




"""simple docstring"""






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   a__			(       unittest.TestCase  ):





						@slow
						def       __UpperCamelCase     (							self      :	int)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''')

											_lowerCAmelCase:Union[str, Any]									=  tf.convert_to_tensor(
											    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]     ,dtype=tf.intaa     ,)  # J'aime le camembert !"

											_lowerCAmelCase:Tuple									=  model(a__)['''last_hidden_state''']
											_lowerCAmelCase:List[Any]									=  tf.TensorShape((1, 10, 768))
											self.assertEqual(output.shape     ,a__)
											# compare the actual values for a slice.
											_lowerCAmelCase:int									=  tf.convert_to_tensor(
											    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]     ,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))












"""simple docstring"""






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


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''roc_bert'''





						def __init__(							self      :	Union[str, Any]     ,a__      :	Union[str, Any]=3_0522     ,a__      :	List[Any]=768     ,a__      :	Tuple=12     ,a__      :	Optional[int]=12     ,a__      :	Any=3072     ,a__      :	Optional[int]="gelu"     ,a__      :	Union[str, Any]=0.1     ,a__      :	List[str]=0.1     ,a__      :	Dict=512     ,a__      :	int=2     ,a__      :	Dict=0.02     ,a__      :	Dict=1E-12     ,a__      :	int=True     ,a__      :	Optional[int]=0     ,a__      :	Union[str, Any]="absolute"     ,a__      :	List[Any]=None     ,a__      :	str=True     ,a__      :	str=True     ,a__      :	List[str]=768     ,a__      :	Optional[int]=910     ,a__      :	Any=512     ,a__      :	str=2_4858     ,a__      :	List[str]=True     ,**a__      :	str     ,)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  vocab_size
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  intermediate_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_act
											_lowerCAmelCase:Any									=  hidden_dropout_prob
											_lowerCAmelCase:List[Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:List[Any]									=  initializer_range
											_lowerCAmelCase:Dict									=  type_vocab_size
											_lowerCAmelCase:Dict									=  layer_norm_eps
											_lowerCAmelCase:str									=  use_cache
											_lowerCAmelCase:Any									=  enable_pronunciation
											_lowerCAmelCase:List[str]									=  enable_shape
											_lowerCAmelCase:Optional[int]									=  pronunciation_embed_dim
											_lowerCAmelCase:Union[str, Any]									=  pronunciation_vocab_size
											_lowerCAmelCase:str									=  shape_embed_dim
											_lowerCAmelCase:List[Any]									=  shape_vocab_size
											_lowerCAmelCase:str									=  concat_input
											_lowerCAmelCase:Optional[int]									=  position_embedding_type
											_lowerCAmelCase:Any									=  classifier_dropout
											super().__init__(pad_token_id=a__     ,**a__)






