






import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


UpperCamelCase												=      {
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 32,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 16,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 768,
        '''image_resolution''': 840,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''vision_layers''': 24,
        '''vision_width''': 1_024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}

def 			__lowerCamelCase     (						snake_case__			,snake_case__=""			,snake_case__="/"						)				->    Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []

						for k, v in params.items():
												_SCREAMING_SNAKE_CASE       =    parent_key + sep + k if parent_key else k

												if isinstance(snake_case__			,collections.MutableMapping						):
																		items.extend(flatten_nested_dict(snake_case__			,snake_case__			,sep=snake_case__						).items()						)
												else:
																		items.append((new_key, v)						)
						return dict(snake_case__						)

def 			__lowerCamelCase     (						snake_case__						)				->    Union[str, Any]:






						"""simple docstring"""





						return jax.tree_util.tree_map(lambda snake_case__						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    pt_attn_layer.in_proj_weight.chunk(3			,dim=0						)
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    pt_attn_layer.in_proj_bias.chunk(3			,dim=0						)

						_SCREAMING_SNAKE_CASE       =    pt_attn_layer.out_proj.weight
						_SCREAMING_SNAKE_CASE       =    pt_attn_layer.out_proj.bias

						_SCREAMING_SNAKE_CASE       =    q_proj
						_SCREAMING_SNAKE_CASE       =    q_proj_bias

						_SCREAMING_SNAKE_CASE       =    k_proj
						_SCREAMING_SNAKE_CASE       =    k_proj_bias

						_SCREAMING_SNAKE_CASE       =    v_proj
						_SCREAMING_SNAKE_CASE       =    v_proj_bias

						_SCREAMING_SNAKE_CASE       =    out_proj_weights
						_SCREAMING_SNAKE_CASE       =    out_proj_bias

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    List[str]:






						"""simple docstring"""





						copy_linear(hf_mlp.fca			,pt_mlp.c_fc						)
						copy_linear(hf_mlp.fca			,pt_mlp.c_proj						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    pt_linear.weight
						_SCREAMING_SNAKE_CASE       =    pt_linear.bias

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Any:






						"""simple docstring"""





						copy_linear(hf_layer.layer_norma			,pt_layer.ln_a						)
						copy_linear(hf_layer.layer_norma			,pt_layer.ln_a						)

						# copy MLP
						copy_mlp(hf_layer.mlp			,pt_layer.mlp						)

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,pt_layer.attn						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						for hf_layer, pt_layer in zip(snake_case__			,snake_case__						):
												copy_layer(snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    pt_model.token_embedding.weight
						_SCREAMING_SNAKE_CASE       =    pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,pt_model.ln_final						)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,pt_model.transformer.resblocks						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layernorm			,pt_model.visual.ln_pre						)
						copy_linear(hf_model.vision_model.post_layernorm			,pt_model.visual.ln_post						)

						# copy embeds
						_SCREAMING_SNAKE_CASE       =    pt_model.visual.conva.weight.data
						_SCREAMING_SNAKE_CASE       =    pt_model.visual.class_embedding
						_SCREAMING_SNAKE_CASE       =    pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,pt_model.visual.transformer.resblocks						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]						)

						_SCREAMING_SNAKE_CASE       =    torch.from_numpy(flax_class_token_params["""scale"""]						)
						_SCREAMING_SNAKE_CASE       =    torch.from_numpy(flax_class_token_params["""bias"""]						)
						_SCREAMING_SNAKE_CASE       =    nn.Parameter(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    nn.Parameter(snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    hf_model.state_dict()
						_SCREAMING_SNAKE_CASE       =    {}

						# Rename class prediction head flax params to pytorch HF
						_SCREAMING_SNAKE_CASE       =    flatten_nested_dict(flax_params["""class_head"""]						)

						for flax_key, v in flax_class_params.items():
												_SCREAMING_SNAKE_CASE       =    flax_key.replace("""/"""			,"""."""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace(""".kernel"""			,""".weight"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""Dense_0"""			,"""dense0"""						)
												_SCREAMING_SNAKE_CASE       =    """class_head.""" + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		_SCREAMING_SNAKE_CASE       =    v.T

												_SCREAMING_SNAKE_CASE       =    nn.Parameter(torch.from_numpy(snake_case__						)						)

						# Rename box prediction box flax params to pytorch HF
						_SCREAMING_SNAKE_CASE       =    flatten_nested_dict(flax_params["""obj_box_head"""]						)

						for flax_key, v in flax_box_params.items():
												_SCREAMING_SNAKE_CASE       =    flax_key.replace("""/"""			,"""."""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace(""".kernel"""			,""".weight"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""_"""			,""""""						).lower()
												_SCREAMING_SNAKE_CASE       =    """box_head.""" + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		_SCREAMING_SNAKE_CASE       =    v.T

