






import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__   :						List[Any]													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase):
   # initialize config
   if "resnet-50" in model_name:
      UpperCamelCase_						=  ResNetConfig.from_pretrained('microsoft/resnet-50')
   elif "resnet-101" in model_name:
      UpperCamelCase_						=  ResNetConfig.from_pretrained('microsoft/resnet-101')
   else:
      raise ValueError('Model name should include either resnet50 or resnet101')

   UpperCamelCase_						=  DetrConfig(use_timm_backbone=__lowercase     ,   backbone_config=__lowercase)

   # set label attributes
   UpperCamelCase_						=  'panoptic' in model_name
   if is_panoptic:
      UpperCamelCase_						=  250
   else:
      UpperCamelCase_						=  91
      UpperCamelCase_						=  'huggingface/label-files'
      UpperCamelCase_						=  'coco-detection-id2label.json'
      UpperCamelCase_						=  json.load(open(hf_hub_download(__lowercase     ,   __lowercase     ,   repo_type='dataset')     ,   'r'))
      UpperCamelCase_						=  {int(__lowercase): v for k, v in idalabel.items()}
      UpperCamelCase_						=  idalabel
      UpperCamelCase_						=  {v: k for k, v in idalabel.items()}

   return config, is_panoptic






def      _snake_case     (__lowercase):
   # here we list all keys to be renamed (original name on the left, our name on the right)
   UpperCamelCase_						=  []

   # stem
   # fmt: off
   rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight'))
   rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight'))
   rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias'))
   rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'))
   rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var'))
   # stages
   for stage_idx in range(len(config.backbone_config.depths)):
      for layer_idx in range(config.backbone_config.depths[stage_idx]):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                ))
         # 3 convs
         for i in range(3):
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                ))
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                    f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                ))
    # fmt: on

   for i in range(config.encoder_layers):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
              f"""encoder.layers.{i}.self_attn.out_proj.weight""",
          ))
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight"""))
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
      rename_keys.append(
          (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
              f"""decoder.layers.{i}.self_attn.out_proj.weight""",
          ))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias"""))
      rename_keys.append(
          (
              f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
              f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          ))
      rename_keys.append(
          (
              f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
              f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
          ))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias"""))
      rename_keys.append(
          (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ('input_proj.weight', 'input_projection.weight'),
           ('input_proj.bias', 'input_projection.bias'),
           ('query_embed.weight', 'query_position_embeddings.weight'),
           ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
           ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
           ('class_embed.weight', 'class_labels_classifier.weight'),
           ('class_embed.bias', 'class_labels_classifier.bias'),
           ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
           ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
           ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
           ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
           ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
           ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
       ])

   return rename_keys






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  state_dict.pop(__lowercase)
   UpperCamelCase_						=  val






def      _snake_case     (__lowercase     ,   __lowercase=False):
   UpperCamelCase_						=  ''
   if is_panoptic:
      UpperCamelCase_						=  'detr.'

   # first: transformer encoder
   for i in range(6):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      UpperCamelCase_						=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
      UpperCamelCase_						=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""")
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase_						=  in_proj_weight[:256, :]
      UpperCamelCase_						=  in_proj_bias[:256]
      UpperCamelCase_						=  in_proj_weight[256:512, :]
      UpperCamelCase_						=  in_proj_bias[256:512]
      UpperCamelCase_						=  in_proj_weight[-256:, :]
      UpperCamelCase_						=  in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6):
      # read in weights + bias of input projection layer of self-attention
      UpperCamelCase_						=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""")
      UpperCamelCase_						=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""")
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase_						=  in_proj_weight[:256, :]
      UpperCamelCase_						=  in_proj_bias[:256]
      UpperCamelCase_						=  in_proj_weight[256:512, :]
      UpperCamelCase_						=  in_proj_bias[256:512]
      UpperCamelCase_						=  in_proj_weight[-256:, :]
      UpperCamelCase_						=  in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      UpperCamelCase_						=  state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""")
      UpperCamelCase_						=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""")
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      UpperCamelCase_						=  in_proj_weight_cross_attn[:256, :]
      UpperCamelCase_						=  in_proj_bias_cross_attn[:256]
      UpperCamelCase_						=  in_proj_weight_cross_attn[256:512, :]
      UpperCamelCase_						=  in_proj_bias_cross_attn[256:512]
      UpperCamelCase_						=  in_proj_weight_cross_attn[-256:, :]
      UpperCamelCase_						=  in_proj_bias_cross_attn[-256:]






