



'''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      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						List[Any]     =					inspect.getfile(accelerate.test_utils							)
				A_     :						Optional[int]     =					os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_script.py"""]							)
				A_     :						List[str]     =					os.path.sep.join(
				    mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]							)
				A_     :						Optional[int]     =					os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_ops.py"""]							)


	@require_multi_gpu
	def    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices.'							)
				A_     :						Union[str, Any]     =					["""torchrun""", F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
				with patch_environment(omp_num_threads=1							):
							execute_subprocess_async(lowercase	,				env=os.environ.copy()							)


	@require_multi_gpu
	def    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices.'							)
				A_     :						Tuple     =					["""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(lowercase	,				env=os.environ.copy()							)


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


	@require_multi_gpu
	def    _a     (self							):
				print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'							)
				A_     :						Union[str, Any]     =					["""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(lowercase	,				env=os.environ.copy()							)


if __name__ == "__main__":
						lowerCamelCase							:List[Any]       = Accelerator()
						lowerCamelCase							:int       = (accelerator.state.process_index + 2, 1_0)
						lowerCamelCase							:str       = torch.randint(0, 1_0, shape).to(accelerator.device)

						lowerCamelCase							:str       = ''''''

						lowerCamelCase							:Union[str, Any]       = 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)."

						lowerCamelCase							:Tuple       = 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."
						lowerCamelCase							:Tuple       = 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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:Dict       = {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:str       = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						lowerCamelCase							:Tuple       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)