"""simple docstring"""





from math import pow, sqrt



def      lowerCamelCase_(*__SCREAMING_SNAKE_CASE  )->    bool:
							_SCREAMING_SNAKE_CASE		:						str			  =    len(__SCREAMING_SNAKE_CASE  ) > 0 and all(value > 0.0 for value in values  )
							return result



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float | ValueError:
							return (
							    round(sqrt(molar_mass_a / molar_mass_a  )     ,	6  )
							    if validate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							    else ValueError("""Input Error: Molar mass values must greater than 0."""  )
							)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float | ValueError:
							return (
							    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )     ,	6  )
							    if validate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							    else ValueError(
							        """Input Error: Molar mass and effusion rate values must greater than 0."""  )
							)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float | ValueError:
							return (
							    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )     ,	6  )
							    if validate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							    else ValueError(
							        """Input Error: Molar mass and effusion rate values must greater than 0."""  )
							)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float | ValueError:
							return (
							    round(molar_mass / pow(effusion_rate_a / effusion_rate_a     ,	2  )     ,	6  )
							    if validate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							    else ValueError(
							        """Input Error: Molar mass and effusion rate values must greater than 0."""  )
							)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float | ValueError:
							return (
							    round(pow(effusion_rate_a / effusion_rate_a     ,	2  ) / molar_mass     ,	6  )
							    if validate(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							    else ValueError(
							        """Input Error: Molar mass and effusion rate values must greater than 0."""  )
							)



"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs


