

'''simple docstring'''







import doctest
from collections import deque

import numpy as np


class     A__					:



    def __init__(    self				)  ->					None:





           '''simple docstring'''
           A_		    =					[2, 1, 2, -1]
           A_		    =					[1, 2, 3, 4]







    def     snake_case_	(    self				)  ->					list[float]:





           '''simple docstring'''
           A_		    =					len(self.first_signal				)
           A_		    =					len(self.second_signal				)

           A_		    =					max(UpperCAmelCase_						, UpperCAmelCase_				)

           # create a zero matrix of max_length x max_length
           A_		    =					[[0] * max_length for i in range(UpperCAmelCase_				)]

           # 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(UpperCAmelCase_				):
                  A_		    =					deque(self.second_signal				)
                  rotated_signal.rotate(UpperCAmelCase_				)
                  for j, item in enumerate(UpperCAmelCase_				):
                         matrix[i][j] += item

                          # multiply the matrix with the first signal
           A_		    =					np.matmul(np.transpose(UpperCAmelCase_				)						, np.transpose(self.first_signal				)				)

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


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







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

