
"""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 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				@slow
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       List[str]           =   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(SCREAMING_SNAKE_CASE__   ,       labels=SCREAMING_SNAKE_CASE__    ).loss
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   -tf.math.reduce_mean(SCREAMING_SNAKE_CASE__    ).numpy()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   -21.228168
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 2E-4    )








from typing import TYPE_CHECKING

from ...utils import _LazyModule


_A		       =						{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
   from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





