def       A	(    _lowercase						):
						if not isinstance(_lowercase		,  _lowercase						):
												raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
						if len(_lowercase						) == 0:
												raise ValueError('''Input list must be a non empty list'''						)
						if len(_lowercase						) == 1:
												return True
						SCREAMING_SNAKE_CASE							:    Any							=							series[1] - series[0]
						for index in range(len(_lowercase						) - 1						):
												if series[index + 1] - series[index] != common_diff:
																		return False
						return True
def       A	(    _lowercase						):
						if not isinstance(_lowercase		,  _lowercase						):
												raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
						if len(_lowercase						) == 0:
												raise ValueError('''Input list must be a non empty list'''						)
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						for val in series:
												answer += val
						return answer / len(_lowercase						)


if __name__ == "__main__":
					import doctest

					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						)





