

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'M-CLIP'


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





        '''simple docstring'''







        A__          =   transformerDimSize
        A__          =   imageDimSize
        super().__init__(**lowercase_		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							MCLIPConfig


    def __init__(      self :       List[str],lowercase_ :       Dict,*lowercase_ :       Optional[int],**lowercase_ :       str		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       int,lowercase_ :       int,lowercase_ :       List[Any]		)->    Tuple:





        '''simple docstring'''







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


'''simple docstring'''

import doctest
from collections import deque

import numpy as np



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    str  =					[2, 1, 2, -1]
        lowercase__				:    str  =					[1, 2, 3, 4]


       def _lowerCAmelCase(						self )  ->  list[float]:
        lowercase__				:    Optional[Any]  =					len(self.first_signal )
        lowercase__				:    Union[str, Any]  =					len(self.second_signal )

        lowercase__				:    int  =					max(__lowerCAmelCase	,   __lowerCAmelCase )

        # create a zero matrix of max_length x max_length
        lowercase__				:    List[str]  =					[[0] * max_length for i in range(__lowerCAmelCase )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__lowerCAmelCase ):
         lowercase__				:    int  =					deque(self.second_signal )
         rotated_signal.rotate(__lowerCAmelCase )
         for j, item in enumerate(__lowerCAmelCase ):
          matrix[i][j] += item

        # multiply the matrix with the first signal
        lowercase__				:    Optional[int]  =					np.matmul(np.transpose(__lowerCAmelCase )	,   np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__lowerCAmelCase	,   2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()

