






import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def 		A_			(		lowercase_					,       lowercase_=False					,       lowercase_=False					,       lowercase_="data2vec."	)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						'backbone.' if is_semantic else ''

 SCREAMING_SNAKE_CASE							  =						[]
 for i in range(config.num_hidden_layers	):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')	)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
         (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]	)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('mask_token', f'''{hf_prefix}embeddings.mask_token'''),
          (
              'rel_pos_bias.relative_position_bias_table',
              f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              'rel_pos_bias.relative_position_index',
              f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
      ]	)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]	)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('fc_norm.weight', f'''{hf_prefix}pooler.layernorm.weight'''),
          ('fc_norm.bias', f'''{hf_prefix}pooler.layernorm.bias'''),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]	)

 return rename_keys





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_=False					,       lowercase_=False					,       lowercase_="data2vec_vision."	)							->List[Any]:







 """simple docstring"""

 for i in range(config.num_hidden_layers	):
  SCREAMING_SNAKE_CASE							  =						'backbone.' if is_semantic else ''
  # queries, keys and values
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''	)

  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      : config.hidden_size, :
  ]
  SCREAMING_SNAKE_CASE							  =						q_bias
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE							  =						v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''	)

  SCREAMING_SNAKE_CASE							  =						gamma_a
  SCREAMING_SNAKE_CASE							  =						gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''	)
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''	)

   SCREAMING_SNAKE_CASE							  =						table
   SCREAMING_SNAKE_CASE							  =						index





def 		A_			(		)							->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						argparse.ArgumentParser(
     'Convert Data2VecVision to HF for image classification and pretraining'					,       add_help=SCREAMING_SNAKE_CASE_	)
 parser.add_argument('--hf_checkpoint_name'					,       type=SCREAMING_SNAKE_CASE_	)
 parser.add_argument('--input_size'					,       default=2_2_4					,       type=SCREAMING_SNAKE_CASE_					,       help='images input size'	)
 parser.add_argument('--beit_checkpoint'					,       default=''					,       help='beit checkpoint'	)

 return parser.parse_args()





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

 def load_state_dict(lowercase_					,       lowercase_					,       lowercase_=""					,       lowercase_="relative_position_index"	):
  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  # copy state_dict so _load_from_state_dict can modify it
  SCREAMING_SNAKE_CASE							  =						getattr(SCREAMING_SNAKE_CASE_					,       '_metadata'					,       SCREAMING_SNAKE_CASE_	)
  SCREAMING_SNAKE_CASE							  =						state_dict.copy()
  if metadata is not None:
   SCREAMING_SNAKE_CASE							  =						metadata

  def load(lowercase_					,       lowercase_=""	):
   SCREAMING_SNAKE_CASE							  =						{} if metadata is None else metadata.get(prefix[:-1]					,       {}	)
   module._load_from_state_dict(
       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_	)
   for name, child in module._modules.items():
    if child is not None:
     load(SCREAMING_SNAKE_CASE_					,       prefix + name + '.'	)

  load(SCREAMING_SNAKE_CASE_					,       prefix=SCREAMING_SNAKE_CASE_	)

  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  for key in missing_keys:
   SCREAMING_SNAKE_CASE							  =						True
   for ignore_key in ignore_missing.split('|'	):
    if ignore_key in key:
     SCREAMING_SNAKE_CASE							  =						False
     break
   if keep_flag:
    warn_missing_keys.append(SCREAMING_SNAKE_CASE_	)
   else:
    ignore_missing_keys.append(SCREAMING_SNAKE_CASE_	)

