





'''simple docstring'''




from __future__ import annotations

import math

lowercase__       : List[str]         =       '''2020.9.26'''
lowercase__       : Any         =       '''xcodz-dot, cclaus, dhruvmanila'''






def 			_lowerCAmelCase     (    __snake_case    :					float					,				__snake_case    :					float					,				__snake_case    :					float					,				__snake_case    :					float					,				__snake_case    :					float     )	->	tuple[float, float]:
     if not all(isinstance(__snake_case					,				(float, int)     ) for val in locals().values()     ):
          __A		:					int						=			f'Input values must either be float or int: {list(locals().values()     )}'
          raise TypeError(__snake_case     )
     __A		:					Any						=			((x * distance) / (z + distance)) * scale
     __A		:					List[str]						=			((y * distance) / (z + distance)) * scale
     return projected_x, projected_y






def 			_lowerCAmelCase     (    __snake_case    :					float					,				__snake_case    :					float					,				__snake_case    :					float					,				__snake_case    :					str					,				__snake_case    :					float     )	->	tuple[float, float, float]:
     if not isinstance(__snake_case					,				__snake_case     ):
          raise TypeError('Axis must be a str'     )
     __A		:					str						=			locals()
     del input_variables["axis"]
     if not all(isinstance(__snake_case					,				(float, int)     ) for val in input_variables.values()     ):
          __A		:					List[Any]						=			(
              'Input values except axis must either be float or int: '
              f'{list(input_variables.values()     )}'
          )
          raise TypeError(__snake_case     )
     __A		:					Union[str, Any]						=			(angle % 3_60) / 4_50 * 1_80 / math.pi
     if axis == "z":
          __A		:					Tuple						=			x * math.cos(__snake_case     ) - y * math.sin(__snake_case     )
          __A		:					Tuple						=			y * math.cos(__snake_case     ) + x * math.sin(__snake_case     )
          __A		:					str						=			z
     elif axis == "x":
          __A		:					Any						=			y * math.cos(__snake_case     ) - z * math.sin(__snake_case     )
          __A		:					List[Any]						=			z * math.cos(__snake_case     ) + y * math.sin(__snake_case     )
          __A		:					Any						=			x
     elif axis == "y":
          __A		:					Optional[Any]						=			x * math.cos(__snake_case     ) - z * math.sin(__snake_case     )
          __A		:					List[str]						=			z * math.cos(__snake_case     ) + x * math.sin(__snake_case     )
          __A		:					int						=			y
     else:
          raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''     )

     return new_x, new_y, new_z


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(f"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
  print(f"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")






'''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


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			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):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d