


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        a					(			unittest.TestCase   ):





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





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				List[Any]     =	1
        __lowerCamelCase:				int     =	3
        __lowerCamelCase:				Union[str, Any]     =	(32, 32)

        __lowerCamelCase:				Optional[int]     =	floats_tensor((batch_size, num_channels) + sizes    ,	rng=random.Random(0							)							).to(SCREAMING_SNAKE_CASE_							)
        return image





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[int]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Union[str, Any]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        torch.manual_seed(0							)
        __lowerCamelCase:				str     =	RobertaSeriesConfig(
            hidden_size=32    ,	project_dim=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=5006    ,	)
        return RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def extract(*SCREAMING_SNAKE_CASE_					:				List[str]    ,	**SCREAMING_SNAKE_CASE_					:				int							):



           class        a					:





                def __init__(    self					:				List[Any]							):
                   __lowerCamelCase:				Any     =	torch.ones([0]							)







                def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
                   self.pixel_values.to(SCREAMING_SNAKE_CASE_							)
                   return self



           return Out()

        return extract





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

        __lowerCamelCase:				Optional[Any]     =	self.dummy_image.to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        __lowerCamelCase:				Tuple     =	AltDiffusionImgaImgPipeline(
            unet=SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	vae=SCREAMING_SNAKE_CASE_    ,	text_encoder=SCREAMING_SNAKE_CASE_    ,	tokenizer=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	feature_extractor=self.dummy_extractor    ,	)
        __lowerCamelCase:				Tuple     =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,	do_normalize=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	alt_pipe.to(SCREAMING_SNAKE_CASE_							)
        alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

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

        __lowerCamelCase:				Dict     =	output.images

        __lowerCamelCase:				Dict     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(0							)
        __lowerCamelCase:				Optional[int]     =	alt_pipe(
            [prompt]    ,	generator=SCREAMING_SNAKE_CASE_    ,	guidance_scale=6.0    ,	num_inference_steps=2    ,	output_type="""np"""    ,	image=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_    ,	)[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([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]							)

        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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	self.dummy_cond_unet
        __lowerCamelCase:				Tuple     =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	self.dummy_vae
        __lowerCamelCase:				Dict     =	self.dummy_text_encoder
        __lowerCamelCase:				Optional[Any]     =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
        __lowerCamelCase:				Union[str, Any]     =	77

        __lowerCamelCase:				Optional[Any]     =	self.dummy_image.to(SCREAMING_SNAKE_CASE_							)

        # put models in fp16
        __lowerCamelCase:				Tuple     =	unet.half()
        __lowerCamelCase:				List[str]     =	vae.half()
        __lowerCamelCase:				Any     =	bert.half()

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

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

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







     @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Optional[int]     =	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:				Dict     =	"""BAAI/AltDiffusion"""
        __lowerCamelCase:				Union[str, Any]     =	AltDiffusionImgaImgPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Any     =	"""A fantasy landscape, trending on artstation"""

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

        __lowerCamelCase:				Dict     =	image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        __lowerCamelCase:				List[str]     =	np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]							)

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



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





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







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

        __lowerCamelCase:				List[str]     =	"""BAAI/AltDiffusion"""
        __lowerCamelCase:				Any     =	AltDiffusionImgaImgPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

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

        __lowerCamelCase:				Dict     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	strength=0.75    ,	guidance_scale=7.5    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	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






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       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		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

   _A		:				str									=       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)


