
lowerCamelCase      =frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

lowerCamelCase      =frozenset(["prompt", "negative_prompt"])

lowerCamelCase      =frozenset([])

lowerCamelCase      =frozenset(["image"])

lowerCamelCase      =frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

lowerCamelCase      =frozenset(["image"])

lowerCamelCase      =frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

lowerCamelCase      =frozenset(["prompt", "image", "negative_prompt"])

lowerCamelCase      =frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

lowerCamelCase      =frozenset(["prompt", "image", "mask_image", "negative_prompt"])

lowerCamelCase      =frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

lowerCamelCase      =frozenset(["image", "mask_image"])

lowerCamelCase      =frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

lowerCamelCase      =frozenset(["example_image", "image", "mask_image"])

lowerCamelCase      =frozenset(["class_labels"])

lowerCamelCase      =frozenset(["class_labels"])

lowerCamelCase      =frozenset(["batch_size"])

lowerCamelCase      =frozenset([])

lowerCamelCase      =frozenset(["batch_size"])

lowerCamelCase      =frozenset([])

lowerCamelCase      =frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

lowerCamelCase      =frozenset(["prompt", "negative_prompt"])
lowerCamelCase      =frozenset(["input_tokens"])

lowerCamelCase      =frozenset(["input_tokens"])




import doctest
from collections import deque

import numpy as np




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				[2, 1, 2, -1]
           UpperCamelCase__    :   Union[str, Any]					=				[1, 2, 3, 4]






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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				len(self.first_signal  )
           UpperCamelCase__    :   Any					=				len(self.second_signal  )

           UpperCamelCase__    :   Dict					=				max(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           # create a zero matrix of max_length x max_length
           UpperCamelCase__    :   List[Any]					=				[[0] * max_length for i in range(__SCREAMING_SNAKE_CASE  )]

           # 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(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Optional[int]					=				deque(self.second_signal  )
                 rotated_signal.rotate(__SCREAMING_SNAKE_CASE  )
                 for j, item in enumerate(__SCREAMING_SNAKE_CASE  ):
                       matrix[i][j] += item

        # multiply the matrix with the first signal
           UpperCamelCase__    :   Optional[Any]					=				np.matmul(np.transpose(__SCREAMING_SNAKE_CASE  )						,						np.transpose(self.first_signal  )  )

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


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


