



def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				List[Any]:

		if isinstance(lowercase__      ,  lowercase__			):
				raise TypeError("\'float\' object cannot be interpreted as an integer"			)
		if isinstance(lowercase__      ,  lowercase__			):
				raise TypeError("\'str\' object cannot be interpreted as an integer"			)

		if num == 0:
				return "0b0"

		UpperCAmelCase_						       =					False

		if num < 0:
				UpperCAmelCase_						       =					True
				UpperCAmelCase_						       =					-num

		UpperCAmelCase_						       =					[]
		while num > 0:
				binary.insert(0      ,  num % 2			)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(lowercase__			) for e in binary			)

		return "0b" + "".join(str(lowercase__			) for e in binary			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	KandinskyImgaImgPipeline
			__A    	=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
			__A    	=	[
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			]
			__A    	=	[
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			__A    	=	False


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim * 4


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 1_00


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
				return tokenizer


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			MCLIPConfig(
				    numDims=self.cross_attention_dim			,					transformerDimensions=self.text_embedder_hidden_size			,					hidden_size=self.text_embedder_hidden_size			,					intermediate_size=37			,					num_attention_heads=4			,					num_hidden_layers=5			,					vocab_size=10_05			,					)

				snake_case_   		=			MultilingualCLIP(__UpperCamelCase				)
				snake_case_   		=			text_encoder.eval()

				return text_encoder


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)

				snake_case_   		=			{
				    'in_channels': 4,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'text_image',
				    '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': 'text_image_proj',
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': None,
				}

				snake_case_   		=			UNetaDConditionModel(**__UpperCamelCase				)
				return model


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["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",
				    ],
				    "vq_embed_dim": 4,
				}


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.dummy_text_encoder
				snake_case_   		=			self.dummy_tokenizer
				snake_case_   		=			self.dummy_unet
				snake_case_   		=			self.dummy_movq

				snake_case_   		=			{
				    'num_train_timesteps': 10_00,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				snake_case_   		=			DDIMScheduler(**__UpperCamelCase				)

				snake_case_   		=			{
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(seed + 1				)				).to(__UpperCamelCase				)
				# create init_image
				snake_case_   		=			floats_tensor((1, 3, 64, 64)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)

				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'horse',
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    '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				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				snake_case_   		=			pipe.to(__UpperCamelCase				)

				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			pipe(**self.get_dummy_inputs(__UpperCamelCase				)				)
				snake_case_   		=			output.images

				snake_case_   		=			pipe(
				    **self.get_dummy_inputs(__UpperCamelCase				)			,					return_dict=__UpperCamelCase			,					)[0]

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

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

				snake_case_   		=			np.array(
				    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]				)
				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				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinsky/kandinsky_img2img_frog.npy'				)

				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
				snake_case_   		=			'A red cartoon frog, 4k'

				snake_case_   		=			KandinskyPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1-prior'			,					torch_dtype=torch.floataa				)
				pipe_prior.to(__UpperCamelCase				)

				snake_case_   		=			KandinskyImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1'			,					torch_dtype=torch.floataa				)
				snake_case_   		=			pipeline.to(__UpperCamelCase				)

				pipeline.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			torch.Generator(device='cpu'				).manual_seed(0				)
				snake_case_		,							snake_case_   		=			pipe_prior(
				    __UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=5			,					negative_prompt=''			,					).to_tuple()

				snake_case_   		=			pipeline(
				    __UpperCamelCase			,					image=__UpperCamelCase			,					image_embeds=__UpperCamelCase			,					negative_image_embeds=__UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=1_00			,					height=7_68			,					width=7_68			,					strength=0.2			,					output_type='np'			,					)

				snake_case_   		=			output.images[0]

				assert image.shape == (7_68, 7_68, 3)

				assert_mean_pixel_difference(__UpperCamelCase			,					__UpperCamelCase				)
