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						)






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





