




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				@property
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Optional[Any]:
							a    =			1
							a    =			3
							a    =			(32, 32)

							a    =			floats_tensor((batch_size, num_channels) + sizes					,      rng=random.Random(0							)							).to(__lowerCamelCase							)
							return image





				@property
				def 							__UpperCAmelCase							(      self				:     int							)					->     Optional[Any]:
							torch.manual_seed(0							)
							a    =			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				:     Optional[int]							)					->     Optional[Any]:
							torch.manual_seed(0							)
							a    =			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				:     Optional[int]							)					->     Optional[int]:
							torch.manual_seed(0							)
							a    =			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=10_00					,      )
							return CLIPTextModel(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Union[str, Any]:
							def extract(*__lowerCamelCase				:     int					,      **__lowerCamelCase				:     List[str]							):




										class     snake_case__							:
														"""simple docstring"""



														def __init__(      self				:     List[Any]							)					->     Tuple:
																	a    =			torch.ones([0]							)





														def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Any							)					->     str:
																	self.pixel_values.to(__lowerCamelCase							)
																	return self

										return Out()

							return extract





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a    =			self.dummy_cond_unet
							a    =			DDIMScheduler(
							    beta_start=0.00_085					,      beta_end=0.012					,      beta_schedule="scaled_linear"					,      clip_sample=__lowerCamelCase					,      set_alpha_to_one=__lowerCamelCase					,      )

							a    =			self.dummy_vae
							a    =			self.dummy_text_encoder
							a    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

							# make sure here that pndm scheduler skips prk
							a    =			StableDiffusionPipeline(
							    unet=__lowerCamelCase					,      scheduler=__lowerCamelCase					,      vae=__lowerCamelCase					,      text_encoder=__lowerCamelCase					,      tokenizer=__lowerCamelCase					,      safety_checker=__lowerCamelCase					,      feature_extractor=self.dummy_extractor					,      )
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			'''A painting of a squirrel eating a burger'''

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

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

							a    =			image[0, -3:, -3:, -1]
							a    =			image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							a    =			np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]							)

							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				:     int							)					->     Tuple:
							a    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a    =			self.dummy_cond_unet
							a    =			PNDMScheduler(skip_prk_steps=__lowerCamelCase							)
							a    =			self.dummy_vae
							a    =			self.dummy_text_encoder
							a    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

							# make sure here that pndm scheduler skips prk
							a    =			StableDiffusionPipeline(
							    unet=__lowerCamelCase					,      scheduler=__lowerCamelCase					,      vae=__lowerCamelCase					,      text_encoder=__lowerCamelCase					,      tokenizer=__lowerCamelCase					,      safety_checker=__lowerCamelCase					,      feature_extractor=self.dummy_extractor					,      )
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			'''A painting of a squirrel eating a burger'''
							a    =			torch.Generator(device=__lowerCamelCase							).manual_seed(0							)
							a    =			sd_pipe([prompt]					,      generator=__lowerCamelCase					,      guidance_scale=6.0					,      num_inference_steps=2					,      output_type="np"							)

							a    =			output.images

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

							a    =			image[0, -3:, -3:, -1]
							a    =			image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							a    =			np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]							)

							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				:     Tuple							)					->     Any:
							a    =			StableDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"					,      safety_checker=__lowerCamelCase							)
							assert isinstance(__lowerCamelCase					,      __lowerCamelCase							)
							assert isinstance(pipe.scheduler					,      __lowerCamelCase							)
							assert pipe.safety_checker is None

							a    =			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(__lowerCamelCase							)
										a    =			StableDiffusionPipeline.from_pretrained(__lowerCamelCase							)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							a    =			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				:     Tuple							)					->     Any:
							a    =			self.dummy_cond_unet
							a    =			PNDMScheduler(skip_prk_steps=__lowerCamelCase							)
							a    =			self.dummy_vae
							a    =			self.dummy_text_encoder
							a    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

							# put models in fp16
							a    =			unet.half()
							a    =			vae.half()
							a    =			bert.half()

							# make sure here that pndm scheduler skips prk
							a    =			StableDiffusionPipeline(
							    unet=__lowerCamelCase					,      scheduler=__lowerCamelCase					,      vae=__lowerCamelCase					,      text_encoder=__lowerCamelCase					,      tokenizer=__lowerCamelCase					,      safety_checker=__lowerCamelCase					,      feature_extractor=self.dummy_extractor					,      )
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			'''A painting of a squirrel eating a burger'''
							a    =			sd_pipe([prompt]					,      num_inference_steps=2					,      output_type="np"							).images

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




@nightly
@require_torch_gpu
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[Any]:
							a    =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,      safety_checker=__lowerCamelCase							)
							a    =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			(
							    '''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 '''
							)
							a    =			40_03_66_03_46
							a    =			7

							# without safety guidance (sld_guidance_scale = 0)
							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=0					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

							assert image.shape == (1, 5_12, 5_12, 3)

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

							# without safety guidance (strong configuration)
							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=20_00					,      sld_warmup_steps=7					,      sld_threshold=0.025					,      sld_momentum_scale=0.5					,      sld_mom_beta=0.7					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

							assert image.shape == (1, 5_12, 5_12, 3)

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





				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[str]:
							a    =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,      safety_checker=__lowerCamelCase							)
							a    =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			'''padme amidala taking a bath artwork, safe for work, no nudity'''
							a    =			27_34_97_17_55
							a    =			7

							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=0					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

							assert image.shape == (1, 5_12, 5_12, 3)

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

							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=20_00					,      sld_warmup_steps=7					,      sld_threshold=0.025					,      sld_momentum_scale=0.5					,      sld_mom_beta=0.7					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

							assert image.shape == (1, 5_12, 5_12, 3)

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





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							)
							a    =			sd_pipe.to(__lowerCamelCase							)
							sd_pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			(
							    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
							    ''' leyendecker'''
							)
							a    =			10_44_35_52_34
							a    =			12

							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=0					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]							)

							assert image.shape == (1, 5_12, 5_12, 3)

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

							a    =			torch.manual_seed(__lowerCamelCase							)
							a    =			sd_pipe(
							    [prompt]					,      generator=__lowerCamelCase					,      guidance_scale=__lowerCamelCase					,      num_inference_steps=50					,      output_type="np"					,      width=5_12					,      height=5_12					,      sld_guidance_scale=20_00					,      sld_warmup_steps=7					,      sld_threshold=0.025					,      sld_momentum_scale=0.5					,      sld_mom_beta=0.7					,      )

							a    =			output.images
							a    =			image[0, -3:, -3:, -1]
							a    =			np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]							)
							assert image.shape == (1, 5_12, 5_12, 3)

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














"""simple docstring"""



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		:       str  				=		parser.parse_args()

						__A		:       List[Any]  				=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

						__A		:       List[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)



