


import doctest
from collections import deque

import numpy as np







class      lowerCAmelCase__       :




     '''simple docstring'''


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


     def  _lowerCamelCase (						self)			->				list[float]:
            _A      :       int				    = len(self.first_signal)
            _A      :       List[str]				    = len(self.second_signal)

            _A      :       List[str]				    = max(__lowerCamelCase ,						__lowerCamelCase)

            # create a zero matrix of max_length x max_length
            _A      :       Dict				    = [[0] * max_length for i in range(__lowerCamelCase)]

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

        # multiply the matrix with the first signal
            _A      :       Dict				    = np.matmul(np.transpose(__lowerCamelCase) ,						np.transpose(self.first_signal))

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


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

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





