"""simple docstring"""






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 lowerCamelCase		(__lowercase    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		KandinskyVaaControlnetImgaImgPipeline
					a   	=		["image_embeds", "negative_image_embeds", "image", "hint"]
					a   	=		["image_embeds", "negative_image_embeds", "image", "hint"]
					a   	=		[
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					a   	=		False



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Any:
									return 32



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       List[str]:
									return 32



					@property
					def 			lowerCAmelCase_					(						self			:				str		)							->       Dict:
									return self.time_input_dim



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       str:
									return self.time_input_dim * 4



					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									return 100



					@property
					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__		  =				{
									    "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,
									}

									SCREAMING_SNAKE_CASE__		  =				UNetaDConditionModel(**_A		)
									return model



					@property
					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[Any]:
									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			:				List[Any]		)							->       Any:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				VQModel(**self.dummy_movq_kwargs		)
									return model



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.dummy_unet
									SCREAMING_SNAKE_CASE__		  =				self.dummy_movq

									SCREAMING_SNAKE_CASE__		  =				{
									    "num_train_timesteps": 1000,
									    "beta_schedule": "linear",
									    "beta_start": 0.0_0085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler(**_A		)

									SCREAMING_SNAKE_CASE__		  =				{
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Dict   , _snake_case			:				Dict=0		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(_A		)		).to(_A		)
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(seed + 1		)		).to(
									    _A		)
									# create init_image
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((1, 3, 64, 64)   , rng=random.Random(_A		)		).to(_A		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		)[0]
									SCREAMING_SNAKE_CASE__		  =				Image.fromarray(np.uinta(_A		)		).convert("RGB"		).resize((256, 256)		)
									# create hint
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((1, 3, 64, 64)   , rng=random.Random(_A		)		).to(_A		)

									if str(_A		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(_A		)
									else:
													SCREAMING_SNAKE_CASE__		  =				torch.Generator(device=_A		).manual_seed(_A		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "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			:				Tuple		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				"cpu"

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()

									SCREAMING_SNAKE_CASE__		  =				self.pipeline_class(**_A		)
									SCREAMING_SNAKE_CASE__		  =				pipe.to(_A		)

									pipe.set_progress_bar_config(disable=_A		)

									SCREAMING_SNAKE_CASE__		  =				pipe(**self.get_dummy_inputs(_A		)		)
									SCREAMING_SNAKE_CASE__		  =				output.images

									SCREAMING_SNAKE_CASE__		  =				pipe(
									    **self.get_dummy_inputs(_A		)   , return_dict=_A   , )[0]

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

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

									SCREAMING_SNAKE_CASE__		  =				np.array(
									    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]		)
									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 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"		)

									SCREAMING_SNAKE_CASE__		  =				load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
									SCREAMING_SNAKE_CASE__		  =				init_image.resize((512, 512)		)

									SCREAMING_SNAKE_CASE__		  =				load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinskyv22/hint_image_cat.png"		)
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(np.array(_A		)		).float() / 255.0
									SCREAMING_SNAKE_CASE__		  =				hint.permute(2   , 0   , 1		).unsqueeze(0		)

									SCREAMING_SNAKE_CASE__		  =				"A robot, 4k photo"

									SCREAMING_SNAKE_CASE__		  =				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-prior"   , torch_dtype=torch.floataa		)
									pipe_prior.to(_A		)

									SCREAMING_SNAKE_CASE__		  =				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-controlnet-depth"   , torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__		  =				pipeline.to(_A		)

									pipeline.set_progress_bar_config(disable=_A		)

									SCREAMING_SNAKE_CASE__		  =				torch.Generator(device="cpu"		).manual_seed(0		)

									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				pipe_prior(
									    _A   , image=_A   , strength=0.85   , generator=_A   , negative_prompt=""   , ).to_tuple()

									SCREAMING_SNAKE_CASE__		  =				pipeline(
									    image=_A   , image_embeds=_A   , negative_image_embeds=_A   , hint=_A   , generator=_A   , num_inference_steps=100   , height=512   , width=512   , strength=0.5   , output_type="np"   , )

									SCREAMING_SNAKE_CASE__		  =				output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(_A   , _A		)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




