

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


_lowerCAmelCase					:			Union[str, Any]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[Any]  				=			{
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'lxmert'
			SCREAMING_SNAKE_CASE							   =						{}





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=9500					,  __snake_case=1600					,  __snake_case=400					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=9					,  __snake_case=5					,  __snake_case=5					,  __snake_case=2048					,  __snake_case=4					,  __snake_case=6.67					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =hidden_size
						__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        =num_qa_labels
						__a        =num_object_labels
						__a        =num_attr_labels
						__a        =l_layers
						__a        =x_layers
						__a        =r_layers
						__a        =visual_feat_dim
						__a        =visual_pos_dim
						__a        =visual_loss_normalizer
						__a        =task_matched
						__a        =task_mask_lm
						__a        =task_obj_predict
						__a        =task_qa
						__a        =visual_obj_loss
						__a        =visual_attr_loss
						__a        =visual_feat_loss
						__a        ={'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
						super().__init__(**__snake_case					)









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






