




'''simple docstring'''


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   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


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







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Any:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices.'   )
														_a      	=							['''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(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														_a      	=							['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
														with patch_environment(omp_num_threads=1   ):
																					execute_subprocess_async(lowerCAmelCase_				,     env=os.environ.copy()   )







							@require_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'   )
														_a      	=							['''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(lowerCAmelCase_				,     env=os.environ.copy()   )


if __name__ == "__main__":
				_snake_case : str  				=						Accelerator()
				_snake_case : str  				=						(accelerator.state.process_index + 2, 10)
				_snake_case : Optional[Any]  				=						torch.randint(0, 10, shape).to(accelerator.device)

				_snake_case : Dict  				=						''

				_snake_case : Optional[int]  				=						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)."

				_snake_case : str  				=						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."
				_snake_case : int  				=						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)





'''simple docstring'''






from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



