




def 			_a   (		SCREAMING_SNAKE_CASE_							:       float   ,   SCREAMING_SNAKE_CASE_							:       float   ,   SCREAMING_SNAKE_CASE_							:       float   ,   SCREAMING_SNAKE_CASE_							:       float   ,   SCREAMING_SNAKE_CASE_							:       float   ,   ):
					__lowerCAmelCase            = [redshift, radiation_density, matter_density, dark_energy]
					if any(p < 0 for p in parameters  ):
										raise ValueError("All input parameters must be positive"  )

					if any(p > 1 for p in parameters[1:4]  ):
										raise ValueError("Relative densities cannot be greater than one"  )
					else:
										__lowerCAmelCase            = 1 - (matter_density + radiation_density + dark_energy)

										__lowerCAmelCase            = (
										    radiation_density * (redshift + 1) ** 4
										    + matter_density * (redshift + 1) ** 3
										    + curvature * (redshift + 1) ** 2
										    + dark_energy
										)

										__lowerCAmelCase            = hubble_constant * e_a ** (1 / 2)
										return hubble


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# demo LCDM approximation
					UpperCamelCase__					    =    0.3

					print(
					    hubble_parameter(
					        hubble_constant=68.3,
					        radiation_density=1E-4,
					        matter_density=matter_density,
					        dark_energy=1 - matter_density,
					        redshift=0,
					    )
					)










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






						def __init__(     self	,    _A	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



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




