






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 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@slow
					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

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

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

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

												__lowercase					:							List[str]			=   -21.228168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 2E-4							)
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





