




'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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


def    lowerCamelCase__   ( A     :    Any  ):




						'''simple docstring'''






						UpperCAmelCase         =			DPTConfig()

						if "large" in checkpoint_url:
												UpperCAmelCase         =			10_24
												UpperCAmelCase         =			40_96
												UpperCAmelCase         =			24
												UpperCAmelCase         =			16
												UpperCAmelCase         =			[5, 11, 17, 23]
												UpperCAmelCase         =			[2_56, 5_12, 10_24, 10_24]
												UpperCAmelCase         =			(1, 3_84, 3_84)

						if "ade" in checkpoint_url:
												UpperCAmelCase         =			True

												UpperCAmelCase         =			1_50
												UpperCAmelCase         =			"""huggingface/label-files"""
												UpperCAmelCase         =			"""ade20k-id2label.json"""
												UpperCAmelCase         =			json.load(open(cached_download(hf_hub_url(A   ,     A   ,     repo_type='''dataset'''  )  )   ,     '''r'''  )  )
												UpperCAmelCase         =			{int(A  ): v for k, v in idalabel.items()}
												UpperCAmelCase         =			idalabel
												UpperCAmelCase         =			{v: k for k, v in idalabel.items()}
												UpperCAmelCase         =			[1, 1_50, 4_80, 4_80]

						return config, expected_shape


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(A   ,     A  )


def    lowerCamelCase__   ( A     :    Any  ):




						'''simple docstring'''






						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												UpperCAmelCase         =			name.replace('''pretrained.model'''   ,     '''dpt.encoder'''  )
						if "pretrained.model" in name:
												UpperCAmelCase         =			name.replace('''pretrained.model'''   ,     '''dpt.embeddings'''  )
						if "patch_embed" in name:
												UpperCAmelCase         =			name.replace('''patch_embed'''   ,     '''patch_embeddings'''  )
						if "pos_embed" in name:
												UpperCAmelCase         =			name.replace('''pos_embed'''   ,     '''position_embeddings'''  )
						if "attn.proj" in name:
												UpperCAmelCase         =			name.replace('''attn.proj'''   ,     '''attention.output.dense'''  )
						if "proj" in name and "project" not in name:
												UpperCAmelCase         =			name.replace('''proj'''   ,     '''projection'''  )
						if "blocks" in name:
												UpperCAmelCase         =			name.replace('''blocks'''   ,     '''layer'''  )
						if "mlp.fc1" in name:
												UpperCAmelCase         =			name.replace('''mlp.fc1'''   ,     '''intermediate.dense'''  )
						if "mlp.fc2" in name:
												UpperCAmelCase         =			name.replace('''mlp.fc2'''   ,     '''output.dense'''  )
						if "norm1" in name:
												UpperCAmelCase         =			name.replace('''norm1'''   ,     '''layernorm_before'''  )
						if "norm2" in name:
												UpperCAmelCase         =			name.replace('''norm2'''   ,     '''layernorm_after'''  )
						if "scratch.output_conv" in name:
												UpperCAmelCase         =			name.replace('''scratch.output_conv'''   ,     '''head'''  )
						if "scratch" in name:
												UpperCAmelCase         =			name.replace('''scratch'''   ,     '''neck'''  )
						if "layer1_rn" in name:
												UpperCAmelCase         =			name.replace('''layer1_rn'''   ,     '''convs.0'''  )
						if "layer2_rn" in name:
												UpperCAmelCase         =			name.replace('''layer2_rn'''   ,     '''convs.1'''  )
						if "layer3_rn" in name:
												UpperCAmelCase         =			name.replace('''layer3_rn'''   ,     '''convs.2'''  )
						if "layer4_rn" in name:
												UpperCAmelCase         =			name.replace('''layer4_rn'''   ,     '''convs.3'''  )
						if "refinenet" in name:
												UpperCAmelCase         =			int(name[len('''neck.refinenet'''  ) : len('''neck.refinenet'''  ) + 1]  )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												UpperCAmelCase         =			name.replace(f"""refinenet{layer_idx}"""   ,     f"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
						if "out_conv" in name:
												UpperCAmelCase         =			name.replace('''out_conv'''   ,     '''projection'''  )
						if "resConfUnit1" in name:
												UpperCAmelCase         =			name.replace('''resConfUnit1'''   ,     '''residual_layer1'''  )
						if "resConfUnit2" in name:
												UpperCAmelCase         =			name.replace('''resConfUnit2'''   ,     '''residual_layer2'''  )
						if "conv1" in name:
												UpperCAmelCase         =			name.replace('''conv1'''   ,     '''convolution1'''  )
						if "conv2" in name:
												UpperCAmelCase         =			name.replace('''conv2'''   ,     '''convolution2'''  )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.0.0'''  )
						if "pretrained.act_postprocess2.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.1.0'''  )
						if "pretrained.act_postprocess3.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess3.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.2.0'''  )
						if "pretrained.act_postprocess4.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.3.0'''  )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.3'''   ,     '''neck.reassemble_stage.layers.0.projection'''  )
						if "pretrained.act_postprocess1.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.4'''   ,     '''neck.reassemble_stage.layers.0.resize'''  )
						if "pretrained.act_postprocess2.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.3'''   ,     '''neck.reassemble_stage.layers.1.projection'''  )
						if "pretrained.act_postprocess2.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.4'''   ,     '''neck.reassemble_stage.layers.1.resize'''  )
						if "pretrained.act_postprocess3.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess3.3'''   ,     '''neck.reassemble_stage.layers.2.projection'''  )
						if "pretrained.act_postprocess4.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.3'''   ,     '''neck.reassemble_stage.layers.3.projection'''  )
						if "pretrained.act_postprocess4.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.4'''   ,     '''neck.reassemble_stage.layers.3.resize'''  )
						if "pretrained" in name:
												UpperCAmelCase         =			name.replace('''pretrained'''   ,     '''dpt'''  )
						if "bn" in name:
												UpperCAmelCase         =			name.replace('''bn'''   ,     '''batch_norm'''  )
						if "head" in name:
												UpperCAmelCase         =			name.replace('''head'''   ,     '''head.head'''  )
						if "encoder.norm" in name:
												UpperCAmelCase         =			name.replace('''encoder.norm'''   ,     '''layernorm'''  )
						if "auxlayer" in name:
												UpperCAmelCase         =			name.replace('''auxlayer'''   ,     '''auxiliary_head.head'''  )

