



"""simple docstring"""






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


lowerCAmelCase__              =							'''sshleifer/bart-tiny-random'''
lowerCAmelCase__              =							'''patrickvonplaten/t5-tiny-random'''






@require_torch
class 			__snake_case			(      unittest.TestCase):




  @cached_property
  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         return AutoConfig.from_pretrained(__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






         _lowerCamelCase     ,       *_lowerCamelCase		:							Optional[Any]            =					create_student_by_copying_alternating_layers(__lowerCAmelCase		,   tempfile.mkdtemp()		,   e=1		,   d=1						)
         self.assertEqual(student.config.num_hidden_layers		,   1						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase     ,       *_lowerCamelCase		:							Union[str, Any]            =					create_student_by_copying_alternating_layers(__lowerCAmelCase		,   tempfile.mkdtemp()		,   e=1		,   d=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






         _lowerCamelCase     ,       *_lowerCamelCase		:							Dict            =					create_student_by_copying_alternating_layers(__lowerCAmelCase		,   tempfile.mkdtemp()		,   e=1		,   d=__lowerCAmelCase						)
         self.assertEqual(student.config.encoder_layers		,   1						)
         self.assertEqual(student.config.decoder_layers		,   self.teacher_config.encoder_layers						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase     ,       *_lowerCamelCase		:							str            =					create_student_by_copying_alternating_layers(__lowerCAmelCase		,   tempfile.mkdtemp()		,   e=1		,   d=1						)
         self.assertEqual(student.config.encoder_layers		,   1						)
         self.assertEqual(student.config.decoder_layers		,   1						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         with self.assertRaises(__lowerCAmelCase						):
                create_student_by_copying_alternating_layers(__lowerCAmelCase		,   tempfile.mkdtemp()		,   e=__lowerCAmelCase		,   d=__lowerCAmelCase						)




"""simple docstring"""




import doctest
from collections import deque

import numpy as np





class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

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

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

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


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