

from __future__ import annotations

__A			:					List[str]						=			1.6021e-19  # units = C





def 	__a     (      A__			:  float			,  A__			:  float			,  A__			:  float			,  ):
    if (conductivity, electron_conc, mobility).count(0       ) != 1:
        raise ValueError("You cannot supply more or less than 2 values"       )
    elif conductivity < 0:
        raise ValueError("Conductivity cannot be negative"       )
    elif electron_conc < 0:
        raise ValueError("Electron concentration cannot be negative"       )
    elif mobility < 0:
        raise ValueError("mobility cannot be negative"       )
    elif conductivity == 0:
        return (
            "conductivity",
            mobility * electron_conc * ELECTRON_CHARGE,
        )
    elif electron_conc == 0:
        return (
            "electron_conc",
            conductivity / (mobility * ELECTRON_CHARGE),
        )
    else:
        return (
            "mobility",
            conductivity / (electron_conc * ELECTRON_CHARGE),
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


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


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       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=__snake_case					,       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=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d