
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def 	A_ (							*snake_case							:   str )      ->    Dict:



						'''simple docstring'''

						if not isinstance(lowerCamelCase_ ,      lowerCamelCase_ ):
												__UpperCamelCase       =			list(lowerCamelCase_ )
						for i in range(len(lowerCamelCase_ ) ):
												__UpperCamelCase       =			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 	A_ (							snake_case							:   Union[str, Any] )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			[
						    """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 	A_ (							snake_case							:   List[Any] = None ,      snake_case							:   List[str] = 128 )      ->    str:



						'''simple docstring'''

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

						__UpperCamelCase       =			starting_batch_size

						def decorator(*snake_case							:   Any ,      **snake_case							:   Any ):
												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       =			list(inspect.signature(lowerCamelCase_ ).parameters.keys() )
												# Guard against user error
												if len(lowerCamelCase_ ) < (len(lowerCamelCase_ ) + 1):
																		__UpperCamelCase       =			""", """.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






def 	A_ (							snake_case							:   int = 10 ,      snake_case							:   int = 1000 ,      snake_case							:   bool = True )      ->    int:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)''' )
						return min_val if option else max_val




def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						return int((number_a + number_a) / 2 )




def 	A_ (							snake_case							:   int ,      snake_case							:   int ,      snake_case							:   int )      ->    None:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError('''argument value for lower and higher must be(lower > higher)''' )

						if not lower < to_guess < higher:
												raise ValueError(
												    '''guess value must be within the range of lower and higher value''' )

						def answer(snake_case							:   int ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print('''started...''' )

						__UpperCamelCase       =			lower
						__UpperCamelCase       =			higher

						__UpperCamelCase       =			[]

						while True:
												__UpperCamelCase       =			get_avg(snake_case ,      snake_case )
												last_numbers.append(snake_case )

												if answer(snake_case ) == "low":
																		__UpperCamelCase       =			number
												elif answer(snake_case ) == "high":
																		__UpperCamelCase       =			number
												else:
																		break

						print(f"guess the number : {last_numbers[-1]}" )
						print(f"details : {last_numbers!s}" )




def 	A_ (							)      ->    None:



						'''simple docstring'''

						__UpperCamelCase       =			int(input('''Enter lower value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter high value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter value to guess : ''' ).strip() )
						guess_the_number(snake_case ,      snake_case ,      snake_case )


if __name__ == "__main__":
				main()




