




'''simple docstring'''




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    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	KandinskyVaaInpaintPipeline
							a__   		=	['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							a__   		=	[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							a__   		=	[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							a__   		=	False

							@property
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										return 32

							@property
							def 							_A	(		self			:Union[str, Any]				)				->  Optional[int]:


										'''simple docstring'''




										return 32

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


										'''simple docstring'''




										return self.time_input_dim

							@property
							def 							_A	(		self			:List[str]				)				->  List[str]:


										'''simple docstring'''




										return self.time_input_dim * 4

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


										'''simple docstring'''




										return 100

							@property
							def 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




										torch.manual_seed(0				)

										snake_case_				:					int   =       {
										    "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,
										}

										snake_case_				:					Any   =       UNetaDConditionModel(**lowerCAmelCase__				)
										return model

							@property
							def 							_A	(		self			:Tuple				)				->  Dict:


										'''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 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




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

							def 							_A	(		self			:List[str]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.dummy_unet
										snake_case_				:					List[Any]   =       self.dummy_movq

										snake_case_				:					Dict   =       DDIMScheduler(
										    num_train_timesteps=1_000       ,							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__       ,							)

										snake_case_				:					Any   =       {
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]=0				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					str   =       floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(seed + 1				)				).to(
										    lowerCAmelCase__				)
										# create init_image
										snake_case_				:					Dict   =       floats_tensor((1, 3, 64, 64)       ,							rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       image.cpu().permute(0       ,							2       ,							3       ,							1				)[0]
										snake_case_				:					Tuple   =       Image.fromarray(np.uinta(lowerCAmelCase__				)				).convert("RGB"				).resize((256, 256)				)
										# create mask
										snake_case_				:					Dict   =       np.ones((64, 64)       ,							dtype=np.floataa				)
										snake_case_				:					Union[str, Any]   =       0

										if str(lowerCAmelCase__				).startswith("mps"				):
													snake_case_				:					Dict   =       torch.manual_seed(lowerCAmelCase__				)
										else:
													snake_case_				:					str   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       {
										    "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 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       "cpu"

										snake_case_				:					Optional[int]   =       self.get_dummy_components()

										snake_case_				:					Optional[int]   =       self.pipeline_class(**lowerCAmelCase__				)
										snake_case_				:					List[str]   =       pipe.to(lowerCAmelCase__				)

										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					Optional[Any]   =       pipe(**self.get_dummy_inputs(lowerCAmelCase__				)				)
										snake_case_				:					Any   =       output.images

										snake_case_				:					Dict   =       pipe(
										    **self.get_dummy_inputs(lowerCAmelCase__				)       ,							return_dict=lowerCAmelCase__       ,							)[0]

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

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

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

										snake_case_				:					List[str]   =       np.array(
										    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]				)

										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 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




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







@slow
@require_torch_gpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




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






							def 							_A	(		self			:Optional[int]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"				)

										snake_case_				:					int   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
										snake_case_				:					int   =       np.ones((768, 768)       ,							dtype=np.floataa				)
										snake_case_				:					Optional[Any]   =       0

										snake_case_				:					Dict   =       "a hat"

										snake_case_				:					Union[str, Any]   =       KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"       ,							torch_dtype=torch.floataa				)
										pipe_prior.to(lowerCAmelCase__				)

										snake_case_				:					Optional[int]   =       KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"       ,							torch_dtype=torch.floataa				)
										snake_case_				:					Union[str, Any]   =       pipeline.to(lowerCAmelCase__				)
										pipeline.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					Any   =       torch.Generator(device="cpu"				).manual_seed(0				)
										snake_case_				:					Union[str, Any]   =       pipe_prior(
										    lowerCAmelCase__       ,							generator=lowerCAmelCase__       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

										snake_case_				:					List[Any]   =       pipeline(
										    image=lowerCAmelCase__       ,							mask_image=lowerCAmelCase__       ,							image_embeds=lowerCAmelCase__       ,							negative_image_embeds=lowerCAmelCase__       ,							generator=lowerCAmelCase__       ,							num_inference_steps=100       ,							height=768       ,							width=768       ,							output_type="np"       ,							)

										snake_case_				:					Optional[int]   =       output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(lowerCAmelCase__       ,							lowerCAmelCase__				)












'''simple docstring'''




from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
			    "path": ["test_1.py", "test_2.py", "unit_test.py"],
			    "content": ["a " * 20, "a " * 30, "b " * 7],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




