
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       List[Any]     						=       logging.get_logger(__name__)

a		:       Dict     						=       {
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}







class     lowercase(UpperCAmelCase__       ):
     __snake_case:	Optional[Any]						=     '''lilt'''



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=3_0_5_2_2     ,				__SCREAMING_SNAKE_CASE=7_6_8     ,				__SCREAMING_SNAKE_CASE=1_2     ,				__SCREAMING_SNAKE_CASE=1_2     ,				__SCREAMING_SNAKE_CASE=3_0_7_2     ,				__SCREAMING_SNAKE_CASE="gelu"     ,				__SCREAMING_SNAKE_CASE=0.1     ,				__SCREAMING_SNAKE_CASE=0.1     ,				__SCREAMING_SNAKE_CASE=5_1_2     ,				__SCREAMING_SNAKE_CASE=2     ,				__SCREAMING_SNAKE_CASE=0.02     ,				__SCREAMING_SNAKE_CASE=1e-12     ,				__SCREAMING_SNAKE_CASE=0     ,				__SCREAMING_SNAKE_CASE="absolute"     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=4     ,				__SCREAMING_SNAKE_CASE=1_0_2_4     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Optional[Any]:







         """simple docstring"""



         super().__init__(pad_token_id=__lowerCAmelCase     ,				**__lowerCAmelCase   )

         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__		=			type_vocab_size
         a__		=			initializer_range
         a__		=			layer_norm_eps
         a__		=			position_embedding_type
         a__		=			classifier_dropout
         a__		=			channel_shrink_ratio
         a__		=			max_ad_position_embeddings








'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   [0] * len(a							)
						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(a							)							):
												if indegree[i] == 0:
																		queue.append(a							)

						while queue:
												__snake_case						 =   queue.pop(0							)
												cnt += 1
												topo.append(a							)
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(a							)

						if cnt != len(a							):
												print('Cycle exists'							)
						else:
												print(a							)


# Adjacency List of Graph
_lowercase  =			{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)

