





'''simple docstring'''

class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A=None	)		->       str:
        _SCREAMING_SNAKE_CASE     =      data
        _SCREAMING_SNAKE_CASE     =      None





    def __repr__(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      """"""
        _SCREAMING_SNAKE_CASE     =      self
        while temp:
            string_rep += f'<{temp.data}> ---> '
            _SCREAMING_SNAKE_CASE     =      temp.next
        string_rep += "<END>"
        return string_rep





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""   )

    # Set first element as Head
    _SCREAMING_SNAKE_CASE     =      Node(elements_list[0]   )
    _SCREAMING_SNAKE_CASE     =      head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        _SCREAMING_SNAKE_CASE     =      Node(__lowerCamelCase   )
        _SCREAMING_SNAKE_CASE     =      current.next
    return head


lowercase_         =			[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print("""Creating Linked List from List.""")
lowercase_         =			make_linked_list(list_data)
print("""Linked List:""")






print(linked_list)










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_         =			logging.get_logger(__name__)

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


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



