import doctest
from collections import deque

import numpy as np





class       lowercase__		:


						def __init__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[2, 1, 2, -1]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[1, 2, 3, 4]



						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							len(self.first_signal		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(self.second_signal		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							max(UpperCamelCase__					,	UpperCamelCase__		)

												# create a zero matrix of max_length x max_length
												SCREAMING_SNAKE_CASE							:					Dict           =							[[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__		):
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							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
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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()






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