def      _snake_case     ():
   UpperCamelCase_						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase_						=  Image.open(requests.get(__lowercase     ,   stream=__lowercase).raw)

   return im






@torch.no_grad()
def      _snake_case     (__lowercase     ,   __lowercase=None     ,   __lowercase=False):
   UpperCamelCase_		,   UpperCamelCase_						=  get_detr_config(__lowercase)

   # load original model from torch hub
   UpperCamelCase_						=  {
       'detr-resnet-50': 'detr_resnet50',
       'detr-resnet-101': 'detr_resnet101',
   }
   logger.info(f"""Converting model {model_name}...""")
   UpperCamelCase_						=  torch.hub.load('facebookresearch/detr'     ,   model_name_to_original_name[model_name]     ,   pretrained=__lowercase).eval()
   UpperCamelCase_						=  detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(__lowercase):
      if is_panoptic:
         UpperCamelCase_						=  'detr.' + src
      rename_key(__lowercase     ,   __lowercase     ,   __lowercase)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__lowercase     ,   is_panoptic=__lowercase)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCamelCase_						=  'detr.model.' if is_panoptic else 'model.'
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('detr')
             and not key.startswith('class_labels_classifier')
             and not key.startswith('bbox_predictor')
         ):
            UpperCamelCase_						=  state_dict.pop(__lowercase)
            UpperCamelCase_						=  val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCamelCase_						=  state_dict.pop(__lowercase)
            UpperCamelCase_						=  val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            UpperCamelCase_						=  state_dict.pop(__lowercase)
            UpperCamelCase_						=  val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            UpperCamelCase_						=  state_dict.pop(__lowercase)
            UpperCamelCase_						=  val

    # finally, create HuggingFace model and load state dict
   UpperCamelCase_						=  DetrForSegmentation(__lowercase) if is_panoptic else DetrForObjectDetection(__lowercase)
   model.load_state_dict(__lowercase)
   model.eval()

   # verify our conversion on an image
   UpperCamelCase_						=  'coco_panoptic' if is_panoptic else 'coco_detection'
   UpperCamelCase_						=  DetrImageProcessor(format=__lowercase)

   UpperCamelCase_						=  processor(images=prepare_img()     ,   return_tensors='pt')
   UpperCamelCase_						=  encoding['pixel_values']

   UpperCamelCase_						=  detr(__lowercase)
   UpperCamelCase_						=  model(__lowercase)

   assert torch.allclose(outputs.logits     ,   original_outputs['pred_logits']     ,   atol=1e-3)
   assert torch.allclose(outputs.pred_boxes     ,   original_outputs['pred_boxes']     ,   atol=1e-3)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks     ,   original_outputs['pred_masks']     ,   atol=1e-4)
   print('Looks ok!')

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""")
      Path(__lowercase).mkdir(exist_ok=__lowercase)
      model.save_pretrained(__lowercase)
      processor.save_pretrained(__lowercase)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('Uploading PyTorch model and image processor to the hub...')
      model.push_to_hub(f"""nielsr/{model_name}""")
      processor.push_to_hub(f"""nielsr/{model_name}""")


if __name__ == "__main__":
 snake_case__   :						Any													=   argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""detr-resnet-50""",
     type=str,
     choices=["""detr-resnet-50""", """detr-resnet-101"""],
     help="""Name of the DETR model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
 snake_case__   :						List[str]													=   parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





