





# 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

SCREAMING_SNAKE_CASE_						   =       TypeVar("""T""")

class 		UpperCamelCase__ (      Generic[T]   ):



     '''simple docstring'''

     def __init__(							self						:    Optional[Any]						,lowerCamelCase__						:    bool = True       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}  # dictionary of lists
           SCREAMING_SNAKE_CASE		=						directed






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    T						,lowerCamelCase__						:    T       )     ->		GraphAdjacencyList[T]:




           '''simple docstring'''


           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						:    Tuple       )     ->		str:




           '''simple docstring'''


           return pformat(self.adj_list       )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

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