												_SCREAMING_SNAKE_CASE       =    nn.Parameter(torch.from_numpy(snake_case__						)						)

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    List[str]:






						"""simple docstring"""





						for k, v in flax_attn_params.items():
												if k.startswith("""transformer"""						):
																		_SCREAMING_SNAKE_CASE       =    k.replace("""transformer.resblocks"""			,"""text_model.encoder.layers"""						)
												else:
																		_SCREAMING_SNAKE_CASE       =    k.replace("""visual.transformer.resblocks"""			,"""vision_model.encoder.layers"""						)

												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""attn"""			,"""self_attn"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""key"""			,"""k_proj"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""value"""			,"""v_proj"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""query"""			,"""q_proj"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""out"""			,"""out_proj"""						)

												if "bias" in torch_key and v.ndim == 2:
																		_SCREAMING_SNAKE_CASE       =    v.shape[0] * v.shape[1]
																		_SCREAMING_SNAKE_CASE       =    v.reshape(snake_case__						)

												if "weight" in torch_key and "out" in torch_key:
																		_SCREAMING_SNAKE_CASE       =    (v.shape[0] * v.shape[1], v.shape[2])
																		_SCREAMING_SNAKE_CASE       =    v.reshape(snake_case__						).T

												if "weight" in torch_key and "out" not in torch_key:
																		_SCREAMING_SNAKE_CASE       =    (v.shape[0], v.shape[1] * v.shape[2])
																		_SCREAMING_SNAKE_CASE       =    v.reshape(snake_case__						).T

												# Copy flax CLIP attn params to HF PyTorch params
												_SCREAMING_SNAKE_CASE       =    torch.from_numpy(snake_case__						)
												hf_backbone.state_dict()[torch_key].copy_(snake_case__						)

def 			__lowerCamelCase     (						snake_case__						)				->    Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    {}
						_SCREAMING_SNAKE_CASE       =    []

						for k, v in params.items():
												if "attn." in k:
																		_SCREAMING_SNAKE_CASE       =    k[: k.rindex("""attn."""						) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(snake_case__						)
																		_SCREAMING_SNAKE_CASE       =    params[base + """out.weight"""].shape[-1]
																		_SCREAMING_SNAKE_CASE       =    params[base + """out.weight"""].reshape(snake_case__			,snake_case__						).T
																		_SCREAMING_SNAKE_CASE       =    params[base + """out.bias"""]
												else:
																		_SCREAMING_SNAKE_CASE       =    v
						return new_params

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    CLIP(**snake_case__						)
						torch_model.eval()
						_SCREAMING_SNAKE_CASE       =    torch_model.state_dict()

						_SCREAMING_SNAKE_CASE       =    flatten_nested_dict(flax_params["""backbone"""]["""clip"""]						)
						_SCREAMING_SNAKE_CASE       =    {}

						for flax_key, v in flax_clip_params.items():
												_SCREAMING_SNAKE_CASE       =    flax_key.replace("""/"""			,"""."""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""text.token_embedding.embedding"""			,"""token_embedding.kernel"""						)

												if (
												    torch_key.startswith("""text.transformer"""						)
												    or torch_key.startswith("""text.text_projection"""						)
												    or torch_key.startswith("""text.ln_final"""						)
												    or torch_key.startswith("""text.positional_embedding"""						)
												):
																		_SCREAMING_SNAKE_CASE       =    torch_key[5:]

												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""text_projection.kernel"""			,"""text_projection"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace("""visual.proj.kernel"""			,"""visual.proj"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace(""".scale"""			,""".weight"""						)
												_SCREAMING_SNAKE_CASE       =    torch_key.replace(""".kernel"""			,""".weight"""						)

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		_SCREAMING_SNAKE_CASE       =    v.transpose(3			,2			,0			,1						)

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		_SCREAMING_SNAKE_CASE       =    v.T

												_SCREAMING_SNAKE_CASE       =    v

						_SCREAMING_SNAKE_CASE       =    _convert_attn_layers(snake_case__						)
						new_torch_params.update(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    {}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		_SCREAMING_SNAKE_CASE       =    torch.from_numpy(new_torch_params[name]						)
																		torch_clip_params[name].copy_(snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    param

						return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__			,snake_case__			,snake_case__=None						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    Repository(snake_case__			,clone_from=F'google/{pytorch_dump_folder_path}'						)
						repo.git_pull()

						if config_path is not None:
												_SCREAMING_SNAKE_CASE       =    OwlViTConfig.from_pretrained(snake_case__						)
						else:
												_SCREAMING_SNAKE_CASE       =    OwlViTConfig()

