





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase			:	str					    =	logging.get_logger(__name__)



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase=False	)		->					Union[str, Any]:
 UpperCamelCase      :   List[Any]			      =  OrderedDict()
 for key, value in state_dict.items():
  if encoder_only and not key.startswith("head"	):
   UpperCamelCase      :   List[Any]			      =  "segformer.encoder." + key
  if key.startswith("backbone"	):
   UpperCamelCase      :   List[Any]			      =  key.replace("backbone"				,  "segformer.encoder"	)
  if "patch_embed" in key:
   # replace for example patch_embed1 by patch_embeddings.0
   UpperCamelCase      :   Union[str, Any]			      =  key[key.find("patch_embed"	) + len("patch_embed"	)]
   UpperCamelCase      :   List[str]			      =  key.replace(F"""patch_embed{idx}"""				,  F"""patch_embeddings.{int(_lowerCAmelCase	)-1}"""	)
  if "norm" in key:
   UpperCamelCase      :   Optional[int]			      =  key.replace("norm"				,  "layer_norm"	)
  if "segformer.encoder.layer_norm" in key:
   # replace for example layer_norm1 by layer_norm.0
   UpperCamelCase      :   Tuple			      =  key[key.find("segformer.encoder.layer_norm"	) + len("segformer.encoder.layer_norm"	)]
   UpperCamelCase      :   int			      =  key.replace(F"""layer_norm{idx}"""				,  F"""layer_norm.{int(_lowerCAmelCase	)-1}"""	)
  if "layer_norm1" in key:
   UpperCamelCase      :   List[str]			      =  key.replace("layer_norm1"				,  "layer_norm_1"	)
  if "layer_norm2" in key:
   UpperCamelCase      :   Tuple			      =  key.replace("layer_norm2"				,  "layer_norm_2"	)
  if "block" in key:
   # replace for example block1 by block.0
   UpperCamelCase      :   Optional[int]			      =  key[key.find("block"	) + len("block"	)]
   UpperCamelCase      :   Any			      =  key.replace(F"""block{idx}"""				,  F"""block.{int(_lowerCAmelCase	)-1}"""	)
  if "attn.q" in key:
   UpperCamelCase      :   Dict			      =  key.replace("attn.q"				,  "attention.self.query"	)
  if "attn.proj" in key:
   UpperCamelCase      :   Optional[int]			      =  key.replace("attn.proj"				,  "attention.output.dense"	)
  if "attn" in key:
   UpperCamelCase      :   Any			      =  key.replace("attn"				,  "attention.self"	)
  if "fc1" in key:
   UpperCamelCase      :   Union[str, Any]			      =  key.replace("fc1"				,  "dense1"	)
  if "fc2" in key:
   UpperCamelCase      :   Union[str, Any]			      =  key.replace("fc2"				,  "dense2"	)
  if "linear_pred" in key:
   UpperCamelCase      :   List[Any]			      =  key.replace("linear_pred"				,  "classifier"	)
  if "linear_fuse" in key:
   UpperCamelCase      :   int			      =  key.replace("linear_fuse.conv"				,  "linear_fuse"	)
   UpperCamelCase      :   List[Any]			      =  key.replace("linear_fuse.bn"				,  "batch_norm"	)
  if "linear_c" in key:
   # replace for example linear_c4 by linear_c.3
   UpperCamelCase      :   str			      =  key[key.find("linear_c"	) + len("linear_c"	)]
   UpperCamelCase      :   int			      =  key.replace(F"""linear_c{idx}"""				,  F"""linear_c.{int(_lowerCAmelCase	)-1}"""	)
  if key.startswith("head"	):
   UpperCamelCase      :   Dict			      =  key.replace("head"				,  "classifier"	)
  UpperCamelCase      :   Tuple			      =  value

 return new_state_dict



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Tuple:
 # for each of the encoder blocks:
 for i in range(config.num_encoder_blocks	):
  for j in range(config.depths[i]	):
   # read in weights + bias of keys and values (which is a single matrix in the original implementation)
   UpperCamelCase      :   Optional[int]			      =  state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""	)
   UpperCamelCase      :   List[str]			      =  state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""	)
   # next, add keys and values (in that order) to the state dict
   UpperCamelCase      :   str			      =  kv_weight[
       : config.hidden_sizes[i], :
   ]
   UpperCamelCase      :   Dict			      =  kv_bias[: config.hidden_sizes[i]]
   UpperCamelCase      :   List[str]			      =  kv_weight[
       config.hidden_sizes[i] :, :
   ]
   UpperCamelCase      :   int			      =  kv_bias[
       config.hidden_sizes[i] :
   ]



def 						A_							(	)		->					str:
 UpperCamelCase      :   List[str]			      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase      :   List[Any]			      =  Image.open(requests.get(_lowerCAmelCase				,  stream=_lowerCAmelCase	).raw	)

 return image



@torch.no_grad()
def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					Tuple:
 UpperCamelCase      :   Any			      =  SegformerConfig()
 UpperCamelCase      :   Any			      =  False

 # set attributes based on model_name
 UpperCamelCase      :   str			      =  "huggingface/label-files"
 if "segformer" in model_name:
  UpperCamelCase      :   Optional[Any]			      =  model_name[len("segformer."	) : len("segformer."	) + 2]
  if "ade" in model_name:
   UpperCamelCase      :   Union[str, Any]			      =  150
   UpperCamelCase      :   Tuple			      =  "ade20k-id2label.json"
   UpperCamelCase      :   str			      =  (1, 150, 128, 128)
  elif "city" in model_name:
   UpperCamelCase      :   Optional[int]			      =  19
   UpperCamelCase      :   Any			      =  "cityscapes-id2label.json"
   UpperCamelCase      :   Optional[int]			      =  (1, 19, 128, 128)
  else:
   raise ValueError(F"""Model {model_name} not supported"""	)
 elif "mit" in model_name:
  UpperCamelCase      :   Optional[int]			      =  True
  UpperCamelCase      :   str			      =  model_name[4:6]
  UpperCamelCase      :   Any			      =  1000
  UpperCamelCase      :   Dict			      =  "imagenet-1k-id2label.json"
  UpperCamelCase      :   int			      =  (1, 1000)
 else:
  raise ValueError(F"""Model {model_name} not supported"""	)

 # set config attributes
 UpperCamelCase      :   Tuple			      =  json.load(open(hf_hub_download(_lowerCAmelCase				,  _lowerCAmelCase				,  repo_type="dataset"	)				,  "r"	)	)
 UpperCamelCase      :   Union[str, Any]			      =  {int(_lowerCAmelCase	): v for k, v in idalabel.items()}
 UpperCamelCase      :   Union[str, Any]			      =  idalabel
 UpperCamelCase      :   Any			      =  {v: k for k, v in idalabel.items()}
 if size == "b0":
  pass
 elif size == "b1":
  UpperCamelCase      :   Optional[Any]			      =  [64, 128, 320, 512]
  UpperCamelCase      :   Tuple			      =  256
 elif size == "b2":
  UpperCamelCase      :   Tuple			      =  [64, 128, 320, 512]
  UpperCamelCase      :   Optional[Any]			      =  768
  UpperCamelCase      :   List[Any]			      =  [3, 4, 6, 3]
 elif size == "b3":
  UpperCamelCase      :   int			      =  [64, 128, 320, 512]
  UpperCamelCase      :   int			      =  768
  UpperCamelCase      :   Optional[int]			      =  [3, 4, 18, 3]
 elif size == "b4":
  UpperCamelCase      :   Optional[int]			      =  [64, 128, 320, 512]
  UpperCamelCase      :   int			      =  768
  UpperCamelCase      :   Tuple			      =  [3, 8, 27, 3]
 elif size == "b5":
  UpperCamelCase      :   List[Any]			      =  [64, 128, 320, 512]
  UpperCamelCase      :   Union[str, Any]			      =  768
  UpperCamelCase      :   Any			      =  [3, 6, 40, 3]
 else:
  raise ValueError(F"""Size {size} not supported"""	)

 # load image processor (only resize + normalize)
 UpperCamelCase      :   int			      =  SegformerImageProcessor(
     image_scale=(512, 512)				,  keep_ratio=_lowerCAmelCase				,  align=_lowerCAmelCase				,  do_random_crop=_lowerCAmelCase	)

 # prepare image
 UpperCamelCase      :   Tuple			      =  prepare_img()
 UpperCamelCase      :   int			      =  image_processor(images=_lowerCAmelCase				,  return_tensors="pt"	).pixel_values

 logger.info(F"""Converting model {model_name}..."""	)

 # load original state dict
 if encoder_only:
  UpperCamelCase      :   List[str]			      =  torch.load(_lowerCAmelCase				,  map_location=torch.device("cpu"	)	)
 else:
  UpperCamelCase      :   List[Any]			      =  torch.load(_lowerCAmelCase				,  map_location=torch.device("cpu"	)	)["state_dict"]

 # rename keys
 UpperCamelCase      :   List[Any]			      =  rename_keys(_lowerCAmelCase				,  encoder_only=_lowerCAmelCase	)
 if not encoder_only:
  del state_dict["decode_head.conv_seg.weight"]
  del state_dict["decode_head.conv_seg.bias"]

 # key and value matrices need special treatment
 read_in_k_v(_lowerCAmelCase				,  _lowerCAmelCase	)

 # create HuggingFace model and load state dict
 if encoder_only:
  UpperCamelCase      :   Tuple			      =  False
  UpperCamelCase      :   Optional[int]			      =  SegformerForImageClassification(_lowerCAmelCase	)
 else:
  UpperCamelCase      :   Optional[int]			      =  SegformerForSemanticSegmentation(_lowerCAmelCase	)
 model.load_state_dict(_lowerCAmelCase	)
 model.eval()

 # forward pass
 UpperCamelCase      :   Optional[Any]			      =  model(_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  outputs.logits

 # set expected_slice based on model name
 # ADE20k checkpoints
 if model_name == "segformer.b0.512x512.ade.160k":
  UpperCamelCase      :   Optional[Any]			      =  torch.tensor(
      [
          [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
          [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
          [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
      ]	)
 elif model_name == "segformer.b1.512x512.ade.160k":
  UpperCamelCase      :   Any			      =  torch.tensor(
      [
          [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
          [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
          [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
      ]	)
 elif model_name == "segformer.b2.512x512.ade.160k":
  UpperCamelCase      :   Any			      =  torch.tensor(
      [
          [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
          [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
          [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
      ]	)
 elif model_name == "segformer.b3.512x512.ade.160k":
  UpperCamelCase      :   Dict			      =  torch.tensor(
      [
          [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
          [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
          [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
      ]	)
 elif model_name == "segformer.b4.512x512.ade.160k":
  UpperCamelCase      :   Union[str, Any]			      =  torch.tensor(
      [
          [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
          [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
          [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
      ]	)
 elif model_name == "segformer.b5.640x640.ade.160k":
  UpperCamelCase      :   Optional[Any]			      =  torch.tensor(
      [
          [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
          [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
          [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
      ]	)
 # Cityscapes checkpoints
 elif model_name == "segformer.b0.1024x1024.city.160k":
  UpperCamelCase      :   Dict			      =  torch.tensor(
      [
          [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
          [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
          [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
      ]	)
 elif model_name == "segformer.b0.512x1024.city.160k":
  UpperCamelCase      :   Optional[int]			      =  torch.tensor(
      [
          [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
          [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
          [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
      ]	)
 elif model_name == "segformer.b0.640x1280.city.160k":
  UpperCamelCase      :   Dict			      =  torch.tensor(
      [
          [
              [-1.1372e01, -1.2787e01, -1.3477e01],
              [-1.2536e01, -1.4194e01, -1.4409e01],
              [-1.3217e01, -1.4888e01, -1.5327e01],
          ],
          [
              [-1.4791e01, -1.7122e01, -1.8277e01],
              [-1.7163e01, -1.9192e01, -1.9533e01],
              [-1.7897e01, -1.9991e01, -2.0315e01],
          ],
          [
              [7.6723e-01, 4.1921e-01, -7.7878e-02],
              [4.7772e-01, 9.5557e-03, -2.8082e-01],
              [3.6032e-01, -2.4826e-01, -5.1168e-01],
          ],
      ]	)
 elif model_name == "segformer.b0.768x768.city.160k":
  UpperCamelCase      :   str			      =  torch.tensor(
      [
          [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
          [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
          [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
      ]	)
 elif model_name == "segformer.b1.1024x1024.city.160k":
  UpperCamelCase      :   List[str]			      =  torch.tensor(
      [
          [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
          [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
          [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
      ]	)
 elif model_name == "segformer.b2.1024x1024.city.160k":
  UpperCamelCase      :   int			      =  torch.tensor(
      [
          [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
          [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
          [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
      ]	)
 elif model_name == "segformer.b3.1024x1024.city.160k":
  UpperCamelCase      :   Tuple			      =  torch.tensor(
      [
          [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
          [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
          [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
      ]	)
 elif model_name == "segformer.b4.1024x1024.city.160k":
  UpperCamelCase      :   Tuple			      =  torch.tensor(
      [
          [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
          [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
          [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
      ]	)
 elif model_name == "segformer.b5.1024x1024.city.160k":
  UpperCamelCase      :   List[Any]			      =  torch.tensor(
      [
          [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
          [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
          [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
      ]	)
 else:
  UpperCamelCase      :   List[str]			      =  logits.argmax(-1	).item()
  print("Predicted class:"				,  model.config.idalabel[predicted_class_idx]	)

 # verify logits
 if not encoder_only:
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3, :3, :3]				,  _lowerCAmelCase				,  atol=1e-2	)

 # finally, save model and image processor
 logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
 Path(_lowerCAmelCase	).mkdir(exist_ok=_lowerCAmelCase	)
 model.save_pretrained(_lowerCAmelCase	)
 image_processor.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
    __lowerCamelCase			:	Optional[int]					    =	argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""segformer.b0.512x512.ade.160k""",
        type=str,
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    __lowerCamelCase			:	Optional[int]					    =	parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







# 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

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''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(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # 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(A_  )
         UpperCamelCase      :   Any			      =  [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(A_  )
         UpperCamelCase      :   Any			      =  [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:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [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(A_  )
        # 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(A_  )
         UpperCamelCase      :   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:
         UpperCamelCase      :   Union[str, Any]			      =  [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:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )
