





import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_UpperCAmelCase							:			Union[str, Any]        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 def __init__( self    : Tuple     ,	*UpperCAmelCase    : List[str]     ,	**UpperCAmelCase    : int      )     ->      None:
     warnings.warn(
         'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use SegformerImageProcessor instead.'     ,	UpperCAmelCase     ,	)
     super().__init__(*UpperCAmelCase     ,	**UpperCAmelCase      )












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     ):
 @slow
 def 	A_      ( self    : int      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
     lowerCamelCase__       : Any   					=						AutoTokenizer.from_pretrained('google/mt5-small'      )

     lowerCamelCase__       : int   					=						tokenizer('Hello there'     ,	return_tensors='tf'      ).input_ids
     lowerCamelCase__       : int   					=						tokenizer('Hi I am'     ,	return_tensors='tf'      ).input_ids

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

     lowerCamelCase__       : Dict   					=						-2_1.2_2_8_1_6_8
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )





