


'''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__(     lowercase__		,       unittest.TestCase ):
				__magic_name__       :   str							= KandinskyVaaControlnetImgaImgPipeline
				__magic_name__       :   int							= ["image_embeds", "negative_image_embeds", "image", "hint"]
				__magic_name__       :   List[Any]							= ["image_embeds", "negative_image_embeds", "image", "hint"]
				__magic_name__       :   Optional[Any]							= [
				    "generator",
				    "height",
				    "width",
				    "strength",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				__magic_name__       :   str							= False




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

										"""simple docstring"""




										return 32




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

										"""simple docstring"""




										return 32




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

										"""simple docstring"""




										return self.time_input_dim




				@property
				def   a__(	self    :				str   )->  Tuple:

										"""simple docstring"""




										return self.time_input_dim * 4




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

										"""simple docstring"""




										return 100




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

										"""simple docstring"""




										torch.manual_seed(0   )

										UpperCAmelCase         =			{
										    '''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,
										}

										UpperCAmelCase         =			UNetaDConditionModel(**UpperCAmelCase__   )
										return model




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

										"""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   a__(	self    :				Tuple   )->  Any:

										"""simple docstring"""




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




				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.dummy_unet
										UpperCAmelCase         =			self.dummy_movq

										UpperCAmelCase         =			{
										    '''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,
										}

										UpperCAmelCase         =			DDIMScheduler(**UpperCAmelCase__   )

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

										return components




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Any=0   )->  Dict:

										"""simple docstring"""




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

										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																UpperCAmelCase         =			torch.manual_seed(UpperCAmelCase__   )
										else:
																UpperCAmelCase         =			torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										UpperCAmelCase         =			{
										    '''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   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			'''cpu'''

										UpperCAmelCase         =			self.get_dummy_components()

										UpperCAmelCase         =			self.pipeline_class(**UpperCAmelCase__   )
										UpperCAmelCase         =			pipe.to(UpperCAmelCase__   )

										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										UpperCAmelCase         =			pipe(**self.get_dummy_inputs(UpperCAmelCase__   )   )
										UpperCAmelCase         =			output.images

										UpperCAmelCase         =			pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__   )   ,					return_dict=UpperCAmelCase__   ,					)[0]

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

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

										UpperCAmelCase         =			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   a__(	self    :				List[str]   )->  Union[str, Any]:

										"""simple docstring"""




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





				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




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

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

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

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

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

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

										pipeline.set_progress_bar_config(disable=UpperCAmelCase__   )

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

										UpperCAmelCase         =			pipe_prior(
										    UpperCAmelCase__   ,					image=UpperCAmelCase__   ,					strength=0.85   ,					generator=UpperCAmelCase__   ,					negative_prompt=''''''   ,					).to_tuple()

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

										UpperCAmelCase         =			output.images[0]

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

										assert_mean_pixel_difference(UpperCAmelCase__   ,					UpperCAmelCase__   )





'''simple docstring'''



def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						assert column_title.isupper()
						UpperCAmelCase         =			0
						UpperCAmelCase         =			len(A  ) - 1
						UpperCAmelCase         =			0

						while index >= 0:
												UpperCAmelCase         =			(ord(column_title[index]  ) - 64) * pow(26   ,     A  )
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





