

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 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						KandinskyVaaInpaintPipeline
			SCREAMING_SNAKE_CASE							   =						['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
			SCREAMING_SNAKE_CASE							   =						[
			    'image_embeds',
			    'negative_image_embeds',
			    'image',
			    'mask_image',
			]
			SCREAMING_SNAKE_CASE							   =						[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			SCREAMING_SNAKE_CASE							   =						False





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




						'''simple docstring'''
						return 32





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						return 32





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						return self.time_input_dim





			@property
			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						return self.time_input_dim * 4





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						return 100





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




						'''simple docstring'''
						torch.manual_seed(0					)

						__a        ={
						    '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,
						}

						__a        =UNetaDConditionModel(**__snake_case					)
						return model





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




						'''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 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =VQModel(**self.dummy_movq_kwargs					)
						return model





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.dummy_unet
						__a        =self.dummy_movq

						__a        =DDIMScheduler(
						    num_train_timesteps=1000					,  beta_schedule='linear'					,  beta_start=0.0_0085					,  beta_end=0.012					,  clip_sample=__snake_case					,  set_alpha_to_one=__snake_case					,  steps_offset=1					,  prediction_type='epsilon'					,  thresholding=__snake_case					,  )

						__a        ={
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(__snake_case					)					).to(__snake_case					)
						__a        =floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(seed + 1					)					).to(
						    __snake_case					)
						# create init_image
						__a        =floats_tensor((1, 3, 64, 64)					,  rng=random.Random(__snake_case					)					).to(__snake_case					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					)[0]
						__a        =Image.fromarray(np.uinta(__snake_case					)					).convert('RGB'					).resize((256, 256)					)
						# create mask
						__a        =np.ones((64, 64)					,  dtype=np.floataa					)
						__a        =0

						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__a        ={
						    '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 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**__snake_case					)
						__a        =pipe.to(__snake_case					)

						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =pipe(**self.get_dummy_inputs(__snake_case					)					)
						__a        =output.images

						__a        =pipe(
						    **self.get_dummy_inputs(__snake_case					)					,  return_dict=__snake_case					,  )[0]

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

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

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

						__a        =np.array(
						    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]					)

						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 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						super().test_inference_batch_single_identical(expected_max_diff=3e-3					)






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





			def 		__magic_name__    (		self					)   -> str:




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





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




						'''simple docstring'''
						__a        =load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
						__a        =np.ones((768, 768)					,  dtype=np.floataa					)
						__a        =0

						__a        ='a hat'

						__a        =KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'					,  torch_dtype=torch.floataa					)
						pipe_prior.to(__snake_case					)

						__a        =KandinskyVaaInpaintPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder-inpaint'					,  torch_dtype=torch.floataa					)
						__a        =pipeline.to(__snake_case					)
						pipeline.set_progress_bar_config(disable=__snake_case					)

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

						__a        =pipeline(
						    image=__snake_case					,  mask_image=__snake_case					,  image_embeds=__snake_case					,  negative_image_embeds=__snake_case					,  generator=__snake_case					,  num_inference_steps=100					,  height=768					,  width=768					,  output_type='np'					,  )

						__a        =output.images[0]

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

						assert_mean_pixel_difference(__snake_case					,  __snake_case					)









from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase					:			Optional[Any]  				=			Lock()






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  List[str]       ,     _snake_case   :  Tuple       ,     _snake_case   :  List[str]	):




			"""simple docstring"""






			global process_lock

			# we perform n swaps since after n swaps we know we are sorted
			# we *could* stop early if we are sorted already, but it takes as long to
			# find out we are sorted as it does to sort the list with this algorithm
			for i in range(0       ,     10	):
						if (i + position) % 2 == 0 and r_send is not None:
									# send your value to your right neighbor
									process_lock.acquire()
									r_send[1].send(_snake_case	)
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									__a        =rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									__a        =min(_snake_case       ,     _snake_case	)
						elif (i + position) % 2 != 0 and l_send is not None:
									# send your value to your left neighbor
									process_lock.acquire()
									l_send[1].send(_snake_case	)
									process_lock.release()

									# receive your left neighbor's value
									process_lock.acquire()
									__a        =lr_cv[0].recv()
									process_lock.release()

									# take the higher value since you are on the right
									__a        =max(_snake_case       ,     _snake_case	)
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(_snake_case	)






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			__a        =[]
			__a        =[]
			# initialize the list of pipes where the values will be retrieved
			for _ in arr:
						result_pipe.append(Pipe()	)
			# creates the processes
			# the first and last process only have one neighbor so they are made outside
			# of the loop
			__a        =Pipe()
			__a        =Pipe()
			process_array_.append(
			    Process(
			        target=_snake_case       ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])       ,     )	)
			__a        =temp_rs
			__a        =temp_rr

			for i in range(1       ,     len(_snake_case	) - 1	):
						__a        =Pipe()
						__a        =Pipe()
						process_array_.append(
						    Process(
						        target=_snake_case       ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])       ,     )	)
						__a        =temp_rs
						__a        =temp_rr

			process_array_.append(
			    Process(
			        target=_snake_case       ,     args=(
			            len(_snake_case	) - 1,
			            arr[len(_snake_case	) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(_snake_case	) - 1],
			        )       ,     )	)

			# start the processes
			for p in process_array_:
						p.start()

			# wait for the processes to end and write their values to the list
			for p in range(0       ,     len(_snake_case	)	):
						__a        =result_pipe[p][0].recv()
						process_array_[p].join()
			return arr






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =list(range(10       ,     0       ,     -1	)	)
			print('Initial List'	)
			print(*_snake_case	)
			__a        =odd_even_transposition(_snake_case	)
			print('Sorted List\n'	)
			print(*_snake_case	)


if __name__ == "__main__":
						main()






