






# 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.
from queue import Queue
def       a__						(  A					):
			lowercase__						:	Tuple         = Queue()
			lowercase__						:	Optional[Any]         = [False] * len(A					)
			lowercase__						:	List[Any]         = [-1] * len(A					)

			def bfs():
						while not queue.empty():
									lowercase__						:	int         = queue.get()
									lowercase__						:	Optional[Any]         = True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															lowercase__						:	str         = 1 - color[u]
															queue.put(A					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(A					)					):
						if not visited[i]:
									queue.put(A					)
									lowercase__						:	int         = 0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
	# Adjacency List of graph
	print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))










from collections import OrderedDict
from typing import Mapping

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


a    =       logging.get_logger(__name__)

a    =       {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       Dict		      =		'''ernie'''



 def __init__(	self   :   Tuple ,		UpperCAmelCase__   :   int=30_522 ,		UpperCAmelCase__   :   List[str]=768 ,		UpperCAmelCase__   :   Tuple=12 ,		UpperCAmelCase__   :   Optional[int]=12 ,		UpperCAmelCase__   :   Any=3_072 ,		UpperCAmelCase__   :   Tuple="gelu" ,		UpperCAmelCase__   :   Union[str, Any]=0.1 ,		UpperCAmelCase__   :   str=0.1 ,		UpperCAmelCase__   :   List[str]=512 ,		UpperCAmelCase__   :   str=2 ,		UpperCAmelCase__   :   Optional[int]=3 ,		UpperCAmelCase__   :   Dict=False ,		UpperCAmelCase__   :   Any=0.02 ,		UpperCAmelCase__   :   Any=1e-12 ,		UpperCAmelCase__   :   str=0 ,		UpperCAmelCase__   :   Any="absolute" ,		UpperCAmelCase__   :   List[str]=True ,		UpperCAmelCase__   :   Tuple=None ,		**UpperCAmelCase__   :   Dict ,		):

    """simple docstring"""






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

    lowercase__						:	List[str]         = vocab_size
    lowercase__						:	Optional[int]         = hidden_size
    lowercase__						:	Union[str, Any]         = num_hidden_layers
    lowercase__						:	Any         = num_attention_heads
    lowercase__						:	Optional[int]         = hidden_act
    lowercase__						:	str         = intermediate_size
    lowercase__						:	Tuple         = hidden_dropout_prob
    lowercase__						:	Tuple         = attention_probs_dropout_prob
    lowercase__						:	str         = max_position_embeddings
    lowercase__						:	List[Any]         = type_vocab_size
    lowercase__						:	Any         = task_type_vocab_size
    lowercase__						:	Optional[Any]         = use_task_id
    lowercase__						:	Optional[Any]         = initializer_range
    lowercase__						:	Union[str, Any]         = layer_norm_eps
    lowercase__						:	Dict         = position_embedding_type
    lowercase__						:	Union[str, Any]         = use_cache
    lowercase__						:	int         = classifier_dropout
class        __snake_case(					__UpperCamelCase      ):



 @property
 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    if self.task == "multiple-choice":
       lowercase__						:	Dict         = {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
       lowercase__						:	Dict         = {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
            ("""token_type_ids""", dynamic_axis),
            ("""task_type_ids""", dynamic_axis),
        ]	)


