


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a			:			Dict												=					{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			List[str]												=					[
	    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ViTMAEForPreTraining''',
	    '''ViTMAELayer''',
	    '''ViTMAEModel''',
	    '''ViTMAEPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''TFViTMAEForPreTraining''',
	    '''TFViTMAEModel''',
	    '''TFViTMAEPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_vit_mae import (
		    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ViTMAEForPreTraining,
		    ViTMAELayer,
		    ViTMAEModel,
		    ViTMAEPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
	import sys

	a			:			Any												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






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







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(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(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

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

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


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


