



"""simple docstring"""


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


snake_case_    					=	logging.get_logger(__name__)







@dataclass
class 				A_      (					lowerCamelCase__    ):



						"""simple docstring"""
						__UpperCamelCase		    =							[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__( self			:Union[str, Any]       ,  **lowercase_			:int )    ->      List[str]:





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

												UpperCAmelCase					=			kwargs.pop('torchscript'       ,  self.torchscript )
												UpperCAmelCase					=			kwargs.pop('torch_xla_tpu_print_metrics'       ,  self.torch_xla_tpu_print_metrics )
												UpperCAmelCase					=			kwargs.pop('fp16_opt_level'       ,  self.fpaa_opt_level )
												super().__init__(**snake_case__ )

						__UpperCamelCase		    =							field(default=lowerCamelCase__     ,       metadata={"""help""": """Trace the models using torchscript"""}    )
						__UpperCamelCase		    =							field(default=lowerCamelCase__     ,       metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}    )
						__UpperCamelCase		    =							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      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple["torch.device", int]:
												requires_backends(self       ,  ['torch'] )
												logger.info('PyTorch: setting up devices' )
												if not self.cuda:
																		UpperCAmelCase					=			torch.device('cpu' )
																		UpperCAmelCase					=			0
												elif is_torch_tpu_available():
																		UpperCAmelCase					=			xm.xla_device()
																		UpperCAmelCase					=			0
												else:
																		UpperCAmelCase					=			torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
																		UpperCAmelCase					=			torch.cuda.device_count()
												return device, n_gpu






						@property
						def      UpperCAmelCase__					( self			:str )    ->      int:
												return is_torch_tpu_available() and self.tpu






						@property
						def      UpperCAmelCase__					( self			:int )    ->      int:
												requires_backends(self       ,  ['torch'] )
												# TODO(PVP): currently only single GPU is supported
												return torch.cuda.current_device()






						@property
						def      UpperCAmelCase__					( self			:Dict )    ->      "torch.device":
												requires_backends(self       ,  ['torch'] )
												return self._setup_devices[0]






						@property
						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												requires_backends(self       ,  ['torch'] )
												return self._setup_devices[1]






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[int]:
												return self.n_gpu > 0









"""simple docstring"""


from math import factorial, radians

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = 18		,     lowercase_ = 10						):
						UpperCAmelCase					=			angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

						# Converting from degrees to radians
						UpperCAmelCase					=			radians(lowercase_						)

						UpperCAmelCase					=			angle_in_radians
						UpperCAmelCase					=			3
						UpperCAmelCase					=			-1

						for _ in range(lowercase_						):
												result += (b * (angle_in_radians**a)) / factorial(lowercase_						)

												UpperCAmelCase					=			-b  # One positive term and the next will be negative and so on...
												a += 2  # Increased by 2 for every term.

						return round(lowercase_		,     lowercase_						)


if __name__ == "__main__":
				__import__("""doctest""").testmod()





