



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     List[str]									= ArgumentParser(
		    description=(
		        '''PyTorch TPU distributed training launch '''
		        '''helper utility that will spawn up '''
		        '''multiple distributed processes'''
		    ) )

		# Optional arguments for the launch helper
		parser.add_argument('''--num_cores'''     ,type=__UpperCamelCase     ,default=1     ,help='''Number of TPU cores to use (1 or 8).''' )

		# positional
		parser.add_argument(
		    '''training_script'''     ,type=__UpperCamelCase     ,help=(
		        '''The full path to the single TPU training '''
		        '''program/script to be launched in parallel, '''
		        '''followed by all the arguments for the '''
		        '''training script'''
		    )     ,)

		# rest from the training program
		parser.add_argument('''training_script_args'''     ,nargs=__UpperCamelCase )

		return parser.parse_args()


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= parse_args()

		# Import training_script as a module.
		lowerCAmelCase_			:     str									= Path(args.training_script )
		sys.path.append(str(script_fpath.parent.resolve() ) )
		lowerCAmelCase_			:     Tuple									= script_fpath.stem
		lowerCAmelCase_			:     Union[str, Any]									= importlib.import_module(__UpperCamelCase )

		# Patch sys.argv
		lowerCAmelCase_			:     Optional[int]									= [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
		xmp.spawn(mod._mp_fn     ,args=()     ,nprocs=args.num_cores )


if __name__ == "__main__":
						main()










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 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = IFPipeline
							_a			      = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							_a			      = TEXT_TO_IMAGE_BATCH_PARAMS
							_a			      = PipelineTesterMixin.required_optional_params - {'''latents'''}




							def    UpperCAmelCase__			(       self     :       List[str]):
									return self._get_dummy_components()




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       List[Any]				,				A_     :       Any=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_			:     List[Any]									= torch.manual_seed(A_)
									else:
											lowerCAmelCase_			:     List[str]									= torch.Generator(device=A_).manual_seed(A_)

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

									return inputs




							def    UpperCAmelCase__			(       self     :       int):
									self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''				,				reason='''float16 requires CUDA''')
							def    UpperCAmelCase__			(       self     :       str):
									# 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    UpperCAmelCase__			(       self     :       str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def    UpperCAmelCase__			(       self     :       int):
									self._test_save_load_local()




							def    UpperCAmelCase__			(       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    UpperCAmelCase__			(       self     :       Optional[int]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)





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




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




							def    UpperCAmelCase__			(       self     :       List[str]):
									# if

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

									lowerCAmelCase_			:     Dict									= IFSuperResolutionPipeline.from_pretrained(
									    '''DeepFloyd/IF-II-L-v1.0'''				,				variant='''fp16'''				,				torch_dtype=torch.floataa				,				text_encoder=A_				,				tokenizer=A_)

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to('''cuda''')

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

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

									lowerCAmelCase_			:     Tuple									= None
									lowerCAmelCase_			:     str									= 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(A_				,				A_				,				A_				,				A_)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									lowerCAmelCase_			:     List[str]									= IFImgaImgPipeline(**pipe_a.components)
									lowerCAmelCase_			:     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(A_				,				A_				,				A_				,				A_)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									lowerCAmelCase_			:     int									= IFInpaintingPipeline(**pipe_a.components)
									lowerCAmelCase_			:     Union[str, Any]									= 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(A_				,				A_				,				A_				,				A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       Any				,				A_     :       str				,				A_     :       Union[str, Any]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)
									lowerCAmelCase_			:     Optional[Any]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Dict									= output.images[0]

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     Any									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_3 * 1_0**9

									lowerCAmelCase_			:     List[str]									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
									assert_mean_pixel_difference(A_				,				A_)

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     List[str]									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     str									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     Optional[int]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     Any									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

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




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       Any				,				A_     :       List[str]				,				A_     :       List[str]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Any									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     Tuple									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     Any									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_0 * 1_0**9

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

									# pipeline 2

									_start_torch_memory_measurement()

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

									lowerCAmelCase_			:     List[Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Any									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     List[str]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				original_image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

									lowerCAmelCase_			:     Tuple									= 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(A_				,				A_)







							def    UpperCAmelCase__			(       self     :       str				,				A_     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       Dict				,				A_     :       List[str]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     int									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(1)).to(A_)

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)
									lowerCAmelCase_			:     Any									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				mask_image=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

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

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_0 * 1_0**9

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

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     Optional[Any]									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Optional[Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(1)).to(A_)

									lowerCAmelCase_			:     int									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				mask_image=A_				,				original_image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Tuple									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     int									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

									lowerCAmelCase_			:     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(A_				,				A_)





def 			UpperCamelCase(  ):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()





