
"""simple docstring"""





# 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

__magic_name__       =			TypeVar("T")
class 						SCREAMING_SNAKE_CASE_ (						Generic[T] ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__ = True):
				__SCREAMING_SNAKE_CASE            = {}  # dictionary of lists
				__SCREAMING_SNAKE_CASE            = directed




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):

				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(lowerCAmelCase__)
								self.adj_list[destination_vertex].append(lowerCAmelCase__)
						# 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(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = [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(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = [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:
								__SCREAMING_SNAKE_CASE            = [destination_vertex]
								__SCREAMING_SNAKE_CASE            = [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(lowerCAmelCase__)
						# 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(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = []
						# 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:
								__SCREAMING_SNAKE_CASE            = [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:
								__SCREAMING_SNAKE_CASE            = [destination_vertex]
								__SCREAMING_SNAKE_CASE            = []

				return self




		def __repr__(     self):
				return pformat(self.adj_list)








from typing import TYPE_CHECKING

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


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

