



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   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		KandinskyVaaControlnetImgaImgPipeline
							lowerCamelCase__   			=		['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowerCamelCase__   			=		['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowerCamelCase__   			=		[
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowerCamelCase__   			=		False







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 32







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 32







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.time_input_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.time_input_dim * 4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 100







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase     :							int    =    {
														    "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,
														}

														_lowerCAmelCase     :							Dict    =    UNetaDConditionModel(**__a)
														return model







							@property
							def    snake_case__      (   self):

														'''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    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.dummy_unet
														_lowerCAmelCase     :							Dict    =    self.dummy_movq

														_lowerCAmelCase     :							Optional[int]    =    {
														    "num_train_timesteps": 1000,
														    "beta_schedule": "linear",
														    "beta_start": 0.00_085,
														    "beta_end": 0.012,
														    "clip_sample": False,
														    "set_alpha_to_one": False,
														    "steps_offset": 0,
														    "prediction_type": "epsilon",
														    "thresholding": False,
														}

														_lowerCAmelCase     :							int    =    DDIMScheduler(**__a)

														_lowerCAmelCase     :							List[str]    =    {
														    "unet": unet,
														    "scheduler": scheduler,
														    "movq": movq,
														}

														return components







							def    snake_case__      (   self,			__a,			__a=0):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(__a)).to(__a)
														_lowerCAmelCase     :							List[str]    =    floats_tensor((1, self.text_embedder_hidden_size),			rng=random.Random(seed + 1)).to(
														    __a)
														# create init_image
														_lowerCAmelCase     :							Tuple    =    floats_tensor((1, 3, 64, 64),			rng=random.Random(__a)).to(__a)
														_lowerCAmelCase     :							Optional[Any]    =    image.cpu().permute(0,			2,			3,			1)[0]
														_lowerCAmelCase     :							Tuple    =    Image.fromarray(np.uinta(__a)).convert("RGB").resize((256, 256))
														# create hint
														_lowerCAmelCase     :							Dict    =    floats_tensor((1, 3, 64, 64),			rng=random.Random(__a)).to(__a)

														if str(__a).startswith("mps"):
																					_lowerCAmelCase     :							str    =    torch.manual_seed(__a)
														else:
																					_lowerCAmelCase     :							int    =    torch.Generator(device=__a).manual_seed(__a)
														_lowerCAmelCase     :							List[Any]    =    {
														    "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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    "cpu"

														_lowerCAmelCase     :							Optional[int]    =    self.get_dummy_components()

														_lowerCAmelCase     :							Any    =    self.pipeline_class(**__a)
														_lowerCAmelCase     :							Union[str, Any]    =    pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase     :							List[Any]    =    pipe(**self.get_dummy_inputs(__a))
														_lowerCAmelCase     :							Optional[int]    =    output.images

														_lowerCAmelCase     :							Tuple    =    pipe(
														    **self.get_dummy_inputs(__a),			return_dict=__a,			)[0]

														_lowerCAmelCase     :							int    =    image[0, -3:, -3:, -1]
														_lowerCAmelCase     :							Dict    =    image_from_tuple[0, -3:, -3:, -1]

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

														_lowerCAmelCase     :							Optional[Any]    =    np.array(
														    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736])
														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   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy")

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

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

														_lowerCAmelCase     :							Any    =    "A robot, 4k photo"

														_lowerCAmelCase     :							List[str]    =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
														    "kandinsky-community/kandinsky-2-2-prior",			torch_dtype=torch.floataa)
														pipe_prior.to(__a)

														_lowerCAmelCase     :							str    =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
														    "kandinsky-community/kandinsky-2-2-controlnet-depth",			torch_dtype=torch.floataa)
														_lowerCAmelCase     :							Dict    =    pipeline.to(__a)

														pipeline.set_progress_bar_config(disable=__a)

														_lowerCAmelCase     :							Dict    =    torch.Generator(device="cpu").manual_seed(0)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    pipe_prior(
														    __a,			image=__a,			strength=0.85,			generator=__a,			negative_prompt="",			).to_tuple()

														_lowerCAmelCase     :							Optional[Any]    =    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",			)

														_lowerCAmelCase     :							int    =    output.images[0]

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

														assert_mean_pixel_difference(__a,			__a)





def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count < 0:
														raise ValueError("The given input must be positive"  )

							# get the generated string sequence
							_lowerCAmelCase     :							List[str]    =    gray_code_sequence_string(_lowerCamelCase  )
							#
							# convert them to integers
							for i in range(len(_lowerCamelCase  )  ):
														_lowerCAmelCase     :							List[str]    =    int(sequence[i]				, 2  )

							return sequence






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							_lowerCAmelCase     :							List[Any]    =    1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							_lowerCAmelCase     :							Optional[int]    =    gray_code_sequence_string(bit_count - 1  )

							_lowerCAmelCase     :							str    =    []

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2  ):
														_lowerCAmelCase     :							Dict    =    "0" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2  )  ):
														_lowerCAmelCase     :							Optional[Any]    =    "1" + smaller_sequence[i]
														sequence.append(_lowerCamelCase  )

							return sequence


if __name__ == "__main__":
						import doctest

						doctest.testmod()
