





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

lowerCamelCase_	:		Tuple         =   {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  """rembert"""

      def __init__(	self   , snake_case_=2_5_0_3_0_0   , snake_case_=1_1_5_2   , snake_case_=3_2   , snake_case_=1_8   , snake_case_=2_5_6   , snake_case_=1_6_6_4   , snake_case_=4_6_0_8   , snake_case_="gelu"   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=0.1   , snake_case_=5_1_2   , snake_case_=2   , snake_case_=0.02   , snake_case_=1E-12   , snake_case_=True   , snake_case_=0   , snake_case_=3_1_2   , snake_case_=3_1_3   , **snake_case_   , ):

        """simple docstring"""

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

        A_    :			List[str]									=    vocab_size
        A_    :			List[Any]									=    input_embedding_size
        A_    :			List[str]									=    output_embedding_size
        A_    :			Optional[Any]									=    max_position_embeddings
        A_    :			str									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			List[Any]									=    num_attention_heads
        A_    :			Dict									=    intermediate_size
        A_    :			Any									=    hidden_act
        A_    :			Any									=    hidden_dropout_prob
        A_    :			Optional[int]									=    attention_probs_dropout_prob
        A_    :			List[Any]									=    classifier_dropout_prob
        A_    :			List[str]									=    initializer_range
        A_    :			str									=    type_vocab_size
        A_    :			Union[str, Any]									=    layer_norm_eps
        A_    :			Union[str, Any]									=    use_cache
        A_    :			List[Any]									=    False


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_    :			Dict									=    {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_    :			str									=    {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1E-4





'''simple docstring'''







def 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




