


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


A   :					Tuple	=      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     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "retribert"





       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple=30522      ,   __lowerCamelCase							:    List[Any]=768      ,   __lowerCamelCase							:    Dict=8      ,   __lowerCamelCase							:    Dict=12      ,   __lowerCamelCase							:    str=3072      ,   __lowerCamelCase							:    Optional[int]="gelu"      ,   __lowerCamelCase							:    Tuple=0.1      ,   __lowerCamelCase							:    Tuple=0.1      ,   __lowerCamelCase							:    Dict=512      ,   __lowerCamelCase							:    Dict=2      ,   __lowerCamelCase							:    int=0.0_2      ,   __lowerCamelCase							:    Tuple=1E-1_2      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple=128      ,   __lowerCamelCase							:    Optional[int]=0      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

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






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 numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,   dtype=tf.intaa      ,   )  # J'aime le camembert !"

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   dtype=tf.floataa      ,   )
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1E-4 ) )


