"""simple docstring"""


from collections import namedtuple

lowerCAmelCase_:					List[str]     =					namedtuple("from_to", "from_ to")

lowerCAmelCase_:					int     =					{
    "cubicmeter": from_to(1, 1),
    "litre": from_to(0.001, 1_0_0_0),
    "kilolitre": from_to(1, 1),
    "gallon": from_to(0.00_454, 264.172),
    "cubicyard": from_to(0.76_455, 1.30_795),
    "cubicfoot": from_to(0.028, 35.3_147),
    "cup": from_to(0.000_236_588, 4_2_2_6.7_5),
}







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     if from_type not in METRIC_CONVERSION:
          raise ValueError(
              f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
              + ", ".join(A      )      )
     if to_type not in METRIC_CONVERSION:
          raise ValueError(
              f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
              + ", ".join(A      )      )
     return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()





"""simple docstring"""


import os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




