
'''simple docstring'''





from math import pow, sqrt

def      A (       *A_							:					float       ):
						snake_case       :     Union[str, Any] =    len(A_       ) > 0 and all(value > 0.0 for value in values       )
						return result

def      A (       A_							:					float    ,							A_							:					float       ):
						return (
						    round(sqrt(molar_mass_a / molar_mass_a       )    ,							6       )
						    if validate(A_    ,							A_       )
						    else ValueError('''Input Error: Molar mass values must greater than 0.'''       )
						)

def      A (       A_							:					float    ,							A_							:					float    ,							A_							:					float       ):
						return (
						    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a       )    ,							6       )
						    if validate(A_    ,							A_    ,							A_       )
						    else ValueError(
						        '''Input Error: Molar mass and effusion rate values must greater than 0.'''       )
						)

def      A (       A_							:					float    ,							A_							:					float    ,							A_							:					float       ):
						return (
						    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a       )    ,							6       )
						    if validate(A_    ,							A_    ,							A_       )
						    else ValueError(
						        '''Input Error: Molar mass and effusion rate values must greater than 0.'''       )
						)

def      A (       A_							:					float    ,							A_							:					float    ,							A_							:					float       ):
						return (
						    round(molar_mass / pow(effusion_rate_a / effusion_rate_a    ,							2       )    ,							6       )
						    if validate(A_    ,							A_    ,							A_       )
						    else ValueError(
						        '''Input Error: Molar mass and effusion rate values must greater than 0.'''       )
						)





def      A (       A_							:					float    ,							A_							:					float    ,							A_							:					float       ):
						return (
						    round(pow(effusion_rate_a / effusion_rate_a    ,							2       ) / molar_mass    ,							6       )
						    if validate(A_    ,							A_    ,							A_       )
						    else ValueError(
						        '''Input Error: Molar mass and effusion rate values must greater than 0.'''       )
						)



'''simple docstring'''





import os
import sys

UpperCAmelCase    					=   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,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

