import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[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      ):
       SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      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()

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
