






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()

class _lowercase     (    unittest.TestCase		):




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




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     List[Any]	  =	1
														lowerCamelCase__ :     Any	  =	3
														lowerCamelCase__ :     List[str]	  =	(32, 32)

														lowerCamelCase__ :     Tuple	  =	floats_tensor((batch_size, num_channels) + sizes      ,							rng=random.Random(0					)					).to(UpperCamelCase__					)
														return image




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Dict	  =	UNetaDConditionModel(
														    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,							cross_attention_dim=32      ,							)
														return model




							@property
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Dict	  =	AutoencoderKL(
														    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,							latent_channels=4      ,							)
														return model




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	RobertaSeriesConfig(
														    hidden_size=32      ,							project_dim=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=5_006      ,							)
														return RobertaSeriesModelWithTransformation(UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														def extract(*UpperCamelCase__:    List[Any]      ,							**UpperCamelCase__:    int					):

																					class _lowercase     :




																												def __init__(							self:    int					):
																																			lowerCamelCase__ :     int	  =	torch.ones([0]					)




																												def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Union[str, Any]					):
																																			self.pixel_values.to(UpperCamelCase__					)
																																			return self



																					return Out()

														return extract




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Union[str, Any]	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__ :     Optional[int]	  =	self.dummy_cond_unet
														lowerCamelCase__ :     Any	  =	PNDMScheduler(skip_prk_steps=UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	self.dummy_vae
														lowerCamelCase__ :     List[str]	  =	self.dummy_text_encoder
														lowerCamelCase__ :     Optional[int]	  =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
														lowerCamelCase__ :     int	  =	77

														lowerCamelCase__ :     List[str]	  =	self.dummy_image.to(UpperCamelCase__					)
														lowerCamelCase__ :     Union[str, Any]	  =	init_image / 2 + 0.5

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__ :     Any	  =	AltDiffusionImgaImgPipeline(
														    unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							safety_checker=UpperCamelCase__      ,							feature_extractor=self.dummy_extractor      ,							)
														lowerCamelCase__ :     List[Any]	  =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,							do_normalize=UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	alt_pipe.to(UpperCamelCase__					)
														alt_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     List[str]	  =	"""A painting of a squirrel eating a burger"""
														lowerCamelCase__ :     int	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
														lowerCamelCase__ :     Optional[Any]	  =	alt_pipe(
														    [prompt]      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							num_inference_steps=2      ,							output_type="""np"""      ,							image=UpperCamelCase__      ,							)

														lowerCamelCase__ :     Tuple	  =	output.images

														lowerCamelCase__ :     str	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
														lowerCamelCase__ :     Tuple	  =	alt_pipe(
														    [prompt]      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							num_inference_steps=2      ,							output_type="""np"""      ,							image=UpperCamelCase__      ,							return_dict=UpperCamelCase__      ,							)[0]

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

														assert image.shape == (1, 32, 32, 3)
														lowerCamelCase__ :     Optional[Any]	  =	np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]					)

														assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-3
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5e-3




							@unittest.skipIf(torch_device != """cuda"""      ,							"""This test requires a GPU"""					)
							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_cond_unet
														lowerCamelCase__ :     Optional[Any]	  =	PNDMScheduler(skip_prk_steps=UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	self.dummy_vae
														lowerCamelCase__ :     str	  =	self.dummy_text_encoder
														lowerCamelCase__ :     Dict	  =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
														lowerCamelCase__ :     List[str]	  =	77

														lowerCamelCase__ :     Dict	  =	self.dummy_image.to(UpperCamelCase__					)

														# put models in fp16
														lowerCamelCase__ :     int	  =	unet.half()
														lowerCamelCase__ :     Tuple	  =	vae.half()
														lowerCamelCase__ :     Optional[int]	  =	bert.half()

														# make sure here that pndm scheduler skips prk
														lowerCamelCase__ :     str	  =	AltDiffusionImgaImgPipeline(
														    unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							safety_checker=UpperCamelCase__      ,							feature_extractor=self.dummy_extractor      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,							do_normalize=UpperCamelCase__					)
														lowerCamelCase__ :     Optional[int]	  =	alt_pipe.to(UpperCamelCase__					)
														alt_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

														lowerCamelCase__ :     Union[str, Any]	  =	"""A painting of a squirrel eating a burger"""
														lowerCamelCase__ :     Tuple	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     List[str]	  =	alt_pipe(
														    [prompt]      ,							generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""np"""      ,							image=UpperCamelCase__      ,							).images

														assert image.shape == (1, 32, 32, 3)




							@unittest.skipIf(torch_device != """cuda"""      ,							"""This test requires a GPU"""					)
							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Tuple	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/img2img/sketch-mountains-input.jpg"""					)
														# resize to resolution that is divisible by 8 but not 16 or 32
														lowerCamelCase__ :     int	  =	init_image.resize((760, 504)					)

														lowerCamelCase__ :     Optional[Any]	  =	"""BAAI/AltDiffusion"""
														lowerCamelCase__ :     str	  =	AltDiffusionImgaImgPipeline.from_pretrained(
														    UpperCamelCase__      ,							safety_checker=UpperCamelCase__      ,							)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Optional[int]	  =	"""A fantasy landscape, trending on artstation"""

														lowerCamelCase__ :     Dict	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Any	  =	pipe(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							strength=0.75      ,							guidance_scale=7.5      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     Optional[int]	  =	output.images[0]

														lowerCamelCase__ :     List[str]	  =	image[255:258, 383:386, -1]

														assert image.shape == (504, 760, 3)
														lowerCamelCase__ :     Any	  =	np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]					)

														assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



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




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




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     List[Any]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/img2img/sketch-mountains-input.jpg"""					)
														lowerCamelCase__ :     str	  =	init_image.resize((768, 512)					)
														lowerCamelCase__ :     Any	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""					)

														lowerCamelCase__ :     Dict	  =	"""BAAI/AltDiffusion"""
														lowerCamelCase__ :     List[str]	  =	AltDiffusionImgaImgPipeline.from_pretrained(
														    UpperCamelCase__      ,							safety_checker=UpperCamelCase__      ,							)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Optional[Any]	  =	"""A fantasy landscape, trending on artstation"""

														lowerCamelCase__ :     List[Any]	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Tuple	  =	pipe(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							strength=0.75      ,							guidance_scale=7.5      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	output.images[0]

														assert image.shape == (512, 768, 3)
														# img2img is flaky across GPUs even in fp32, so using MAE here
														assert np.abs(expected_image - image					).max() < 1e-2









'''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)

