





import logging

from transformers import PretrainedConfig


__lowerCamelCase	:					Dict			  =					logging.getLogger(__name__)


__lowerCamelCase	:					Dict			  =					{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :str						   =       'bertabs'





      def __init__(			self	,							A_=3_0522	,							A_=512	,							A_=6	,							A_=512	,							A_=8	,							A_=512	,							A_=0.2	,							A_=6	,							A_=768	,							A_=8	,							A_=2048	,							A_=0.2	,							**A_	,							):
       '''simple docstring'''




       super().__init__(**A_       )

       UpperCamelCase     : Dict	     =       vocab_size
       UpperCamelCase     : Dict	     =       max_pos

       UpperCamelCase     : str	     =       enc_layers
       UpperCamelCase     : List[Any]	     =       enc_hidden_size
       UpperCamelCase     : Tuple	     =       enc_heads
       UpperCamelCase     : Dict	     =       enc_ff_size
       UpperCamelCase     : int	     =       enc_dropout

       UpperCamelCase     : Tuple	     =       dec_layers
       UpperCamelCase     : Any	     =       dec_hidden_size
       UpperCamelCase     : Optional[int]	     =       dec_heads
       UpperCamelCase     : List[str]	     =       dec_ff_size
       UpperCamelCase     : Optional[int]	     =       dec_dropout







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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