  SCREAMING_SNAKE_CASE							  =						warn_missing_keys

  if len(SCREAMING_SNAKE_CASE_	) > 0:
   print(
       'Weights of {} not initialized from pretrained model: {}'.format(
           model.__class__.__name__					,       SCREAMING_SNAKE_CASE_	)	)
  if len(SCREAMING_SNAKE_CASE_	) > 0:
   print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__					,       SCREAMING_SNAKE_CASE_	)	)
  if len(SCREAMING_SNAKE_CASE_	) > 0:
   print(
       'Ignored weights of {} not initialized from pretrained model: {}'.format(
           model.__class__.__name__					,       SCREAMING_SNAKE_CASE_	)	)
  if len(SCREAMING_SNAKE_CASE_	) > 0:
   print('\n'.join(SCREAMING_SNAKE_CASE_	)	)

 SCREAMING_SNAKE_CASE							  =						{
     'pretrained': False,
     'use_shared_rel_pos_bias': True,
     'use_abs_pos_emb': False,
     'init_values': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          'num_classes': 1_0_0_0,
          'use_mean_pooling': True,
          'init_scale': 0.0_0_1,
          'use_rel_pos_bias': True,
      }	)

 SCREAMING_SNAKE_CASE							  =						create_model(
     'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'					,       **SCREAMING_SNAKE_CASE_					,       )
 SCREAMING_SNAKE_CASE							  =						model.patch_embed.patch_size
 SCREAMING_SNAKE_CASE							  =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
 SCREAMING_SNAKE_CASE							  =						torch.load(args.beit_checkpoint					,       map_location='cpu'	)

 print(f'''Load ckpt from {args.beit_checkpoint}'''	)
 SCREAMING_SNAKE_CASE							  =						None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   SCREAMING_SNAKE_CASE							  =						checkpoint[model_key]
   print(f'''Load state_dict by model_key = {model_key}'''	)
   break

 SCREAMING_SNAKE_CASE							  =						list(checkpoint_model.keys()	)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(SCREAMING_SNAKE_CASE_	)

  if "relative_position_bias_table" in key:
   SCREAMING_SNAKE_CASE							  =						checkpoint_model[key]
   SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						rel_pos_bias.size()
   SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						model.state_dict()[key].size()
   SCREAMING_SNAKE_CASE							  =						model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       prefix=''	)

 return model





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						get_args()

 SCREAMING_SNAKE_CASE							  =						'ft1k' in args.hf_checkpoint_name
 SCREAMING_SNAKE_CASE							  =						'large' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 SCREAMING_SNAKE_CASE							  =						DataaVecVisionConfig()
 if is_finetuned:
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						1_0_0_0

  SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
  SCREAMING_SNAKE_CASE							  =						'imagenet-1k-id2label.json'
  SCREAMING_SNAKE_CASE							  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       repo_type='dataset'	)					,       'r'	)	)
  SCREAMING_SNAKE_CASE							  =						{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE							  =						idalabel
  SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}
 else:
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						False

 if is_large:
  SCREAMING_SNAKE_CASE							  =						1_0_2_4
  SCREAMING_SNAKE_CASE							  =						4_0_9_6
  SCREAMING_SNAKE_CASE							  =						2_4
  SCREAMING_SNAKE_CASE							  =						1_6

 # 2. Load Beit model
 SCREAMING_SNAKE_CASE							  =						load_beit_model(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_	)
 orig_model.eval()

 # 3. Forward Beit model
 SCREAMING_SNAKE_CASE							  =						BeitImageProcessor(size=config.image_size					,       do_center_crop=SCREAMING_SNAKE_CASE_	)
 SCREAMING_SNAKE_CASE							  =						Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'	)
 SCREAMING_SNAKE_CASE							  =						image_processor(images=SCREAMING_SNAKE_CASE_					,       return_tensors='pt'	)
 SCREAMING_SNAKE_CASE							  =						encoding['pixel_values']

 SCREAMING_SNAKE_CASE							  =						(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  SCREAMING_SNAKE_CASE							  =						orig_model(*SCREAMING_SNAKE_CASE_	)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  SCREAMING_SNAKE_CASE							  =						DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_	)
  hf_model.eval()
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						'data2vec_vision.'
 else:
  SCREAMING_SNAKE_CASE							  =						DataaVecVisionModel(SCREAMING_SNAKE_CASE_	)
  hf_model.eval()
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						''

