






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 _lowercase     (    _lowercase    ,				unittest.TestCase		):
							a    				=	KandinskyVaaPipeline
							a    				=	[
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							a    				=	["""image_embeds""", """negative_image_embeds"""]
							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 		lowerCamelCase_    (							self:    Tuple					):
														return 32




							@property
							def 		lowerCamelCase_    (							self:    Tuple					):
														return 32




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return self.time_input_dim




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														return self.time_input_dim * 4




							@property
							def 		lowerCamelCase_    (							self:    int					):
														return 100




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														torch.manual_seed(0					)

														lowerCamelCase__ :     Dict	  =	{
														    """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,
														}

														lowerCamelCase__ :     Optional[Any]	  =	UNetaDConditionModel(**UpperCamelCase__					)
														return model




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														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:    Optional[Any]					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	VQModel(**self.dummy_movq_kwargs					)
														return model




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	self.dummy_unet
														lowerCamelCase__ :     str	  =	self.dummy_movq

														lowerCamelCase__ :     int	  =	DDIMScheduler(
														    num_train_timesteps=1_000      ,							beta_schedule="""linear"""      ,							beta_start=0.00_085      ,							beta_end=0.012      ,							clip_sample=UpperCamelCase__      ,							set_alpha_to_one=UpperCamelCase__      ,							steps_offset=1      ,							prediction_type="""epsilon"""      ,							thresholding=UpperCamelCase__      ,							)

														lowerCamelCase__ :     Optional[int]	  =	{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    str=0					):
														lowerCamelCase__ :     Union[str, Any]	  =	floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(UpperCamelCase__					)					).to(UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(seed + 1					)					).to(
														    UpperCamelCase__					)
														if str(UpperCamelCase__					).startswith("""mps"""					):
																					lowerCamelCase__ :     Union[str, Any]	  =	torch.manual_seed(UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     List[Any]	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	{
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """guidance_scale""": 4.0,
														    """num_inference_steps""": 2,
														    """output_type""": """np""",
														}
														return inputs




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Optional[Any]	  =	"""cpu"""

														lowerCamelCase__ :     Optional[Any]	  =	self.get_dummy_components()

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

														pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	pipe(**self.get_dummy_inputs(UpperCamelCase__					)					)
														lowerCamelCase__ :     Dict	  =	output.images

														lowerCamelCase__ :     Any	  =	pipe(
														    **self.get_dummy_inputs(UpperCamelCase__					)      ,							return_dict=UpperCamelCase__      ,							)[0]

														lowerCamelCase__ :     Tuple	  =	image[0, -3:, -3:, -1]
														lowerCamelCase__ :     Optional[Any]	  =	image_from_tuple[0, -3:, -3:, -1]

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

														lowerCamelCase__ :     Union[str, Any]	  =	np.array(
														    [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]					)

														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 _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    Tuple					):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Any	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""					)

														lowerCamelCase__ :     Optional[Any]	  =	KandinskyVaaPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-prior"""      ,							torch_dtype=torch.floataa					)
														pipe_prior.to(UpperCamelCase__					)

														lowerCamelCase__ :     Optional[int]	  =	KandinskyVaaPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-decoder"""      ,							torch_dtype=torch.floataa					)
														lowerCamelCase__ :     str	  =	pipeline.to(UpperCamelCase__					)
														pipeline.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	"""red cat, 4k photo"""

														lowerCamelCase__ :     Optional[int]	  =	torch.Generator(device="""cuda"""					).manual_seed(0					)
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	pipe_prior(
														    UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=5      ,							negative_prompt=""""""      ,							).to_tuple()

														lowerCamelCase__ :     List[Any]	  =	torch.Generator(device="""cuda"""					).manual_seed(0					)
														lowerCamelCase__ :     List[Any]	  =	pipeline(
														    image_embeds=UpperCamelCase__      ,							negative_image_embeds=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=100      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     Dict	  =	output.images[0]

														assert image.shape == (512, 512, 3)

														assert_mean_pixel_difference(UpperCamelCase__      ,							UpperCamelCase__					)









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=argparse.ArgumentParser()

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			parser.add_argument(
			    '''--txt2img_unclip''',
			    default='''kakaobrain/karlo-v1-alpha''',
			    type=str,
			    required=False,
			    help='''The pretrained txt2img unclip.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)

