code: 
  import inspect
  import os
  import unittest
  
  import torch
  
  import accelerate
  from accelerate import debug_launcher
  from accelerate.test_utils import (
      execute_subprocess_async,
      require_cpu,
      require_multi_gpu,
      require_single_gpu,
      test_sync,
  )
  from accelerate.utils import patch_environment
  
  
  
  
  
  class      UpperCamelCase_			(			unittest.TestCase		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->Any:
  										lowercase						    =			inspect.getfile(accelerate.test_utils    )
  										lowercase						    =			os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["scripts", "test_sync.py"]    )
  
  
  
  
  
  						@require_cpu
  						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->str:
  										debug_launcher(test_sync.main		,						num_processes=1    )
  
  
  
  
  
  						@require_cpu
  						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->str:
  										debug_launcher(test_sync.main    )
  
  
  
  
  
  						@require_single_gpu
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Tuple:
  										test_sync.main()
  
  
  
  
  
  						@require_multi_gpu
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->str:
  										print(F'''Found {torch.cuda.device_count()} devices.'''    )
  										lowercase						    =			["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()    )
  
style_context: 
  import sys
  from collections.abc import Mapping
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  
  
  
  class      UpperCamelCase_			(			TensorFormatter[Mapping, np.ndarray, Mapping]		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :int		,						lowerCAmelCase__       :Any=None		,						**lowerCAmelCase__       :Union[str, Any]    )     ->int:
  										super().__init__(features=lowerCAmelCase__    )
  										lowercase						    =			np_array_kwargs
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :str    )     ->Optional[Any]:
  										if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ):
  														if column and all(
  														    isinstance(lowerCAmelCase__		,						np.ndarray    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
  																		return np.stack(lowerCAmelCase__    )
  														else:
  																		# don't use np.array(column, dtype=object)
  																		# since it fails in certain cases
  																		# see https://stackoverflow.com/q/51005699
  																		lowercase						    =			np.empty(len(lowerCAmelCase__    )		,						dtype=lowerCAmelCase__    )
  																		lowercase						    =			column
  																		return out
  										return column
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :List[str]    )     ->Union[str, Any]:
  										if isinstance(lowerCAmelCase__		,						(str, bytes, type(lowerCAmelCase__    ))    ):
  														return value
  										elif isinstance(lowerCAmelCase__		,						(np.character, np.ndarray)    ) and np.issubdtype(value.dtype		,						np.character    ):
  														return value
  										elif isinstance(lowerCAmelCase__		,						np.number    ):
  														return value
  
  										lowercase						    =			{}
  
  										if isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.integer    ):
  														lowercase						    =			{"dtype": np.intaa}
  										elif isinstance(lowerCAmelCase__		,						np.ndarray    ) and np.issubdtype(value.dtype		,						np.floating    ):
  														lowercase						    =			{"dtype": np.floataa}
  										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
  														import PIL.Image
  
  														if isinstance(lowerCAmelCase__		,						PIL.Image.Image    ):
  																		return np.asarray(lowerCAmelCase__		,						**self.np_array_kwargs    )
  
  										return np.asarray(lowerCAmelCase__		,						**{**default_dtype, **self.np_array_kwargs}    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :Dict    )     ->int:
  										# support for torch, tf, jax etc.
  										if config.TORCH_AVAILABLE and "torch" in sys.modules:
  														import torch
  
  														if isinstance(lowerCAmelCase__		,						torch.Tensor    ):
  																		return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
  										if hasattr(lowerCAmelCase__		,						"__array__"    ) and not isinstance(lowerCAmelCase__		,						(np.ndarray, np.character, np.number)    ):
  														lowercase						    =			data_struct.__array__()
  										# support for nested types like struct of list of struct
  										if isinstance(lowerCAmelCase__		,						np.ndarray    ):
  														if data_struct.dtype == object:
  																		return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
  										if isinstance(lowerCAmelCase__		,						(list, tuple)    ):
  														return self._consolidate([self.recursive_tensorize(lowerCAmelCase__    ) for substruct in data_struct]    )
  										return self._tensorize(lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :dict    )     ->str:
  										return map_nested(self._recursive_tensorize		,						lowerCAmelCase__		,						map_list=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
  										lowercase						    =			self.numpy_arrow_extractor().extract_row(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_row(lowerCAmelCase__    )
  										return self.recursive_tensorize(lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :pa.Table    )     ->np.ndarray:
  										lowercase						    =			self.numpy_arrow_extractor().extract_column(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_column(lowerCAmelCase__		,						pa_table.column_names[0]    )
  										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
  										lowercase						    =			self._consolidate(lowerCAmelCase__    )
  										return column
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :pa.Table    )     ->Mapping:
  										lowercase						    =			self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__    )
  										lowercase						    =			self.python_features_decoder.decode_batch(lowerCAmelCase__    )
  										lowercase						    =			self.recursive_tensorize(lowerCAmelCase__    )
  										for column_name in batch:
  														lowercase						    =			self._consolidate(batch[column_name]    )
  										return batch
  