




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       inspect.getfile(accelerate.test_utils			)
											__lowerCAmelCase				   =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_script.py"]			)
											__lowerCAmelCase				   =       os.path.sep.join(
											    mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_distributed_data_loop.py"]			)
											__lowerCAmelCase				   =       os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_ops.py"]			)






						@require_multi_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											print(f"""Found {torch.cuda.device_count()} devices."""			)
											__lowerCAmelCase				   =       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
											with patch_environment(omp_num_threads=1			):
																execute_subprocess_async(_A		,			env=os.environ.copy()			)






						@require_multi_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											print(f"""Found {torch.cuda.device_count()} devices."""			)
											__lowerCAmelCase				   =       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
											print(f"""Command: {cmd}"""			)
											with patch_environment(omp_num_threads=1			):
																execute_subprocess_async(_A		,			env=os.environ.copy()			)






						@require_multi_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__			)]
											with patch_environment(omp_num_threads=1			):
																execute_subprocess_async(_A		,			env=os.environ.copy()			)






						@require_multi_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""			)
											__lowerCAmelCase				   =       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
											with patch_environment(omp_num_threads=1		,			cuda_visible_devices="0,1"			):
																execute_subprocess_async(_A		,			env=os.environ.copy()			)


if __name__ == "__main__":
					UpperCamelCase__    			=			Accelerator()
					UpperCamelCase__    			=			(accelerator.state.process_index + 2, 10)
					UpperCamelCase__    			=			torch.randint(0, 10, shape).to(accelerator.device)

					UpperCamelCase__    			=			""""""

					UpperCamelCase__    			=			accelerator.pad_across_processes(tensor)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
										error_msg += "Padding was not done with the right value (0)."

					UpperCamelCase__    			=			accelerator.pad_across_processes(tensor, pad_first=True)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					UpperCamelCase__    			=			accelerator.state.num_processes - accelerator.state.process_index - 1
					if not torch.equal(tensora[index:], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[:index] == 0):
										error_msg += "Padding was not done with the right value (0)."

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)










import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)