 SCREAMING_SNAKE_CASE							  =						create_rename_keys(SCREAMING_SNAKE_CASE_					,       hf_prefix=SCREAMING_SNAKE_CASE_					,       has_lm_head=SCREAMING_SNAKE_CASE_	)
 SCREAMING_SNAKE_CASE							  =						orig_model.state_dict()
 for src, dest in rename_keys:
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(SCREAMING_SNAKE_CASE_	)
  SCREAMING_SNAKE_CASE							  =						val

 read_in_q_k_v(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       hf_prefix=SCREAMING_SNAKE_CASE_					,       has_lm_head=SCREAMING_SNAKE_CASE_	)
 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					,       strict=SCREAMING_SNAKE_CASE_	)
 print('HF missing'					,       SCREAMING_SNAKE_CASE_	)
 print('HF unexpected_keys'					,       SCREAMING_SNAKE_CASE_	)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  SCREAMING_SNAKE_CASE							  =						hf_model(SCREAMING_SNAKE_CASE_	)

 SCREAMING_SNAKE_CASE							  =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 SCREAMING_SNAKE_CASE							  =						torch.max(torch.abs(hf_output - orig_model_output	)	).item()

 print(f'''max_absolute_diff = {max_absolute_diff}'''	)
 SCREAMING_SNAKE_CASE							  =						torch.allclose(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       atol=1e-3	)
 print('Do both models output the same tensors?'					,       '🔥' if success else '💩'	)
 if not success:
  raise Exception('Something went wRoNg'	)

 # 7. Save
 print(f'''Saving to {args.hf_checkpoint_name}'''	)
 hf_model.save_pretrained(args.hf_checkpoint_name	)
 image_processor.save_pretrained(args.hf_checkpoint_name	)


if __name__ == "__main__":
  main()
  # Run the following to convert checkpoints
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"












import math
import sys
class 					a_:



     """simple docstring"""



     def __init__(						self			:					Optional[int])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						{}  # To store the pos of node in array





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      return self.cur_size == 0





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					str)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.left(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.right(lowerCAmelCase__)
      if lc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						lc
      else:
       SCREAMING_SNAKE_CASE							  =						idx
      if rc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						rc
      if smallest != idx:
       self.swap(lowerCAmelCase__       ,       lowerCAmelCase__)
       self.min_heapify(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      # Inserts a node into the Priority Queue
      SCREAMING_SNAKE_CASE							  =						self.cur_size
      self.cur_size += 1
      self.array.append((sys.maxsize, tup[1]))
      self.decrease_key((sys.maxsize, tup[1])       ,       tup[0])





     def   __UpperCamelCase		(						self			:					Dict)    ->					Optional[Any]:

      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      SCREAMING_SNAKE_CASE							  =						self.array[0][1]
      SCREAMING_SNAKE_CASE							  =						self.array[self.cur_size - 1]
      self.cur_size -= 1
      self.min_heapify(1)
      del self.pos[min_node]
      return min_node





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Dict)    ->					int:

      """simple docstring"""
      # returns the index of left child
      return 2 * i + 1





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					List[str])    ->					Optional[Any]:

      """simple docstring"""
      # returns the index of right child
      return 2 * i + 2





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[Any])    ->					Dict:

      """simple docstring"""
      # returns the index of parent
      return math.floor(i / 2)





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					List[str]       ,       lowerCAmelCase__			:					Any)    ->					List[str]:

      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      SCREAMING_SNAKE_CASE							  =						j
      SCREAMING_SNAKE_CASE							  =						i
      SCREAMING_SNAKE_CASE							  =						self.array[i]
      SCREAMING_SNAKE_CASE							  =						self.array[j]
      SCREAMING_SNAKE_CASE							  =						temp





