


import random

def       lowercase_		(		_A			:    int    ,					_A			:    float    ,					_A			:    bool = False						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  {i: [] for i in range(_snake_case						)}

      # if probability is greater or equal than 1, then generate a complete graph
      if probability >= 1:
            return complete_graph(_snake_case						)
      # 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(_snake_case						):
            for j in range(i + 1    ,					_snake_case						):
                  if random.random() < probability:
                        graph[i].append(_snake_case						)
                        if not directed:
                              # if the graph is undirected, add an edge in from j to i, either
                              graph[j].append(_snake_case						)
      return graph

def       lowercase_		(		_A			:    int						):

      """simple docstring"""




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


if __name__ == "__main__":
    import doctest

    doctest.testmod()





from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






