

"""simple docstring"""




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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

def 						UpperCamelCase (lowercase_:			Any )					->				Tuple:
	A__							:   Tuple					       =		OrderedDict()
	for key, value in state_dict.items():
		if key.startswith("""module.encoder""" ):
			A__							:   int					       =		key.replace("""module.encoder"""   ,				"""glpn.encoder""" )
		if key.startswith("""module.decoder""" ):
			A__							:   List[str]					       =		key.replace("""module.decoder"""   ,				"""decoder.stages""" )
		if "patch_embed" in key:
			# replace for example patch_embed1 by patch_embeddings.0
			A__							:   Optional[Any]					       =		key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
			A__							:   str					       =		key.replace(f"""patch_embed{idx}"""   ,				f"""patch_embeddings.{int(lowercase_ )-1}""" )
		if "norm" in key:
			A__							:   int					       =		key.replace("""norm"""   ,				"""layer_norm""" )
		if "glpn.encoder.layer_norm" in key:
			# replace for example layer_norm1 by layer_norm.0
			A__							:   List[str]					       =		key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
			A__							:   Union[str, Any]					       =		key.replace(f"""layer_norm{idx}"""   ,				f"""layer_norm.{int(lowercase_ )-1}""" )
		if "layer_norm1" in key:
			A__							:   Any					       =		key.replace("""layer_norm1"""   ,				"""layer_norm_1""" )
		if "layer_norm2" in key:
			A__							:   List[Any]					       =		key.replace("""layer_norm2"""   ,				"""layer_norm_2""" )
		if "block" in key:
			# replace for example block1 by block.0
			A__							:   List[Any]					       =		key[key.find("""block""" ) + len("""block""" )]
			A__							:   Optional[Any]					       =		key.replace(f"""block{idx}"""   ,				f"""block.{int(lowercase_ )-1}""" )
		if "attn.q" in key:
			A__							:   Optional[int]					       =		key.replace("""attn.q"""   ,				"""attention.self.query""" )
		if "attn.proj" in key:
			A__							:   Optional[Any]					       =		key.replace("""attn.proj"""   ,				"""attention.output.dense""" )
		if "attn" in key:
			A__							:   Union[str, Any]					       =		key.replace("""attn"""   ,				"""attention.self""" )
		if "fc1" in key:
			A__							:   Optional[int]					       =		key.replace("""fc1"""   ,				"""dense1""" )
		if "fc2" in key:
			A__							:   Dict					       =		key.replace("""fc2"""   ,				"""dense2""" )
		if "linear_pred" in key:
			A__							:   Optional[Any]					       =		key.replace("""linear_pred"""   ,				"""classifier""" )
		if "linear_fuse" in key:
			A__							:   Union[str, Any]					       =		key.replace("""linear_fuse.conv"""   ,				"""linear_fuse""" )
			A__							:   Optional[int]					       =		key.replace("""linear_fuse.bn"""   ,				"""batch_norm""" )
		if "linear_c" in key:
			# replace for example linear_c4 by linear_c.3
			A__							:   int					       =		key[key.find("""linear_c""" ) + len("""linear_c""" )]
			A__							:   Any					       =		key.replace(f"""linear_c{idx}"""   ,				f"""linear_c.{int(lowercase_ )-1}""" )
		if "bot_conv" in key:
			A__							:   str					       =		key.replace("""bot_conv"""   ,				"""0.convolution""" )
		if "skip_conv1" in key:
			A__							:   Union[str, Any]					       =		key.replace("""skip_conv1"""   ,				"""1.convolution""" )
		if "skip_conv2" in key:
			A__							:   Any					       =		key.replace("""skip_conv2"""   ,				"""2.convolution""" )
		if "fusion1" in key:
			A__							:   int					       =		key.replace("""fusion1"""   ,				"""1.fusion""" )
		if "fusion2" in key:
			A__							:   int					       =		key.replace("""fusion2"""   ,				"""2.fusion""" )
		if "fusion3" in key:
			A__							:   Dict					       =		key.replace("""fusion3"""   ,				"""3.fusion""" )
		if "fusion" in key and "conv" in key:
			A__							:   List[Any]					       =		key.replace("""conv"""   ,				"""convolutional_layer""" )
		if key.startswith("""module.last_layer_depth""" ):
			A__							:   List[str]					       =		key.replace("""module.last_layer_depth"""   ,				"""head.head""" )
		A__							:   Union[str, Any]					       =		value

	return new_state_dict

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Any )					->				Any:






	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)
			A__							:   str					       =		state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
			A__							:   Optional[Any]					       =		state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
			# next, add keys and values (in that order) to the state dict
			A__							:   Union[str, Any]					       =		kv_weight[
			    : config.hidden_sizes[i], :
			]
			A__							:   Union[str, Any]					       =		kv_bias[: config.hidden_sizes[i]]
			A__							:   Optional[Any]					       =		kv_weight[
			    config.hidden_sizes[i] :, :
			]
			A__							:   str					       =		kv_bias[config.hidden_sizes[i] :]

def 						UpperCamelCase ()					->				List[str]:
	A__							:   Optional[int]					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:   Dict					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )

	return image

@torch.no_grad()
def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Dict=False   ,				lowercase_:			int=None )					->				Optional[Any]:
	A__							:   List[Any]					       =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]   ,				decoder_hidden_size=64   ,				depths=[3, 8, 27, 3] )

	# load image processor (only resize + rescale)
	A__							:   str					       =		GLPNImageProcessor()

	# prepare image
	A__							:   Optional[Any]					       =		prepare_img()
	A__							:   str					       =		image_processor(images=lowercase_   ,				return_tensors="""pt""" ).pixel_values

	logger.info("""Converting model...""" )

	# load original state dict
	A__							:   int					       =		torch.load(lowercase_   ,				map_location=torch.device("""cpu""" ) )

	# rename keys
	A__							:   int					       =		rename_keys(lowercase_ )

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

	# create HuggingFace model and load state dict
	A__							:   Optional[Any]					       =		GLPNForDepthEstimation(lowercase_ )
	model.load_state_dict(lowercase_ )
	model.eval()

	# forward pass
	A__							:   Any					       =		model(lowercase_ )
	A__							:   Tuple					       =		outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			A__							:   List[Any]					       =		torch.tensor(
			    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
		elif "kitti" in model_name:
			A__							:   Tuple					       =		torch.tensor(
			    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
		else:
			raise ValueError(f"""Unknown model name: {model_name}""" )

		A__							:   Any					       =		torch.Size([1, 480, 640] )

		assert predicted_depth.shape == expected_shape
		assert torch.allclose(predicted_depth[0, :3, :3]   ,				lowercase_   ,				atol=1E-4 )
		print("""Looks ok!""" )

	# finally, push to hub if required
	if push_to_hub:
		logger.info("""Pushing model and image processor to the hub...""" )
		model.push_to_hub(
		    repo_path_or_name=Path(lowercase_   ,				lowercase_ )   ,				organization="""nielsr"""   ,				commit_message="""Add model"""   ,				use_temp_dir=lowercase_   ,				)
		image_processor.push_to_hub(
		    repo_path_or_name=Path(lowercase_   ,				lowercase_ )   ,				organization="""nielsr"""   ,				commit_message="""Add image processor"""   ,				use_temp_dir=lowercase_   ,				)


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()

	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.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
	)
	parser.add_argument(
	    '--model_name',
	    default='glpn-kitti',
	    type=str,
	    help='Name of the model in case you\'re pushing to the hub.',
	)
	A_	:			Any  	=      parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



