
import doctest
from collections import deque

import numpy as np




class        _SCREAMING_SNAKE_CASE	:



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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			list[float]:
					lowerCamelCase_         =     len(self.first_signal					)
					lowerCamelCase_         =     len(self.second_signal					)

					lowerCamelCase_         =     max(lowercase		,    lowercase					)

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

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

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

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


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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


