



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_    ( _A						,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 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				List[Any]:
									return 32







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







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									return self.time_input_dim







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







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Union[str, Any]:
									return 1_00







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									torch.manual_seed(0 )

									A							:					str              =						{
									    "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							:					Optional[int]              =						UNetaDConditionModel(**__lowerCamelCase )
									return model







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Any:
									A							:					Optional[Any]              =						self.dummy_unet
									A							:					Optional[Any]              =						self.dummy_movq

									A							:					Optional[Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_schedule": "linear",
									    "beta_start": 0.00085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									A							:					str              =						DDIMScheduler(**__lowerCamelCase )

									A							:					Tuple              =						{
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[str]=0 )      ->				Union[str, Any]:
									A							:					List[Any]              =						floats_tensor((1, self.text_embedder_hidden_size)	,	rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									A							:					Optional[int]              =						floats_tensor((1, self.text_embedder_hidden_size)	,	rng=random.Random(seed + 1 ) ).to(
									    __lowerCamelCase )
									# create init_image
									A							:					Any              =						floats_tensor((1, 3, 64, 64)	,	rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									A							:					Union[str, Any]              =						image.cpu().permute(0	,	2	,	3	,	1 )[0]
									A							:					List[Any]              =						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((2_56, 2_56) )
									# create hint
									A							:					List[Any]              =						floats_tensor((1, 3, 64, 64)	,	rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )

									if str(__lowerCamelCase ).startswith("mps" ):
												A							:					List[str]              =						torch.manual_seed(__lowerCamelCase )
									else:
												A							:					str              =						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									A							:					List[str]              =						{
									    "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 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					int              =						"cpu"

									A							:					Union[str, Any]              =						self.get_dummy_components()

									A							:					List[str]              =						self.pipeline_class(**__lowerCamelCase )
									A							:					List[Any]              =						pipe.to(__lowerCamelCase )

									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					str              =						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
									A							:					List[Any]              =						output.images

									A							:					Dict              =						pipe(
									    **self.get_dummy_inputs(__lowerCamelCase )	,	return_dict=__lowerCamelCase	,	)[0]

									A							:					int              =						image[0, -3:, -3:, -1]
									A							:					Any              =						image_from_tuple[0, -3:, -3:, -1]

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

									A							:					List[str]              =						np.array(
									    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
									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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







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

									A							:					Tuple              =						load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
									A							:					List[str]              =						init_image.resize((5_12, 5_12) )

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

									A							:					Tuple              =						"A robot, 4k photo"

									A							:					Union[str, Any]              =						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-prior"	,	torch_dtype=torch.floataa )
									pipe_prior.to(__lowerCamelCase )

									A							:					int              =						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-controlnet-depth"	,	torch_dtype=torch.floataa )
									A							:					Union[str, Any]              =						pipeline.to(__lowerCamelCase )

									pipeline.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Tuple              =						torch.Generator(device="cpu" ).manual_seed(0 )

									A       , A							:					Any              =						pipe_prior(
									    __lowerCamelCase	,	image=__lowerCamelCase	,	strength=0.85	,	generator=__lowerCamelCase	,	negative_prompt=""	,	).to_tuple()

									A							:					Any              =						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							:					int              =						output.images[0]

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

									assert_mean_pixel_difference(__lowerCamelCase	,	__lowerCamelCase )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """InstructBlipQFormerModel""",
							    """InstructBlipPreTrainedModel""",
							    """InstructBlipForConditionalGeneration""",
							    """InstructBlipVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)