


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     UpperCamelCase(  unittest.TestCase   ):


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"						)
											__snake_case				=		AutoTokenizer.from_pretrained("google/mt5-small"						)

											__snake_case				=		tokenizer("Hello there"     ,    return_tensors="tf"						).input_ids
											__snake_case				=		tokenizer("Hi I am"     ,    return_tensors="tf"						).input_ids

											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						).loss
											__snake_case				=		-tf.math.reduce_mean(SCREAMING_SNAKE_CASE						).numpy()

											__snake_case				=		-21.228168
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2e-4						)





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A							:					Any     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	None:




											'''simple docstring'''

											warnings.warn(
											    "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
											    " use BeitImageProcessor instead."     ,    SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)





