






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def 	UpperCamelCase	(							snake_case__  : Tuple						,snake_case__  : Optional[int]						,snake_case__  : Union[str, Any] = False	):




     '''simple docstring'''






     if radian_mode:
          return [magnitude * cos(snake_case__	), magnitude * sin(snake_case__	)]
     return [magnitude * cos(radians(snake_case__	)	), magnitude * sin(radians(snake_case__	)	)]






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : List[str]						,snake_case__  : Optional[int] = 10**-1	):




     '''simple docstring'''






     __snake_case      :Dict 							=    cross(snake_case__						,snake_case__	)
     __snake_case      :str 							=    sum(snake_case__	)
     return abs(snake_case__	) < eps


if __name__ == "__main__":
     # Test to check if it works
     lowerCamelCase__       		=  array(
         [
             polar_force(7_1_8.4, 180 - 30),
             polar_force(8_7_9.5_4, 45),
             polar_force(100, -90),
         ]
     )

     lowerCamelCase__       		=  array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem 1 in image_data/2D_problems.jpg
     lowerCamelCase__       		=  array(
         [
             polar_force(30 * 9.8_1, 15),
             polar_force(215, 180 - 45),
             polar_force(264, 90 - 30),
         ]
     )

     lowerCamelCase__       		=  array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem in image_data/2D_problems_1.jpg
     lowerCamelCase__       		=  array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

     lowerCamelCase__       		=  array([[0, 0], [6, 0], [10, 0], [12, 0]])

     assert in_static_equilibrium(forces, location)

     import doctest

     doctest.testmod()






'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d