





from ....configuration_utils import PretrainedConfig
from ....utils import logging


A_		:		List[str]		     =			logging.get_logger(__name__)

# TODO: upload to AWS
A_		:		List[Any]		     =			{
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[int]									='''retribert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1_2_8  ,      _lowerCamelCase=0  ,      **_lowerCamelCase  ,      ):
         super().__init__(pad_token_id=_lowerCamelCase  ,      **_lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    vocab_size
         UpperCamelCase_: Any   				=    hidden_size
         UpperCamelCase_: Dict   				=    num_hidden_layers
         UpperCamelCase_: Tuple   				=    num_attention_heads
         UpperCamelCase_: List[str]   				=    hidden_act
         UpperCamelCase_: str   				=    intermediate_size
         UpperCamelCase_: Optional[int]   				=    hidden_dropout_prob
         UpperCamelCase_: Optional[int]   				=    attention_probs_dropout_prob
         UpperCamelCase_: Optional[Any]   				=    max_position_embeddings
         UpperCamelCase_: Dict   				=    type_vocab_size
         UpperCamelCase_: Optional[Any]   				=    initializer_range
         UpperCamelCase_: Union[str, Any]   				=    layer_norm_eps
         UpperCamelCase_: Optional[Any]   				=    share_encoders
         UpperCamelCase_: str   				=    projection_dim







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)