





'''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 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Union[str, Any]											=       KandinskyVaaControlnetImgaImgPipeline
	a    :				Optional[Any]											=       ["image_embeds", "negative_image_embeds", "image", "hint"]
	a    :				Optional[Any]											=       ["image_embeds", "negative_image_embeds", "image", "hint"]
	a    :				List[Any]											=       [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a    :				Optional[int]											=       False


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				return self.time_input_dim


	@property
	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				return self.time_input_dim * 4


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				return 100


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0 )

				__lowercase										=							{
				    '''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,
				}

				__lowercase										=							UNetaDConditionModel(**_lowerCamelCase )
				return model


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''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 )							->							Optional[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.dummy_unet
				__lowercase										=							self.dummy_movq

				__lowercase										=							{
				    '''num_train_timesteps''': 1000,
				    '''beta_schedule''': '''linear''',
				    '''beta_start''': 0.0_0_0_8_5,
				    '''beta_end''': 0.0_1_2,
				    '''clip_sample''': False,
				    '''set_alpha_to_one''': False,
				    '''steps_offset''': 0,
				    '''prediction_type''': '''epsilon''',
				    '''thresholding''': False,
				}

				__lowercase										=							DDIMScheduler(**_lowerCamelCase )

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

				return components


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=0 )							->							Any:







				'''simple docstring'''





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

				if str(_lowerCamelCase ).startswith('''mps''' ):
							__lowercase										=							torch.manual_seed(_lowerCamelCase )
				else:
							__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
				__lowercase										=							{
				    '''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 )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.get_dummy_components()

				__lowercase										=							self.pipeline_class(**_lowerCamelCase )
				__lowercase										=							pipe.to(_lowerCamelCase )

				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
				__lowercase										=							output.images

				__lowercase										=							pipe(
				    **self.get_dummy_inputs(_lowerCamelCase )     ,return_dict=_lowerCamelCase     ,)[0]

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

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

				__lowercase										=							np.array(
				    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
				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 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )

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

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

				__lowercase										=							'''A robot, 4k photo'''

				__lowercase										=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-prior'''     ,torch_dtype=torch.floataa )
				pipe_prior.to(_lowerCamelCase )

				__lowercase										=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''     ,torch_dtype=torch.floataa )
				__lowercase										=							pipeline.to(_lowerCamelCase )

				pipeline.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							torch.Generator(device='''cpu''' ).manual_seed(0 )

				__lowercase						,		__lowercase										=							pipe_prior(
				    _lowerCamelCase     ,image=_lowerCamelCase     ,strength=0.8_5     ,generator=_lowerCamelCase     ,negative_prompt=''''''     ,).to_tuple()

				__lowercase										=							pipeline(
				    image=_lowerCamelCase     ,image_embeds=_lowerCamelCase     ,negative_image_embeds=_lowerCamelCase     ,hint=_lowerCamelCase     ,generator=_lowerCamelCase     ,num_inference_steps=100     ,height=512     ,width=512     ,strength=0.5     ,output_type='''np'''     ,)

				__lowercase										=							output.images[0]

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

				assert_mean_pixel_difference(_lowerCamelCase     ,_lowerCamelCase )










'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = 32     ,_lowerCamelCase=PILImageResampling.BILINEAR     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							size_divisor
				__lowercase										=							resample
				super().__init__(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							get_image_size(_lowerCamelCase )
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase										=							height // size_divisor * size_divisor
				__lowercase										=							width // size_divisor * size_divisor
				__lowercase										=							resize(_lowerCamelCase     ,(new_h, new_w)     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )
				return image


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				return rescale(image=_lowerCamelCase     ,scale=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase										=							resample if resample is not None else self.resample

				if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing''' )

				__lowercase										=							make_list_of_images(_lowerCamelCase )

				if not valid_images(_lowerCamelCase ):
							raise ValueError('''Invalid image(s)''' )

				# All transformations expect numpy arrays.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for img in images]

				if do_resize:
							__lowercase										=							[self.resize(_lowerCamelCase     ,size_divisor=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(_lowerCamelCase     ,scale=1 / 255 ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )





