

'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						KandinskyVaaImgaImgPipeline
				lowerCamelCase_							=						['image_embeds', 'negative_image_embeds', 'image']
				lowerCamelCase_							=						[
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				]
				lowerCamelCase_							=						[
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_							=						False







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return 100







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										torch.manual_seed(0      )

										lowercase   :       List[str]								={
										    '''in_channels''': 4,
										    # 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,
										}

										lowercase   :       Optional[int]								=UNetaDConditionModel(**UpperCAmelCase__      )
										return model







				@property
				def    lowerCamelCase_  (					self    :		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    :		str      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       int								=VQModel(**self.dummy_movq_kwargs      )
										return model







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.dummy_unet
										lowercase   :       Any								=self.dummy_movq

										lowercase   :       Dict								={
										    '''num_train_timesteps''': 1000,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_00_85,
										    '''beta_end''': 0.0_12,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowercase   :       Optional[int]								=DDIMScheduler(**UpperCAmelCase__      )

										lowercase   :       int								={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Union[str, Any]=0      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       List[str]								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(seed + 1      )      ).to(
										    UpperCAmelCase__      )
										# create init_image
										lowercase   :       List[str]								=floats_tensor((1, 3, 64, 64)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       int								=image.cpu().permute(0		,				2		,				3		,				1      )[0]
										lowercase   :       Any								=Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert('''RGB'''      ).resize((256, 256)      )

										if str(UpperCAmelCase__      ).startswith('''mps'''      ):
																lowercase   :       Optional[Any]								=torch.manual_seed(UpperCAmelCase__      )
										else:
																lowercase   :       Optional[Any]								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )
										lowercase   :       Tuple								={
										    '''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    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								='''cpu'''

										lowercase   :       int								=self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								=pipe(**self.get_dummy_inputs(UpperCAmelCase__      )      )
										lowercase   :       Optional[int]								=output.images

										lowercase   :       Optional[Any]								=pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__      )		,				return_dict=UpperCAmelCase__		,				)[0]

										lowercase   :       Optional[int]								=image[0, -3:, -3:, -1]
										lowercase   :       Optional[Any]								=image_from_tuple[0, -3:, -3:, -1]

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

										lowercase   :       str								=np.array(
										    [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
										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			):







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

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

										lowercase   :       Any								=KandinskyVaaPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''		,				torch_dtype=torch.floataa      )
										pipe_prior.to(UpperCAmelCase__      )

										lowercase   :       List[str]								=KandinskyVaaImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-decoder'''		,				torch_dtype=torch.floataa      )
										lowercase   :       Optional[int]								=pipeline.to(UpperCAmelCase__      )

										pipeline.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[Any]								=torch.Generator(device='''cpu'''      ).manual_seed(0      )
										lowercase		,  lowercase   :       Any								=pipe_prior(
										    UpperCAmelCase__		,				generator=UpperCAmelCase__		,				num_inference_steps=5		,				negative_prompt=''''''		,				).to_tuple()

										lowercase   :       str								=pipeline(
										    image=UpperCAmelCase__		,				image_embeds=UpperCAmelCase__		,				negative_image_embeds=UpperCAmelCase__		,				generator=UpperCAmelCase__		,				num_inference_steps=100		,				height=768		,				width=768		,				strength=0.2		,				output_type='''np'''		,				)

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

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

										assert_mean_pixel_difference(UpperCAmelCase__		,				UpperCAmelCase__      )








'''simple docstring'''





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Tuple								=HfArgumentParser(__magic_name__	)
						lowercase   :       Union[str, Any]								=parser.parse_args_into_dataclasses()[0]
						lowercase   :       Any								=TensorFlowBenchmark(args=__magic_name__	)
						try:
												lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase   :       List[Any]								='''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
												lowercase   :       Any								=''' '''.join(str(__magic_name__	).split(''' '''	)[:-1]	)
												lowercase   :       Optional[Any]								=''''''
												lowercase   :       List[str]								=eval(str(__magic_name__	).split(''' '''	)[-1]	)
												lowercase   :       Optional[Any]								=[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]	)
																		else:
																								wrong_args.append(__magic_name__	)
												if len(__magic_name__	) > 0:
																		lowercase   :       int								=full_error_msg + begin_error_msg + str(__magic_name__	)
												raise ValueError(__magic_name__	)
						benchmark.run()


if __name__ == "__main__":
							main()





