





a		:				Union[str, Any]  			=    {
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    10: "a",
    11: "b",
    12: "c",
    13: "d",
    14: "e",
    15: "f",
}






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float      )				->str:






	'''simple docstring'''






	assert type(_lowercase      ) in (int, float) and decimal == int(_lowercase      )
	a   :	Tuple			=       int(_lowercase      )
	a   :	int			=       ''
	a   :	Tuple			=       False
	if decimal < 0:
		a   :	Optional[Any]			=       True
		decimal *= -1
	while decimal > 0:
		a   :	Dict			=       divmod(_lowercase					, 16      )
		a   :	Tuple			=       values[remainder] + hexadecimal
	a   :	Optional[Any]			=       '0x' + hexadecimal
	if negative:
		a   :	str			=       '-' + hexadecimal
	return hexadecimal


if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""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  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =IFPipeline
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCamelCase     :      int         =PipelineTesterMixin.required_optional_params - {"""latents"""}






							def 					__a     (     self   )			->				List[str]:
								return self._get_dummy_components()






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Dict:
								if str(lowerCAmelCase__   ).startswith("mps"   ):
									a   :	Tuple			=       torch.manual_seed(lowerCAmelCase__   )
								else:
									a   :	int			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )

								a   :	Optional[Any]			=       {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_save_load_optional_components()






							@unittest.skipIf(torch_device != "cuda"   ,      reason="float16 requires CUDA"   )
							def 					__a     (     self   )			->				Any:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1   )






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )






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






							def 					__a     (     self   )			->				Tuple:
								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 					__a     (     self   )			->				str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )


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






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






							def 					__a     (     self   )			->				Tuple:
								# if

								a   :	Tuple			=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   )

								a   :	str			=       IFSuperResolutionPipeline.from_pretrained(
								    "DeepFloyd/IF-II-L-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   )

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to("cuda"   )

								a, a   :	List[str]			=       pipe_a.encode_prompt("anime turtle"   ,      device="cuda"   )

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

								a   :	Optional[int]			=       None
								a   :	Optional[int]			=       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(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								a   :	Union[str, Any]			=       IFImgaImgPipeline(**pipe_a.components   )
								a   :	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(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								a   :	Union[str, Any]			=       IFInpaintingPipeline(**pipe_a.components   )
								a   :	List[str]			=       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(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

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

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

								a   :	Dict			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								a   :	Optional[Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Union[str, Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

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

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

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	int			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Tuple			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	Tuple			=       output.images[0]

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

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								a   :	Optional[int]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	str			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	int			=       output.images[0]

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

								a   :	str			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	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(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	Union[str, Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	List[Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	List[str]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	List[Any]			=       output.images[0]

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

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

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	str			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	int			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	Dict			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

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

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

								a   :	Tuple			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	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(lowerCAmelCase__   ,      lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->List[str]:






	'''simple docstring'''






	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()


