipe_prior(
							    UpperCAmelCase       ,    generator=UpperCAmelCase       ,    num_inference_steps=5       ,    negative_prompt=''       ,    ).to_tuple()

							_UpperCAmelCase									=			pipeline(
							    image=UpperCAmelCase       ,    image_embeds=UpperCAmelCase       ,    negative_image_embeds=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    num_inference_steps=100       ,    height=768       ,    width=768       ,    strength=0.2       ,    output_type='np'       ,    )

							_UpperCAmelCase									=			output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(UpperCAmelCase       ,    UpperCAmelCase    )






'''simple docstring'''







import os
from distutils.util import strtobool





def 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							List[Any]     ):
    for e in env_keys:
        UpperCAmelCase			       =		int(os.environ.get(_a    ,   -1     )     )
        if val >= 0:
            return val
    return default





def 						snake_case_							(_a	:							Dict    ,   _a	:							Any=False     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return strtobool(_a     ) == 1  # As its name indicates `strtobool` actually returns an int...







def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]="no"     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return value




