


'''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 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	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       __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							)

						__a			:       Any					    =  {
						    '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			:       List[str]					    =  UNetaDConditionModel(**__a							)
						return model




	@property
	def       __UpperCAmelCase							(   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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       List[str]					    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.dummy_unet
						__a			:       Any					    =  self.dummy_movq

						__a			:       Dict					    =  {
						    'num_train_timesteps': 1000,
						    '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(**__a							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




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

						'''simple docstring'''





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

						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       Tuple					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    '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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  'cpu'

						__a			:       List[Any]					    =  self.get_dummy_components()

						__a			:       Any					    =  self.pipeline_class(**__a							)
						__a			:       Tuple					    =  pipe.to(__a							)

						pipe.set_progress_bar_config(disable=__a							)

						__a			:       int					    =  pipe(**self.get_dummy_inputs(__a							)							)
						__a			:       Tuple					    =  output.images

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

						__a			:       int					    =  image[0, -3:, -3:, -1]
						__a			:       Union[str, Any]					    =  image_from_tuple[0, -3:, -3:, -1]

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

						__a			:       Optional[Any]					    =  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 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'							)
						__a			:       Optional[int]					    =  init_image.resize((512, 512)							)

						__a			:       Union[str, Any]					    =  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(__a							)							).float() / 255.0
						__a			:       Optional[int]					    =  hint.permute(2						,				0						,				1							).unsqueeze(0							)

						__a			:       Tuple					    =  'A robot, 4k photo'

						__a			:       Optional[Any]					    =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'						,				torch_dtype=torch.floataa							)
						pipe_prior.to(__a							)

						__a			:       Optional[int]					    =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-controlnet-depth'						,				torch_dtype=torch.floataa							)
						__a			:       Optional[int]					    =  pipeline.to(__a							)

						pipeline.set_progress_bar_config(disable=__a							)

						__a			:       Dict					    =  torch.Generator(device='cpu'							).manual_seed(0							)

						__a    ,				__a			:       List[str]					    =  pipe_prior(
						    __a						,				image=__a						,				strength=0.85						,				generator=__a						,				negative_prompt=''						,				).to_tuple()

						__a			:       Tuple					    =  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'						,				)

						__a			:       Dict					    =  output.images[0]

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

						assert_mean_pixel_difference(__a						,				__a							)







'''simple docstring'''



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  inspect.getfile(accelerate.test_utils							)
						__a			:       List[str]					    =  os.path.sep.join(
						    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'external_deps', 'test_metrics.py']							)

						from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

						__a			:       Union[str, Any]					    =  test_metrics




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main						,				num_processes=1							)




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main							)




	@require_single_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.test_metrics.main()





	@require_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(f"""Found {torch.cuda.device_count()} devices."""							)
						__a			:       List[Any]					    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
						with patch_environment(omp_num_threads=1							):
											execute_subprocess_async(__a						,				env=os.environ.copy()							)



