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


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """qdqbert"""




 def __init__(     self   :    Any     ,       lowercase__   :    Any=30_522     ,       lowercase__   :    Dict=768     ,       lowercase__   :    str=12     ,       lowercase__   :    Dict=12     ,       lowercase__   :    Tuple=3_072     ,       lowercase__   :    int="gelu"     ,       lowercase__   :    List[str]=0.1     ,       lowercase__   :    Tuple=0.1     ,       lowercase__   :    Optional[Any]=512     ,       lowercase__   :    Any=2     ,       lowercase__   :    Optional[int]=0.02     ,       lowercase__   :    str=1e-12     ,       lowercase__   :    Union[str, Any]=True     ,       lowercase__   :    Optional[int]=1     ,       lowercase__   :    Optional[Any]=0     ,       lowercase__   :    Any=2     ,       **lowercase__   :    str     ,       ):







        '''simple docstring'''



        super().__init__(pad_token_id=_lowerCamelCase     ,       bos_token_id=_lowerCamelCase     ,       eos_token_id=_lowerCamelCase     ,       **_lowerCamelCase)

        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   max_position_embeddings
        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   num_hidden_layers
        lowerCAmelCase__	      =   num_attention_heads
        lowerCAmelCase__	      =   intermediate_size
        lowerCAmelCase__	      =   hidden_act
        lowerCAmelCase__	      =   hidden_dropout_prob
        lowerCAmelCase__	      =   attention_probs_dropout_prob
        lowerCAmelCase__	      =   initializer_range
        lowerCAmelCase__	      =   type_vocab_size
        lowerCAmelCase__	      =   layer_norm_eps
        lowerCAmelCase__	      =   use_cache


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():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




