




'''simple docstring'''


import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class 					lowercase						(  __UpperCAmelCase,					__UpperCAmelCase,					unittest.TestCase ):
							_lowerCamelCase		:      List[Any]=   IFPipeline
							_lowerCamelCase		:      List[Any]=   TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
							_lowerCamelCase		:      Optional[Any]=   TEXT_TO_IMAGE_BATCH_PARAMS
							_lowerCamelCase		:      Any=   PipelineTesterMixin.required_optional_params - {"latents"}


							def 						_snake_case							(			self)							->				str:
								return self._get_dummy_components()


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=0)							->				Dict:

								if str(_snake_case).startswith('mps'):
									UpperCAmelCase_		:      List[str]       	=					torch.manual_seed(_snake_case)
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					torch.Generator(device=_snake_case).manual_seed(_snake_case)

								UpperCAmelCase_		:      List[str]       	=					{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs


							def 						_snake_case							(			self)							->				Optional[Any]:
								self._test_save_load_optional_components()


							@unittest.skipIf(torch_device != 'cuda'    ,			reason='float16 requires CUDA')
							def 						_snake_case							(			self)							->				List[Any]:
								super().test_save_load_floataa(expected_max_diff=1e-1)


							def 						_snake_case							(			self)							->				List[Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


							def 						_snake_case							(			self)							->				Optional[int]:
								self._test_save_load_local()


							def 						_snake_case							(			self)							->				Optional[int]:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2    ,			)





							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()    ,			reason='XFormers attention is only available with CUDA and `xformers` installed'    ,			)
							def 						_snake_case							(			self)							->				Optional[Any]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)



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


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


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'    ,			variant='fp16'    ,			torch_dtype=torch.floataa)

								UpperCAmelCase_		:      List[str]       	=					IFSuperResolutionPipeline.from_pretrained(
								    'DeepFloyd/IF-II-L-v1.0'    ,			variant='fp16'    ,			torch_dtype=torch.floataa    ,			text_encoder=_snake_case    ,			tokenizer=_snake_case)

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to('cuda')

								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					pipe_a.encode_prompt('anime turtle'    ,			device='cuda')

								del pipe_a.tokenizer
								del pipe_a.text_encoder
								gc.collect()

								UpperCAmelCase_		:      int       	=					None
								UpperCAmelCase_		:      Optional[Any]       	=					None

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

								self._test_if(_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								UpperCAmelCase_		:      List[str]       	=					IFImgaImgPipeline(**pipe_a.components)
								UpperCAmelCase_		:      List[Any]       	=					IFImgaImgSuperResolutionPipeline(**pipe_a.components)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

								self._test_if_imgaimg(_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								UpperCAmelCase_		:      Dict       	=					IFInpaintingPipeline(**pipe_a.components)
								UpperCAmelCase_		:      Tuple       	=					IFInpaintingSuperResolutionPipeline(**pipe_a.components)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

								self._test_if_inpainting(_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								_start_torch_memory_measurement()

								UpperCAmelCase_		:      List[str]       	=					torch.Generator(device='cpu').manual_seed(0)
								UpperCAmelCase_		:      Union[str, Any]       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			num_inference_steps=2    ,			generator=_snake_case    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      Any       	=					output.images[0]

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

								UpperCAmelCase_		:      List[Any]       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								UpperCAmelCase_		:      Optional[int]       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)

								# pipeline 2

								_start_torch_memory_measurement()

								UpperCAmelCase_		:      Union[str, Any]       	=					torch.Generator(device='cpu').manual_seed(0)

								UpperCAmelCase_		:      Any       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(0)).to(_snake_case)

								UpperCAmelCase_		:      List[str]       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			image=_snake_case    ,			generator=_snake_case    ,			num_inference_steps=2    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      Tuple       	=					output.images[0]

								assert image.shape == (256, 256, 3)

								UpperCAmelCase_		:      Dict       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								UpperCAmelCase_		:      Dict       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[str]:
								_start_torch_memory_measurement()

								UpperCAmelCase_		:      Dict       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(0)).to(_snake_case)

								UpperCAmelCase_		:      Any       	=					torch.Generator(device='cpu').manual_seed(0)

								UpperCAmelCase_		:      int       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			image=_snake_case    ,			num_inference_steps=2    ,			generator=_snake_case    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      List[str]       	=					output.images[0]

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

								UpperCAmelCase_		:      str       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								UpperCAmelCase_		:      str       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)

								# pipeline 2

								_start_torch_memory_measurement()

								UpperCAmelCase_		:      int       	=					torch.Generator(device='cpu').manual_seed(0)

								UpperCAmelCase_		:      Tuple       	=					floats_tensor((1, 3, 256, 256)    ,			rng=random.Random(0)).to(_snake_case)
								UpperCAmelCase_		:      int       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(0)).to(_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			image=_snake_case    ,			original_image=_snake_case    ,			generator=_snake_case    ,			num_inference_steps=2    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      Any       	=					output.images[0]

								assert image.shape == (256, 256, 3)

								UpperCAmelCase_		:      Dict       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								UpperCAmelCase_		:      List[Any]       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[str]:
								_start_torch_memory_measurement()

								UpperCAmelCase_		:      Union[str, Any]       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(0)).to(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(1)).to(_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					torch.Generator(device='cpu').manual_seed(0)
								UpperCAmelCase_		:      Any       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			image=_snake_case    ,			mask_image=_snake_case    ,			num_inference_steps=2    ,			generator=_snake_case    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					output.images[0]

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

								UpperCAmelCase_		:      Tuple       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								UpperCAmelCase_		:      str       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)

								# pipeline 2

								_start_torch_memory_measurement()

								UpperCAmelCase_		:      Union[str, Any]       	=					torch.Generator(device='cpu').manual_seed(0)

								UpperCAmelCase_		:      Optional[int]       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(0)).to(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					floats_tensor((1, 3, 256, 256)    ,			rng=random.Random(0)).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					floats_tensor((1, 3, 256, 256)    ,			rng=random.Random(1)).to(_snake_case)

								UpperCAmelCase_		:      Any       	=					pipe_a(
								    prompt_embeds=_snake_case    ,			negative_prompt_embeds=_snake_case    ,			image=_snake_case    ,			mask_image=_snake_case    ,			original_image=_snake_case    ,			generator=_snake_case    ,			num_inference_steps=2    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      List[str]       	=					output.images[0]

								assert image.shape == (256, 256, 3)

								UpperCAmelCase_		:      Dict       	=					torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								UpperCAmelCase_		:      Optional[Any]       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy')
								assert_mean_pixel_difference(_snake_case    ,			_snake_case)







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= 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.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= 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)






