
"""simple docstring"""







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 			A__	(     unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'			)
			__lowerCAmelCase      :						Any						      =				AutoTokenizer.from_pretrained('google/mt5-small'			)

			__lowerCAmelCase      :						Any						      =				tokenizer('Hello there'	,		return_tensors='tf'			).input_ids
			__lowerCAmelCase      :						str						      =				tokenizer('Hi I am'	,		return_tensors='tf'			).input_ids

			__lowerCAmelCase      :						Optional[Any]						      =				model(_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE			).loss
			__lowerCAmelCase      :						Optional[Any]						      =				-tf.math.reduce_mean(_SCREAMING_SNAKE_CASE			).numpy()

			__lowerCAmelCase      :						str						      =				-21.22_8168
			self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 2E-4			)

"""simple docstring"""







import warnings
from functools import wraps
from typing import Callable
def    __lowerCAmelCase    (_UpperCamelCase		):

	@wraps(_UpperCamelCase		)
	def _inner_fn(*_UpperCamelCase	,  **_UpperCamelCase		):
		warnings.warn(
		    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")	,  _UpperCamelCase	,  )
		return fn(*_UpperCamelCase	,  **_UpperCamelCase		)

	return _inner_fn