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      SCREAMING_SNAKE_CASE							  =						(new_d, tup[1])
      while idx > 0 and self.array[self.par(lowerCAmelCase__)][0] > self.array[idx][0]:
       self.swap(lowerCAmelCase__       ,       self.par(lowerCAmelCase__))
       SCREAMING_SNAKE_CASE							  =						self.par(lowerCAmelCase__)

class 					a_:



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					str)    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						{}  # To store graph: u -> (v,w)
      SCREAMING_SNAKE_CASE							  =						num  # Number of nodes in graph
      # To store the distance from source vertex
      SCREAMING_SNAKE_CASE							  =						[0] * self.num_nodes
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes  # To store the path





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					List[Any])    ->					Tuple:

      """simple docstring"""
      #  Edge going from node u to v and v to u with weight w
      # u (w)-> v, v (w) -> u
      # Check if u already in graph
      if u in self.adjList:
       self.adjList[u].append((v, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(v, w)]

      # Assuming undirected graph
      if v in self.adjList:
       self.adjList[v].append((u, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(u, w)]





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Tuple:

      """simple docstring"""
      # u -> v(w)
      for u in self.adjList:
       print(lowerCAmelCase__       ,       '->'       ,       ' -> '.join(str(f'''{v}({w})''') for v, w in self.adjList[u]))





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					List[str])    ->					List[Any]:

      """simple docstring"""
      # Flush old junk values in par[]
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes
      # src is the source node
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						PriorityQueue()
      q.insert((0, src))  # (dist from src, node)
      for u in self.adjList:
       if u != src:
        SCREAMING_SNAKE_CASE							  =						sys.maxsize  # Infinity
        SCREAMING_SNAKE_CASE							  =						-1

      while not q.is_empty():
       SCREAMING_SNAKE_CASE							  =						q.extract_min()  # Returns node with the min dist from source
       # Update the distance of all the neighbours of u and
       # if their prev dist was INFINITY then push them in Q
       for v, w in self.adjList[u]:
        SCREAMING_SNAKE_CASE							  =						self.dist[u] + w
        if self.dist[v] > new_dist:
         if self.dist[v] == sys.maxsize:
          q.insert((new_dist, v))
         else:
          q.decrease_key((self.dist[v], v)       ,       lowerCAmelCase__)
         SCREAMING_SNAKE_CASE							  =						new_dist
         SCREAMING_SNAKE_CASE							  =						u

        # Show the shortest distances from src
      self.show_distances(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """simple docstring"""
      print(f'''Distance from node: {src}''')
      for u in range(self.num_nodes):
       print(f'''Node {u} has distance: {self.dist[u]}''')





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
       path.append(lowerCAmelCase__)
       if temp != src:
        for v, w in self.adjList[temp]:
         if v == self.par[temp]:
          cost += w
          break
       SCREAMING_SNAKE_CASE							  =						self.par[temp]
      path.append(lowerCAmelCase__)
      path.reverse()

      print(f'''----Path to reach {dest} from {src}----''')
      for u in path:
       print(f'''{u}'''       ,       end=' ')
       if u != dest:
        print('-> '       ,       end='')

      print('\nTotal cost of path: '       ,       lowerCAmelCase__)


if __name__ == "__main__":
      __UpperCAmelCase      				=							Graph(9)
      graph.add_edge(0, 1, 4)
      graph.add_edge(0, 7, 8)
      graph.add_edge(1, 2, 8)
      graph.add_edge(1, 7, 1_1)
      graph.add_edge(2, 3, 7)
      graph.add_edge(2, 8, 2)
      graph.add_edge(2, 5, 4)
      graph.add_edge(3, 4, 9)
      graph.add_edge(3, 5, 1_4)
      graph.add_edge(4, 5, 1_0)
      graph.add_edge(5, 6, 2)
      graph.add_edge(6, 7, 1)
      graph.add_edge(6, 8, 6)
      graph.add_edge(7, 8, 7)
      graph.show_graph()
      graph.dijkstra(0)
      graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




