



"""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__			(      A,					A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					IFPipeline
							_UpperCAmelCase      				=					TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							_UpperCAmelCase      				=					TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {'''latents'''}






							def        lowerCamelCase_   (  self						)			->							int:
									return self._get_dummy_components()






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							int:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)

									_UpperCAmelCase							 =		{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}

									return inputs






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_save_load_optional_components()






							@unittest.skipIf(torch_device != 'cuda'	,       reason='float16 requires CUDA'						)
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									# 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        lowerCamelCase_   (  self						)			->							Any:
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_save_load_local()






							def        lowerCamelCase_   (  self						)			->							str:
									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        lowerCamelCase_   (  self						)			->							List[str]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Dict:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							Dict:
									# if

									_UpperCAmelCase							 =		IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'	,       variant='fp16'	,       torch_dtype=torch.floataa						)

									_UpperCAmelCase							 =		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							 =		pipe_a.encode_prompt('anime turtle'	,       device='cuda'						)

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

									_UpperCAmelCase							 =		None
									_UpperCAmelCase							 =		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							 =		IFImgaImgPipeline(**pipe_a.components						)
									_UpperCAmelCase							 =		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							 =		IFInpaintingPipeline(**pipe_a.components						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									# pipeline 1

									_start_torch_memory_measurement()

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
									_UpperCAmelCase							 =		pipe_a(
									    prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       num_inference_steps=2	,       generator=snake_case	,       output_type='np'	,       )

									_UpperCAmelCase							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									_UpperCAmelCase							 =		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							 =		torch.Generator(device='cpu'						).manual_seed(0						)

									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

									_UpperCAmelCase							 =		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							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							List[Any]:
									# pipeline 1

									_start_torch_memory_measurement()

									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)

									_UpperCAmelCase							 =		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							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									_UpperCAmelCase							 =		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							 =		torch.Generator(device='cpu'						).manual_seed(0						)

									_UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(0						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

									_UpperCAmelCase							 =		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							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									# pipeline 1

									_start_torch_memory_measurement()

									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(1						)						).to(snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
									_UpperCAmelCase							 =		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							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									_UpperCAmelCase							 =		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							 =		torch.Generator(device='cpu'						).manual_seed(0						)

									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(0						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(1						)						).to(snake_case						)

									_UpperCAmelCase							 =		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							 =		output.images[0]

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

									_UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									_UpperCAmelCase							 =		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 	UpperCAmelCase     (		):

		'''simple docstring'''


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







"""simple docstring"""





import os




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		os.path.join(os.path.dirname(A       )     ,				'num.txt'       )
		with open(A       ) as file_hand:
				return str(sum(int(A       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
					print(solution())


