




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


__lowerCAmelCase		:				Any       			=		logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__   )
class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							bool							 =					field(default=_UpperCamelCase    ,       metadata={"""help""": """Whether to use SortishSampler or not."""}   )
				SCREAMING_SNAKE_CASE_       :							bool							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					field(
				    default=_UpperCamelCase    ,       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."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					field(
				    default=_UpperCamelCase    ,       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."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[Union[str, Path, GenerationConfig]]							 =					field(
				    default=_UpperCamelCase    ,       metadata={
				        """help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
				    }    ,       )





				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							a    =			super().to_dict()
							for k, v in d.items():
										if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
													a    =			v.to_dict()
							return d









def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




