

'''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 		__SCREAMING_SNAKE_CASE							(			_lowercase						,							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  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										return 100







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										torch.manual_seed(0   )

										lowercase :							List[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,
										}

										lowercase :							Any    		=UNetaDConditionModel(**__A   )
										return model







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							Union[str, Any]    		=VQModel(**self.dummy_movq_kwargs   )
										return model







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Any    		=self.dummy_unet
										lowercase :							Any    		=self.dummy_movq

										lowercase :							Optional[Any]    		={
										    """num_train_timesteps""": 1000,
										    """beta_schedule""": """linear""",
										    """beta_start""": 0.0_00_85,
										    """beta_end""": 0.0_12,
										    """clip_sample""": False,
										    """set_alpha_to_one""": False,
										    """steps_offset""": 0,
										    """prediction_type""": """epsilon""",
										    """thresholding""": False,
										}

										lowercase :							Tuple    		=DDIMScheduler(**__A   )

										lowercase :							str    		={
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components







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

										if str(__A   ).startswith('''mps'''   ):
																lowercase :							str    		=torch.manual_seed(__A   )
										else:
																lowercase :							List[Any]    		=torch.Generator(device=__A   ).manual_seed(__A   )
										lowercase :							Optional[int]    		={
										    """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  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Any    		="""cpu"""

										lowercase :							str    		=self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=__A   )

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

										lowercase :							int    		=pipe(
										    **self.get_dummy_inputs(__A   )		,					return_dict=__A		,					)[0]

										lowercase :							Optional[int]    		=image[0, -3:, -3:, -1]
										lowercase :							str    		=image_from_tuple[0, -3:, -3:, -1]

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

										lowercase :							str    		=np.array(
										    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]   )
										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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''   )

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

										lowercase :							Union[str, Any]    		=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''   )
										lowercase :							Optional[int]    		=torch.from_numpy(np.array(__A   )   ).float() / 255.0
										lowercase :							int    		=hint.permute(2		,					0		,					1   ).unsqueeze(0   )

										lowercase :							List[str]    		="""A robot, 4k photo"""

										lowercase :							int    		=KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''		,					torch_dtype=torch.floataa   )
										pipe_prior.to(__A   )

										lowercase :							List[Any]    		=KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''		,					torch_dtype=torch.floataa   )
										lowercase :							str    		=pipeline.to(__A   )

										pipeline.set_progress_bar_config(disable=__A   )

										lowercase :							Optional[Any]    		=torch.Generator(device='''cpu'''   ).manual_seed(0   )

										lowercase :							Tuple    		=pipe_prior(
										    __A		,					image=__A		,					strength=0.85		,					generator=__A		,					negative_prompt=''''''		,					).to_tuple()

										lowercase :							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'''		,					)

										lowercase :							int    		=output.images[0]

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

										assert_mean_pixel_difference(__A		,					__A   )








from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





