

import random






def        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  float       ,     _snake_case   :  bool = False	):




			"""simple docstring"""






			__a        ={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        UpperCamelCase_(							_snake_case   :  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






