

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							int:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							1
				SCREAMING_SNAKE_CASE :   str					  =							3
				SCREAMING_SNAKE_CASE :   int					  =							(32, 32)

				SCREAMING_SNAKE_CASE :   str					  =							floats_tensor((batch_size, num_channels) + sizes   ,   rng=random.Random(0						)						).to(a						)
				return image
			@property
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							str:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   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 				__UpperCamelCase			(      self				:			int						)				->							Any:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							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 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							CLIPTextConfig(
				    bos_token_id=0   ,   eos_token_id=2   ,   hidden_size=32   ,   intermediate_size=37   ,   layer_norm_eps=1e-05   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
				return CLIPTextModel(a						)
			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				def extract(*a				:			int   ,   **a				:			Union[str, Any]						):






					class _UpperCamelCase				:
								'''simple docstring'''







								def __init__(      self				:			Optional[Any]						)				->							str:

									"""simple docstring"""

									SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.ones([0]						)






								def 				__UpperCamelCase			(      self				:			str   ,   a				:			List[Any]						)				->							List[str]:

									"""simple docstring"""

									self.pixel_values.to(a						)
									return self

					return Out()

				return extract
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   int					  =							self.dummy_cond_unet
				SCREAMING_SNAKE_CASE :   Dict					  =							DDIMScheduler(
				    beta_start=0.0_0085   ,   beta_end=0.012   ,   beta_schedule="scaled_linear"   ,   clip_sample=a   ,   set_alpha_to_one=a   ,   )

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.dummy_vae
				SCREAMING_SNAKE_CASE :   Dict					  =							self.dummy_text_encoder
				SCREAMING_SNAKE_CASE :   Dict					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionPipeline(
				    unet=a   ,   scheduler=a   ,   vae=a   ,   text_encoder=a   ,   tokenizer=a   ,   safety_checker=a   ,   feature_extractor=self.dummy_extractor   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"A painting of a squirrel eating a burger"

				SCREAMING_SNAKE_CASE :   Any					  =							torch.Generator(device=a						).manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=6.0   ,   num_inference_steps=2   ,   output_type="np"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.Generator(device=a						).manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=6.0   ,   num_inference_steps=2   ,   output_type="np"   ,   return_dict=a   ,   )[0]

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

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE :   Tuple					  =							np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			Any						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.dummy_cond_unet
				SCREAMING_SNAKE_CASE :   Tuple					  =							PNDMScheduler(skip_prk_steps=a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							self.dummy_vae
				SCREAMING_SNAKE_CASE :   Dict					  =							self.dummy_text_encoder
				SCREAMING_SNAKE_CASE :   Tuple					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE :   List[str]					  =							StableDiffusionPipeline(
				    unet=a   ,   scheduler=a   ,   vae=a   ,   text_encoder=a   ,   tokenizer=a   ,   safety_checker=a   ,   feature_extractor=self.dummy_extractor   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

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

				SCREAMING_SNAKE_CASE :   int					  =							output.images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.Generator(device=a						).manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=6.0   ,   num_inference_steps=2   ,   output_type="np"   ,   return_dict=a   ,   )[0]

				SCREAMING_SNAKE_CASE :   str					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   str					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			Dict						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionPipeline.from_pretrained(
				    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"   ,   safety_checker=a						)
				assert isinstance(a   ,   a						)
				assert isinstance(pipe.scheduler   ,   a						)
				assert pipe.safety_checker is None

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipe("example prompt"   ,   num_inference_steps=2						).images[0]
				assert image is not None

				# check that there's no error when saving a pipeline with one of the models being None
				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionPipeline.from_pretrained(a						)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				SCREAMING_SNAKE_CASE :   Dict					  =							pipe("example prompt"   ,   num_inference_steps=2						).images[0]
				assert image is not None






			@unittest.skipIf(torch_device != "cuda"   ,   "This test requires a GPU"						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.dummy_cond_unet
				SCREAMING_SNAKE_CASE :   Dict					  =							PNDMScheduler(skip_prk_steps=a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							self.dummy_vae
				SCREAMING_SNAKE_CASE :   Dict					  =							self.dummy_text_encoder
				SCREAMING_SNAKE_CASE :   Any					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				# put models in fp16
				SCREAMING_SNAKE_CASE :   Tuple					  =							unet.half()
				SCREAMING_SNAKE_CASE :   Tuple					  =							vae.half()
				SCREAMING_SNAKE_CASE :   Tuple					  =							bert.half()

				# make sure here that pndm scheduler skips prk
				SCREAMING_SNAKE_CASE :   str					  =							StableDiffusionPipeline(
				    unet=a   ,   scheduler=a   ,   vae=a   ,   text_encoder=a   ,   tokenizer=a   ,   safety_checker=a   ,   feature_extractor=self.dummy_extractor   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe([prompt]   ,   num_inference_steps=2   ,   output_type="np"						).images

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






@nightly
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[int]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   ,   safety_checker=a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							(
				    "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
				    " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
				    " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
				    " children from bahnhof zoo, detailed "
				)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							40_0366_0346
				SCREAMING_SNAKE_CASE :   Tuple					  =							7

				# without safety guidance (sld_guidance_scale = 0)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=0   ,   )

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   List[str]					  =							[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

				assert image.shape == (1, 512, 512, 3)

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

				# without safety guidance (strong configuration)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=2000   ,   sld_warmup_steps=7   ,   sld_threshold=0.025   ,   sld_momentum_scale=0.5   ,   sld_mom_beta=0.7   ,   )

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			str						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   ,   safety_checker=a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
				SCREAMING_SNAKE_CASE :   Any					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"padme amidala taking a bath artwork, safe for work, no nudity"
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							27_3497_1755
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							7

				SCREAMING_SNAKE_CASE :   str					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=0   ,   )

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   str					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

				assert image.shape == (1, 512, 512, 3)

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

				SCREAMING_SNAKE_CASE :   int					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=2000   ,   sld_warmup_steps=7   ,   sld_threshold=0.025   ,   sld_momentum_scale=0.5   ,   sld_mom_beta=0.7   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							output.images
				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

				assert image.shape == (1, 512, 512, 3)

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






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(
				    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
				    " leyendecker"
				)
				SCREAMING_SNAKE_CASE :   str					  =							10_4435_5234
				SCREAMING_SNAKE_CASE :   List[str]					  =							12

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=0   ,   )

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images
				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]						)

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-7

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.manual_seed(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=a   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   sld_guidance_scale=2000   ,   sld_warmup_steps=7   ,   sld_threshold=0.025   ,   sld_momentum_scale=0.5   ,   sld_mom_beta=0.7   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Tuple					  =							np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]						)
				assert image.shape == (1, 512, 512, 3)

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


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			a_      =     CLIPImageProcessor()
			a_      =     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

			a_      =     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)