



import doctest
from collections import deque

import numpy as np






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self		):
        A			:	str										=       [2, 1, 2, -1]
        A			:	int										=       [1, 2, 3, 4]






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       len(self.first_signal		)
        A			:	int										=       len(self.second_signal		)

        A			:	str										=       max(lowerCamelCase__,		lowerCamelCase__		)

        # create a zero matrix of max_length x max_length
        A			:	int										=       [[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__		):
            A			:	Tuple										=       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
        A			:	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()







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


