






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def  _lowerCamelCase						(						*__lowerCamelCase       ) ->			Optional[int]:

     '''simple docstring'''
     if not isinstance(__lowerCamelCase ,							__lowerCamelCase       ):
          UpperCAmelCase__      :  Any				      =			list(__lowerCamelCase       )
     for i in range(len(__lowerCamelCase       )       ):
          UpperCAmelCase__      :  List[str]				      =			None
     gc.collect()
     if is_xpu_available():
          torch.xpu.empty_cache()
     elif is_npu_available():
          torch.npu.empty_cache()
     else:
          torch.cuda.empty_cache()
     return objects





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			bool:

     '''simple docstring'''
     UpperCAmelCase__      :  str				      =			[
         """CUDA out of memory.""",  # CUDA OOM
         """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
         """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
     ]
     if isinstance(__lowerCamelCase ,							__lowerCamelCase       ) and len(exception.args       ) == 1:
          return any(err in exception.args[0] for err in _statements       )
     return False







def  _lowerCamelCase						(						__lowerCamelCase = None ,							__lowerCamelCase = 128       ) ->			Dict:

     '''simple docstring'''
     if function is None:
          return functools.partial(__lowerCamelCase ,							starting_batch_size=__lowerCamelCase       )

     UpperCAmelCase__      :  Union[str, Any]				      =			starting_batch_size

     def decorator(*__lowerCamelCase ,							**__lowerCamelCase       ):
          nonlocal batch_size
          gc.collect()
          if is_xpu_available():
               torch.xpu.empty_cache()
          elif is_npu_available():
               torch.npu.empty_cache()
          else:
               torch.cuda.empty_cache()
          UpperCAmelCase__      :  int				      =			list(inspect.signature(__lowerCamelCase       ).parameters.keys()       )
          # Guard against user error
          if len(__lowerCamelCase       ) < (len(__lowerCamelCase       ) + 1):
               UpperCAmelCase__      :  Dict				      =			""", """.join([F"{arg}={value}" for arg, value in zip(params[1:] ,							args[1:]       )]       )
               raise TypeError(
                   F"Batch size was passed into `{function.__name__}` as the first argument when called."
                   F"Remove this as the decorator already does so: `{function.__name__}({arg_str})`"       )
          while True:
               if batch_size == 0:
                    raise RuntimeError("""No executable batch size found, reached zero."""       )
               try:
                    return function(__lowerCamelCase ,							*__lowerCamelCase ,							**__lowerCamelCase       )
               except Exception as e:
                    if should_reduce_batch_size(__lowerCamelCase       ):
                         gc.collect()
                         if is_xpu_available():
                              torch.xpu.empty_cache()
                         elif is_npu_available():
                              torch.npu.empty_cache()
                         else:
                              torch.cuda.empty_cache()
                         batch_size //= 2
                    else:
                         raise

     return decorator













import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase__           =			"\nimport os\n"

UpperCamelCase__           =			"\ndef foo():\n    import os\n    return False\n"

UpperCamelCase__           =			"\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n"

UpperCamelCase__           =			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]





@pytest.mark.parametrize("""case""" ,      a__							)
def      _UpperCamelCase  (a__ :Any ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      os.path.join(a__ ,      """test_file.py"""							)
							with open(a__ ,      """w"""							) as _tmp_file:
														_tmp_file.write(a__							)

							UpperCamelCase__					  =      get_imports(a__							)
							assert parsed_imports == ["os"]




