






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    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				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  KandinskyVaaInpaintPipeline
		_snake_case   :   List[Any]							=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
		_snake_case   :   List[str]							=  [
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		    '''mask_image''',
		]
		_snake_case   :   str							=  [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''latents''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		_snake_case   :   Any							=  False



		@property
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							return 3_2



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							return 3_2



		@property
		def  __UpperCAmelCase      (							self       )     ->							str:
							return self.time_input_dim



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.time_input_dim * 4



		@property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return 1_0_0



		@property
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							torch.manual_seed(0       )

							UpperCAmelCase_   :				Any						=						{
							    'in_channels': 9,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': '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': 'image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							UpperCAmelCase_   :				Optional[int]						=						UNetaDConditionModel(**_UpperCamelCase       )
							return model



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



		@property
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							torch.manual_seed(0       )
							UpperCAmelCase_   :				int						=						VQModel(**self.dummy_movq_kwargs       )
							return model



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						self.dummy_unet
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_movq

							UpperCAmelCase_   :				List[str]						=						DDIMScheduler(
							    num_train_timesteps=1_0_0_0    ,		beta_schedule='linear'    ,		beta_start=0.0_00_85    ,		beta_end=0.0_12    ,		clip_sample=_UpperCamelCase    ,		set_alpha_to_one=_UpperCamelCase    ,		steps_offset=1    ,		prediction_type='epsilon'    ,		thresholding=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Tuple						=						{
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0       )     ->							str:
							UpperCAmelCase_   :				Union[str, Any]						=						floats_tensor((1, self.text_embedder_hidden_size)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							UpperCAmelCase_   :				Union[str, Any]						=						floats_tensor((1, self.text_embedder_hidden_size)    ,		rng=random.Random(seed + 1       )       ).to(
							    _UpperCamelCase       )
							# create init_image
							UpperCAmelCase_   :				Dict						=						floats_tensor((1, 3, 6_4, 6_4)    ,		rng=random.Random(_UpperCamelCase       )       ).to(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						image.cpu().permute(0    ,		2    ,		3    ,		1       )[0]
							UpperCAmelCase_   :				Optional[Any]						=						Image.fromarray(np.uinta(_UpperCamelCase       )       ).convert('RGB'       ).resize((2_5_6, 2_5_6)       )
							# create mask
							UpperCAmelCase_   :				Optional[Any]						=						np.ones((6_4, 6_4)    ,		dtype=np.floataa       )
							UpperCAmelCase_   :				List[Any]						=						0

							if str(_UpperCamelCase       ).startswith('mps'       ):
												UpperCAmelCase_   :				Optional[int]						=						torch.manual_seed(_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				List[Any]						=						torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
							UpperCAmelCase_   :				Tuple						=						{
							    'image': init_image,
							    'mask_image': mask,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 6_4,
							    'width': 6_4,
							    'num_inference_steps': 2,
							    'guidance_scale': 4.0,
							    'output_type': 'np',
							}
							return inputs



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				int						=						'cpu'

							UpperCAmelCase_   :				str						=						self.get_dummy_components()

							UpperCAmelCase_   :				List[Any]						=						self.pipeline_class(**_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						pipe.to(_UpperCamelCase       )

							pipe.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						pipe(**self.get_dummy_inputs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				List[Any]						=						output.images

							UpperCAmelCase_   :				List[Any]						=						pipe(
							    **self.get_dummy_inputs(_UpperCamelCase       )    ,		return_dict=_UpperCamelCase    ,		)[0]

							UpperCAmelCase_   :				Dict						=						image[0, -3:, -3:, -1]
							UpperCAmelCase_   :				Any						=						image_from_tuple[0, -3:, -3:, -1]

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

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

							UpperCAmelCase_   :				Union[str, Any]						=						np.array(
							    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]       )

							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  __UpperCAmelCase      (							self       )     ->							List[Any]:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3       )





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







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				int						=						load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'       )

							UpperCAmelCase_   :				Dict						=						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'       )
							UpperCAmelCase_   :				int						=						np.ones((7_6_8, 7_6_8)    ,		dtype=np.floataa       )
							UpperCAmelCase_   :				List[str]						=						0

							UpperCAmelCase_   :				Optional[Any]						=						'a hat'

							UpperCAmelCase_   :				Optional[Any]						=						KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'    ,		torch_dtype=torch.floataa       )
							pipe_prior.to(_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						KandinskyVaaInpaintPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder-inpaint'    ,		torch_dtype=torch.floataa       )
							UpperCAmelCase_   :				List[Any]						=						pipeline.to(_UpperCamelCase       )
							pipeline.set_progress_bar_config(disable=_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						torch.Generator(device='cpu'       ).manual_seed(0       )
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						pipe_prior(
							    _UpperCamelCase    ,		generator=_UpperCamelCase    ,		num_inference_steps=5    ,		negative_prompt=''    ,		).to_tuple()

							UpperCAmelCase_   :				Dict						=						pipeline(
							    image=_UpperCamelCase    ,		mask_image=_UpperCamelCase    ,		image_embeds=_UpperCamelCase    ,		negative_image_embeds=_UpperCamelCase    ,		generator=_UpperCamelCase    ,		num_inference_steps=1_0_0    ,		height=7_6_8    ,		width=7_6_8    ,		output_type='np'    ,		)

							UpperCAmelCase_   :				Any						=						output.images[0]

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(_UpperCamelCase    ,		_UpperCamelCase       )














def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[str]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str					,	__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					if index == r:
										for j in range(__snake_case  ):
															print(data[j]					,	end=' '  )
										print(' '  )
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					UpperCAmelCase_   :				int						=						arr[i]
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	index + 1					,	__snake_case					,	i + 1  )
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	i + 1  )
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()




def 					lowercase__				(					__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	0					,	__snake_case					,	0  )


if __name__ == "__main__":
		# Driver code to check the function above
		__UpperCAmelCase     							=						[10, 20, 30, 40, 50]
		print_combination(arr, len(arr), 3)
		# This code is contributed by Ambuj sahu






