




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_					    = logging.get_logger(__name__)

lowerCAmelCase_					    = {
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		'ibert'

      def __init__(   self      ,				__UpperCAmelCase=3_0522      ,				__UpperCAmelCase=768      ,				__UpperCAmelCase=12      ,				__UpperCAmelCase=12      ,				__UpperCAmelCase=3072      ,				__UpperCAmelCase="gelu"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=512      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=0.02      ,				__UpperCAmelCase=1e-12      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase="absolute"      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase="none"      ,				**__UpperCAmelCase      ,				):
        super().__init__(pad_token_id=__UpperCAmelCase      ,				bos_token_id=__UpperCAmelCase      ,				eos_token_id=__UpperCAmelCase      ,				**__UpperCAmelCase		)

        lowerCAmelCase__				:  Union[str, Any]		       =     vocab_size
        lowerCAmelCase__				:  List[str]		       =     hidden_size
        lowerCAmelCase__				:  Optional[int]		       =     num_hidden_layers
        lowerCAmelCase__				:  Optional[Any]		       =     num_attention_heads
        lowerCAmelCase__				:  int		       =     hidden_act
        lowerCAmelCase__				:  Dict		       =     intermediate_size
        lowerCAmelCase__				:  Optional[Any]		       =     hidden_dropout_prob
        lowerCAmelCase__				:  List[Any]		       =     attention_probs_dropout_prob
        lowerCAmelCase__				:  List[Any]		       =     max_position_embeddings
        lowerCAmelCase__				:  Any		       =     type_vocab_size
        lowerCAmelCase__				:  Union[str, Any]		       =     initializer_range
        lowerCAmelCase__				:  str		       =     layer_norm_eps
        lowerCAmelCase__				:  Union[str, Any]		       =     position_embedding_type
        lowerCAmelCase__				:  int		       =     quant_mode
        lowerCAmelCase__				:  Union[str, Any]		       =     force_dequant






class  _lowerCAmelCase     (  _lowercase	):

      @property
      def 	__magic_name__(   self		):
        if self.task == "multiple-choice":
          lowerCAmelCase__				:  Any		       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
          lowerCAmelCase__				:  Union[str, Any]		       =     {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]		)







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  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

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

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # 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		)		)

