



import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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     __lowerCAmelCase					(    _snake_case       ,		unittest.TestCase		):
			UpperCamelCase           =  KandinskyInpaintPipeline
			UpperCamelCase           =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
			UpperCamelCase           =  [
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			    '''mask_image''',
			]
			UpperCamelCase           =  [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase           =  False



			@property
			def 					_lowerCamelCase (							self     :						Union[str, Any])     ->					Tuple:



						"""simple docstring"""






						return 32



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



						"""simple docstring"""






						return 32



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



						"""simple docstring"""






						return self.time_input_dim



			@property
			def 					_lowerCamelCase (							self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						return self.time_input_dim * 4



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



						"""simple docstring"""






						return 1_00



			@property
			def 					_lowerCamelCase (							self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






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



			@property
			def 					_lowerCamelCase (							self     :						int)     ->					Dict:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase	 =     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				,	)

						_UpperCAmelCase	 =     MultilingualCLIP(lowerCAmelCase__)
						_UpperCAmelCase	 =     text_encoder.eval()

						return text_encoder



			@property
			def 					_lowerCamelCase (							self     :						Dict)     ->					Optional[int]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase	 =     {
						    'in_channels': 9,
						    # 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,
						}

						_UpperCAmelCase	 =     UNetaDConditionModel(**lowerCAmelCase__)
						return model



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



						"""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     :						Dict)     ->					List[Any]:



						"""simple docstring"""






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



			def 					_lowerCamelCase (							self     :						Tuple)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.dummy_text_encoder
						_UpperCAmelCase	 =     self.dummy_tokenizer
						_UpperCAmelCase	 =     self.dummy_unet
						_UpperCAmelCase	 =     self.dummy_movq

						_UpperCAmelCase	 =     DDIMScheduler(
						    num_train_timesteps=10_00				,	beta_schedule='linear'				,	beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	clip_sample=lowerCAmelCase__				,	set_alpha_to_one=lowerCAmelCase__				,	steps_offset=1				,	prediction_type='epsilon'				,	thresholding=lowerCAmelCase__				,	)

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

						return components



			def 					_lowerCamelCase (							self     :						Union[str, Any]				,	A     :						Any				,	A     :						Tuple=0)     ->					str:



						"""simple docstring"""






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

						if str(lowerCAmelCase__).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(lowerCAmelCase__)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
						_UpperCAmelCase	 =     {
						    'prompt': 'horse',
						    'image': init_image,
						    'mask_image': mask,
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'num_inference_steps': 2,
						    'guidance_scale': 4.0,
						    'output_type': 'np',
						}
						return inputs



			def 					_lowerCamelCase (							self     :						Dict)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'cpu'

						_UpperCAmelCase	 =     self.get_dummy_components()

						_UpperCAmelCase	 =     self.pipeline_class(**lowerCAmelCase__)
						_UpperCAmelCase	 =     pipe.to(lowerCAmelCase__)

						pipe.set_progress_bar_config(disable=lowerCAmelCase__)

						_UpperCAmelCase	 =     pipe(**self.get_dummy_inputs(lowerCAmelCase__))
						_UpperCAmelCase	 =     output.images

						_UpperCAmelCase	 =     pipe(
						    **self.get_dummy_inputs(lowerCAmelCase__)				,	return_dict=lowerCAmelCase__				,	)[0]

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

						print(F"image.shape {image.shape}")

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

						_UpperCAmelCase	 =     np.array(
						    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_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()}"



			def 					_lowerCamelCase (							self     :						str)     ->					Tuple:



						"""simple docstring"""






						super().test_inference_batch_single_identical(expected_max_diff=3E-3)







@slow
@require_torch_gpu
class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (							self     :						str)     ->					str:



						"""simple docstring"""






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



			def 					_lowerCamelCase (							self     :						Tuple)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

						_UpperCAmelCase	 =     load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
						_UpperCAmelCase	 =     np.ones((7_68, 7_68)				,	dtype=np.floataa)
						_UpperCAmelCase	 =     0

						_UpperCAmelCase	 =     'a hat'

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

						_UpperCAmelCase	 =     KandinskyInpaintPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1-inpaint'				,	torch_dtype=torch.floataa)
						_UpperCAmelCase	 =     pipeline.to(lowerCAmelCase__)
						pipeline.set_progress_bar_config(disable=lowerCAmelCase__)

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

						_UpperCAmelCase	 =     pipeline(
						    lowerCAmelCase__				,	image=lowerCAmelCase__				,	mask_image=lowerCAmelCase__				,	image_embeds=lowerCAmelCase__				,	negative_image_embeds=lowerCAmelCase__				,	generator=lowerCAmelCase__				,	num_inference_steps=1_00				,	height=7_68				,	width=7_68				,	output_type='np'				,	)

						_UpperCAmelCase	 =     output.images[0]

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

						assert_mean_pixel_difference(lowerCAmelCase__				,	lowerCAmelCase__)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				int:







			'''simple docstring'''
			while second != 0:
						_UpperCAmelCase	 =     first & second
						first ^= second
						_UpperCAmelCase	 =     c << 1
			return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase__     = int(input("Enter the first number: ").strip())
							UpperCAmelCase__     = int(input("Enter the second number: ").strip())
							print(f"""{add(first, second) = }""")
