
# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

UpperCamelCase_						:     Optional[int]	       =							TypeVar("""T""")




class   __lowercase						(						Generic[T]       ):






	def __init__(self				: str      ,	snake_case				: bool = True    ) ->			None:
							_lowercase       :	dict[T, list[T]]           =   {}  # dictionary of lists
							_lowercase       :	Optional[Any]           =   directed






	def       _a(self				: Union[str, Any]      ,	snake_case				: T      ,	snake_case				: T    ) ->			GraphAdjacencyList[T]:

							if not self.directed:  # For undirected graphs
													# if both source vertex and destination vertex are both present in the
													# adjacency list, add destination vertex to source vertex list of adjacent
													# vertices and add source vertex to destination vertex list of adjacent
													# vertices.
													if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(snake_case    )
																			self.adj_list[destination_vertex].append(snake_case    )
													# if only source vertex is present in adjacency list, add destination vertex
													# to source vertex list of adjacent vertices, then create a new vertex with
													# destination vertex as key and assign a list containing the source vertex
													# as it's first adjacent vertex.
													elif source_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(snake_case    )
																			_lowercase       :	int           =   [source_vertex]
													# if only destination vertex is present in adjacency list, add source vertex
													# to destination vertex list of adjacent vertices, then create a new vertex
													# with source vertex as key and assign a list containing the source vertex
													# as it's first adjacent vertex.
													elif destination_vertex in self.adj_list:
																			self.adj_list[destination_vertex].append(snake_case    )
																			_lowercase       :	Tuple           =   [destination_vertex]
													# if both source vertex and destination vertex are not present in adjacency
													# list, create a new vertex with source vertex as key and assign a list
													# containing the destination vertex as it's first adjacent vertex also
													# create a new vertex with destination vertex as key and assign a list
													# containing the source vertex as it's first adjacent vertex.
													else:
																			_lowercase       :	Optional[int]           =   [destination_vertex]
																			_lowercase       :	Optional[int]           =   [source_vertex]
							else:  # For directed graphs
													# if both source vertex and destination vertex are present in adjacency
													# list, add destination vertex to source vertex list of adjacent vertices.
													if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(snake_case    )
													# if only source vertex is present in adjacency list, add destination
													# vertex to source vertex list of adjacent vertices and create a new vertex
													# with destination vertex as key, which has no adjacent vertex
													elif source_vertex in self.adj_list:
																			self.adj_list[source_vertex].append(snake_case    )
																			_lowercase       :	List[str]           =   []
													# if only destination vertex is present in adjacency list, create a new
													# vertex with source vertex as key and assign a list containing destination
													# vertex as first adjacent vertex
													elif destination_vertex in self.adj_list:
																			_lowercase       :	str           =   [destination_vertex]
													# if both source vertex and destination vertex are not present in adjacency
													# list, create a new vertex with source vertex as key and a list containing
													# destination vertex as it's first adjacent vertex. Then create a new vertex
													# with destination vertex as key, which has no adjacent vertex
													else:
																			_lowercase       :	Tuple           =   [destination_vertex]
																			_lowercase       :	str           =   []

							return self






	def __repr__(self				: List[str]    ) ->			str:
							return pformat(self.adj_list    )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




