


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
	import torch

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


A	:		int    =       logging.get_logger(__name__)



@dataclass
class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			[
					    '''no_inference''',
					    '''no_cuda''',
					    '''no_tpu''',
					    '''no_speed''',
					    '''no_memory''',
					    '''no_env_print''',
					    '''no_multi_process''',
					]

					def __init__(self   :						Tuple				, **_UpperCAmelCase   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
											lowercase__					      =		deprecated_arg[3:]
											setattr(self				, _UpperCAmelCase				, not kwargs.pop(_UpperCAmelCase		)		)
											logger.warning(
											    f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
											    f''' {positive_arg}={kwargs[positive_arg]}'''		)

							lowercase__					      =		kwargs.pop("""torchscript"""				, self.torchscript		)
							lowercase__					      =		kwargs.pop("""torch_xla_tpu_print_metrics"""				, self.torch_xla_tpu_print_metrics		)
							lowercase__					      =		kwargs.pop("""fp16_opt_level"""				, self.fpaa_opt_level		)
							super().__init__(**_UpperCAmelCase		)

					A__      =			field(default=UpperCAmelCase__			,  metadata={'''help''': '''Trace the models using torchscript'''}							)
					A__      =			field(default=UpperCAmelCase__			,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}							)
					A__      =			field(
					    default='''O1'''			,  metadata={
					        '''help''': (
					            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
					            '''See details at https://nvidia.github.io/apex/amp.html'''
					        )
					    }			,  )

					@cached_property
					def   lowerCamelCase__					(self   :						str		)						->       Tuple["torch.device", int]:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							logger.info("""PyTorch: setting up devices"""		)
							if not self.cuda:
									lowercase__					      =		torch.device("""cpu"""		)
									lowercase__					      =		0
							elif is_torch_tpu_available():
									lowercase__					      =		xm.xla_device()
									lowercase__					      =		0
							else:
									lowercase__					      =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
									lowercase__					      =		torch.cuda.device_count()
							return device, n_gpu

					@property
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							return is_torch_tpu_available() and self.tpu

					@property
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()

					@property
					def   lowerCamelCase__					(self   :						str		)						->       "torch.device":




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							return self._setup_devices[0]

					@property
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							requires_backends(self				, ["""torch"""]		)
							return self._setup_devices[1]


					@property
					def   lowerCamelCase__					(self   :						Dict		)						->       Any:




							"""simple docstring"""


							return self.n_gpu > 0





from functools import lru_cache






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  set:
		"""simple docstring"""




		lowercase__					      =		2
		lowercase__					      =		set()
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.add(__magic_name__					)
		if n > 1:
				factors.add(__magic_name__					)
		return factors






@lru_cache
def 			UpperCamelCase (     __magic_name__					:							int					)       ->  int:
		"""simple docstring"""




		return len(unique_prime_factors(__magic_name__					)					)






def 			UpperCamelCase (     __magic_name__					:							list					)       ->  bool:
		"""simple docstring"""




		return len(set(__magic_name__					)					) in (0, 1)






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  list:
		"""simple docstring"""




		lowercase__					      =		2

		while True:
				# Increment each value of a generated range
				lowercase__					      =		[base + i for i in range(__magic_name__					)]

				# Run elements through out unique_prime_factors function
				# Append our target number to the end.
				lowercase__					      =		[upf_len(__magic_name__					) for x in group]
				checker.append(__magic_name__					)

				# If all numbers in the list are equal, return the group variable.
				if equality(__magic_name__					):
						return group

				# Increment our base variable by 1
				base += 1






def 			UpperCamelCase (     __magic_name__					:							int = 4					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		run(__magic_name__					)
		return results[0] if len(__magic_name__					) else None


if __name__ == "__main__":
	print(solution())

