



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def      __UpperCamelCase     (  *_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    if not isinstance(_lowerCAmelCase				,					_lowerCAmelCase		):
        A			:	List[str]										=       list(_lowerCAmelCase		)
    for i in range(len(_lowerCAmelCase		)		):
        A			:	Optional[int]										=       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      __UpperCamelCase     (  _lowerCAmelCase		)	->  bool:



    """simple docstring"""






    A			:	Dict										=       [
        """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      __UpperCamelCase     (  _lowerCAmelCase = None				,					_lowerCAmelCase = 128		)	->  List[str]:



    """simple docstring"""






    if function is None:
        return functools.partial(_lowerCAmelCase				,					starting_batch_size=_lowerCAmelCase		)

    A			:	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()
        A			:	Tuple										=       list(inspect.signature(_lowerCAmelCase		).parameters.keys()		)
        # Guard against user error
        if len(_lowerCAmelCase		) < (len(_lowerCAmelCase		) + 1):
            A			:	str										=       """, """.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 requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """weather"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """forecast"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """onecall"""				,					params=locals()		).json()


if __name__ == "__main__":
     from pprint import pprint

     while True:
          SCREAMING_SNAKE_CASE_:int					=						input("""Enter a location:""").strip()
          if location:
               pprint(current_weather(location))
          else:
               break


