



"""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 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



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

					@property
					def 			snake_case__      (    self   ):
									return 3_2

					@property
					def 			snake_case__      (    self   ):
									return 3_2

					@property
					def 			snake_case__      (    self   ):
									return self.time_input_dim

					@property
					def 			snake_case__      (    self   ):
									return self.time_input_dim * 4

					@property
					def 			snake_case__      (    self   ):
									return 1_0_0

					@property
					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )

									_lowerCamelCase        =				{
									    '''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        =				UNetaDConditionModel(**lowerCamelCase__   )
									return model

					@property
					def 			snake_case__      (    self   ):
									return {
									    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}

					@property
					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				VQModel(**self.dummy_movq_kwargs   )
									return model

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.dummy_unet
									_lowerCamelCase        =				self.dummy_movq

									_lowerCamelCase        =				{
									    '''num_train_timesteps''': 1_0_0_0,
									    '''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,
									}

									_lowerCamelCase        =				DDIMScheduler(**lowerCamelCase__   )

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

									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				floats_tensor((1, self.text_embedder_hidden_size)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				floats_tensor((1, self.text_embedder_hidden_size)				,       rng=random.Random(seed + 1   )   ).to(
									    lowerCamelCase__   )
									# create init_image
									_lowerCamelCase        =				floats_tensor((1, 3, 6_4, 6_4)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				image.cpu().permute(0				,       2				,       3				,       1   )[0]
									_lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((2_5_6, 2_5_6)   )
									# create hint
									_lowerCamelCase        =				floats_tensor((1, 3, 6_4, 6_4)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )

									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''image''': init_image,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''hint''': hint,
									    '''generator''': generator,
									    '''height''': 6_4,
									    '''width''': 6_4,
									    '''num_inference_steps''': 1_0,
									    '''guidance_scale''': 7.0,
									    '''strength''': 0.2,
									    '''output_type''': '''np''',
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''

									_lowerCamelCase        =				self.get_dummy_components()

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

									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

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

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

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

									assert image.shape == (1, 6_4, 6_4, 3)

									_lowerCamelCase        =				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 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





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

									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
									_lowerCamelCase        =				init_image.resize((5_1_2, 5_1_2)   )

									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/hint_image_cat.png'''   )
									_lowerCamelCase        =				torch.from_numpy(np.array(lowerCamelCase__   )   ).float() / 2_5_5.0
									_lowerCamelCase        =				hint.permute(2				,       0				,       1   ).unsqueeze(0   )

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

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

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

									pipeline.set_progress_bar_config(disable=lowerCamelCase__   )

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

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

									_lowerCamelCase        =				pipeline(
									    image=lowerCamelCase__				,       image_embeds=lowerCamelCase__				,       negative_image_embeds=lowerCamelCase__				,       hint=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_inference_steps=1_0_0				,       height=5_1_2				,       width=5_1_2				,       strength=0.5				,       output_type='''np'''				,       )

									_lowerCamelCase        =				output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)

									assert_mean_pixel_difference(lowerCamelCase__				,       lowerCamelCase__   )





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
