
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase			=		{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTJForCausalLM""",
							    """GPTJForQuestionAnswering""",
							    """GPTJForSequenceClassification""",
							    """GPTJModel""",
							    """GPTJPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """TFGPTJForCausalLM""",
							    """TFGPTJForQuestionAnswering""",
							    """TFGPTJForSequenceClassification""",
							    """TFGPTJModel""",
							    """TFGPTJPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """FlaxGPTJForCausalLM""",
							    """FlaxGPTJModel""",
							    """FlaxGPTJPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							lowerCAmelCase			=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""


# 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      lowerCAmelCase_     (	snake_case_	:    List[Any]    )     ->Optional[int]:
			lowerCamelCase__			:  Optional[int]  =Queue()
			lowerCamelCase__			:  Any  =[False] * len(snake_case_    )
			lowerCamelCase__			:  Optional[Any]  =[-1] * len(snake_case_    )

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

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

												if color[neighbour] == -1:
															lowerCamelCase__			:  Union[str, Any]  =1 - color[u]
															queue.put(snake_case_    )

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

						return True

			for i in range(len(snake_case_    )    ):
						if not visited[i]:
									queue.put(snake_case_    )
									lowerCamelCase__			:  Tuple  =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]}))