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


lowercase_   				=       logging.get_logger(__name__)

lowercase_   				=       {
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase  ):
       A				:     str					    =     "splinter"

       def __init__(				self :			Tuple       ,       _lowerCAmelCase :			Optional[Any]=3_05_22       ,       _lowerCAmelCase :			List[str]=7_68       ,       _lowerCAmelCase :			Optional[int]=12       ,       _lowerCAmelCase :			Optional[int]=12       ,       _lowerCAmelCase :			Union[str, Any]=30_72       ,       _lowerCAmelCase :			Optional[int]="gelu"       ,       _lowerCAmelCase :			Dict=0.1       ,       _lowerCAmelCase :			List[str]=0.1       ,       _lowerCAmelCase :			Dict=5_12       ,       _lowerCAmelCase :			Tuple=2       ,       _lowerCAmelCase :			List[Any]=0.02       ,       _lowerCAmelCase :			str=1e-12       ,       _lowerCAmelCase :			Any=True       ,       _lowerCAmelCase :			int=0       ,       _lowerCAmelCase :			Union[str, Any]=1_04       ,       **_lowerCAmelCase :			int       ,       ):
           super().__init__(pad_token_id=_lowerCAmelCase       ,       **_lowerCAmelCase       )

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







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        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.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]]]						,      device=a						,      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]						,      a						,      atol=1e-4				)				)

