'''simple docstring'''




import random

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__ = False						)					->       dict:





       """simple docstring"""


       __UpperCAmelCase  :				dict       				=		{i: [] for i in range(lowerCamelCase__						)}

       # if probability is greater or equal than 1, then generate a complete graph
       if probability >= 1:
              return complete_graph(lowerCamelCase__						)
       # if probability is lower or equal than 0, then return a graph without edges
       if probability <= 0:
              return graph

       # for each couple of nodes, add an edge from u to v
       # if the number randomly generated is greater than probability probability
       for i in range(lowerCamelCase__						):
              for j in range(i + 1							,    lowerCamelCase__						):
                     if random.random() < probability:
                            graph[i].append(lowerCamelCase__						)
                            if not directed:
                                   # if the graph is undirected, add an edge in from j to i, either
                                   graph[j].append(lowerCamelCase__						)
       return graph

def _lowercase	(      lowerCamelCase__						)					->       dict:





       """simple docstring"""


       return {
           i: [j for j in range(lowerCamelCase__						) if i != j] for i in range(lowerCamelCase__						)
       }


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


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

_a   :	Any 							=   {
    "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 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					"ibert"






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     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_      )

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






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              if self.task == "multiple-choice":
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]      )






