



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__A             =False

@nightly
@require_torch_gpu
class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			)
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(a_			)
					pipe.set_progress_bar_config(disable=a_			)

					__UpperCAmelCase   :					Dict													=     load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)

					__UpperCAmelCase   :					str													=     torch.manual_seed(0			)
					__UpperCAmelCase   :					Dict													=     pipe(
					    prompt='''first prompt'''	,	image=a_	,	text_to_image_strength=0.7_5	,	generator=a_	,	guidance_scale=7.5	,	num_inference_steps=2	,	output_type='''numpy'''	,	).images

					with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(a_			)
							__UpperCAmelCase   :					str													=     VersatileDiffusionDualGuidedPipeline.from_pretrained(a_			)

					pipe.to(a_			)
					pipe.set_progress_bar_config(disable=a_			)

					__UpperCAmelCase   :					str													=     generator.manual_seed(0			)
					__UpperCAmelCase   :					int													=     pipe(
					    prompt='''first prompt'''	,	image=a_	,	text_to_image_strength=0.7_5	,	generator=a_	,	guidance_scale=7.5	,	num_inference_steps=2	,	output_type='''numpy'''	,	).images

					assert np.abs(image - new_image			).sum() < 1e-5, "Models don't have the same forward pass"




			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			)
					pipe.remove_unused_weights()
					pipe.to(a_			)
					pipe.set_progress_bar_config(disable=a_			)

					__UpperCAmelCase   :					Optional[Any]													=     '''cyberpunk 2077'''
					__UpperCAmelCase   :					Tuple													=     load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     torch.manual_seed(0			)
					__UpperCAmelCase   :					int													=     pipe(
					    prompt=a_	,	image=a_	,	text_to_image_strength=0.7_5	,	generator=a_	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type='''numpy'''	,	).images

					__UpperCAmelCase   :					str													=     image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__UpperCAmelCase   :					Dict													=     np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]			)

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










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