						_SCREAMING_SNAKE_CASE       =    OwlViTModel(snake_case__						).eval()
						_SCREAMING_SNAKE_CASE       =    OwlViTForObjectDetection(snake_case__						).eval()

						copy_text_model_and_projection(snake_case__			,snake_case__						)
						copy_vision_model_and_projection(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    pt_backbone.logit_scale
						copy_flax_attn_params(snake_case__			,snake_case__						)

						_SCREAMING_SNAKE_CASE       =    hf_backbone
						copy_class_merge_token(snake_case__			,snake_case__						)
						copy_class_box_heads(snake_case__			,snake_case__						)

						# Save HF model
						hf_model.save_pretrained(repo.local_dir						)

						# Initialize image processor
						_SCREAMING_SNAKE_CASE       =    OwlViTImageProcessor(
						    size=config.vision_config.image_size			,crop_size=config.vision_config.image_size						)
						# Initialize tokenizer
						_SCREAMING_SNAKE_CASE       =    CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			,pad_token="""!"""			,model_max_length=16						)

						# Initialize processor
						_SCREAMING_SNAKE_CASE       =    OwlViTProcessor(image_processor=snake_case__			,tokenizer=snake_case__						)
						image_processor.save_pretrained(repo.local_dir						)
						processor.save_pretrained(repo.local_dir						)

						repo.git_add()
						repo.git_commit("""Upload model and processor"""						)
						repo.git_push()


if __name__ == "__main__":
		UpperCamelCase												=      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--owlvit_version''',
		    default=None,
		    type=str,
		    required=True,
		    help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
		)
		parser.add_argument(
		    '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
		)
		parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
		)
		UpperCamelCase												=      parser.parse_args()

		# Initialize PyToch clip model
		UpperCamelCase												=      args.owlvit_version
		if model_name == "clip_b16":
				UpperCamelCase												=      CONFIGS['''vit_b16''']
		elif model_name == "clip_b32":
				UpperCamelCase												=      CONFIGS['''vit_b32''']
		elif model_name == "clip_l14":
				UpperCamelCase												=      CONFIGS['''vit_l14''']

		# Load from checkpoint and convert params to float-32
		UpperCamelCase												=      checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
		UpperCamelCase												=      jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
		del variables

		# Convert CLIP backbone
		UpperCamelCase							,       UpperCamelCase							,       UpperCamelCase												=      convert_clip_backbone(flax_params, torch_config)

		convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)









import math
import sys



class  __UpperCAmelCase						:







			def __init__(      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    0
									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    {}  # To store the pos of node in array







			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									return self.cur_size == 0







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.left(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.right(UpperCAmelCase_	)
									if lc < self.cur_size and self.array(UpperCAmelCase_	)[0] < self.array(UpperCAmelCase_	)[0]:
															_SCREAMING_SNAKE_CASE       =    lc
									else:
															_SCREAMING_SNAKE_CASE       =    idx
									if rc < self.cur_size and self.array(UpperCAmelCase_	)[0] < self.array(UpperCAmelCase_	)[0]:
															_SCREAMING_SNAKE_CASE       =    rc
									if smallest != idx:
															self.swap(UpperCAmelCase_ ,      UpperCAmelCase_	)
															self.min_heapify(UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_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:    Union[str, Any]	):







									'''simple docstring'''




									_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:    Dict ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									return 2 * i + 1







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									return 2 * i + 2







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    int	):







									'''simple docstring'''




									return math.floor(i / 2	)







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_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:    int ,      UpperCAmelCase_:    Dict ,      UpperCAmelCase_:    Dict	):







									'''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(UpperCAmelCase_	)][0] > self.array[idx][0]:
															self.swap(UpperCAmelCase_ ,      self.par(UpperCAmelCase_	)	)
															_SCREAMING_SNAKE_CASE       =    self.par(UpperCAmelCase_	)



class  __UpperCAmelCase						:







			def __init__(      self:    Tuple ,      UpperCAmelCase_:    Any	):







									'''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:    Tuple ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




									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:    Dict	):







									'''simple docstring'''




									for u in self.adjList:
															print(UpperCAmelCase_ ,      """->""" ,      """ -> """.join(str(F'{v}({w})'	) for v, w in self.adjList[u]	)	)







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_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) ,      UpperCAmelCase_	)
																											_SCREAMING_SNAKE_CASE       =    new_dist
																											_SCREAMING_SNAKE_CASE       =    u

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







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    int	):







									'''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:    str ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    0
									_SCREAMING_SNAKE_CASE       =    dest
									# Backtracking from dest to src
									while self.par[temp] != -1:
															path.append(UpperCAmelCase_	)
															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(UpperCAmelCase_	)
									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: """ ,      UpperCAmelCase_	)


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, 11)
		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, 14)
		graph.add_edge(4, 5, 10)
		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