						return name


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						for i in range(config.num_hidden_layers  ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												UpperCAmelCase         =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
												UpperCAmelCase         =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""  )
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase         =			in_proj_weight[: config.hidden_size, :]
												UpperCAmelCase         =			in_proj_bias[: config.hidden_size]
												UpperCAmelCase         =			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase         =			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase         =			in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase         =			in_proj_bias[-config.hidden_size :]


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase         =			Image.open(requests.get(A   ,     stream=A  ).raw  )
						return im


@torch.no_grad()
def    lowerCamelCase__   ( A     :    Any   ,     A     :    Optional[int]   ,     A     :    int   ,     A     :    Any  ):




						'''simple docstring'''






						UpperCAmelCase         =			get_dpt_config(A  )
						# load original state_dict from URL
						UpperCAmelCase         =			torch.hub.load_state_dict_from_url(A   ,     map_location='''cpu'''  )
						# remove certain keys
						remove_ignore_keys_(A  )
						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase         =			state_dict.pop(A  )
												UpperCAmelCase         =			val
						# read in qkv matrices
						read_in_q_k_v(A   ,     A  )

						# load HuggingFace model
						UpperCAmelCase         =			DPTForSemanticSegmentation(A  ) if """ade""" in checkpoint_url else DPTForDepthEstimation(A  )
						model.load_state_dict(A  )
						model.eval()

						# Check outputs on an image
						UpperCAmelCase         =			4_80 if """ade""" in checkpoint_url else 3_84
						UpperCAmelCase         =			DPTImageProcessor(size=A  )

						UpperCAmelCase         =			prepare_img()
						UpperCAmelCase         =			image_processor(A   ,     return_tensors='''pt'''  )

						# forward pass
						UpperCAmelCase         =			model(**A  ).logits if """ade""" in checkpoint_url else model(**A  ).predicted_depth

						# Assert logits
						UpperCAmelCase         =			torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]  )
						if "ade" in checkpoint_url:
												UpperCAmelCase         =			torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]  )
						assert outputs.shape == torch.Size(A  )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]   ,     A   ,     atol=1E-4  )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]   ,     A  )
						)

						Path(A  ).mkdir(exist_ok=A  )
						print(f"""Saving model to {pytorch_dump_folder_path}"""  )
						model.save_pretrained(A  )
						print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
						image_processor.save_pretrained(A  )

						if push_to_hub:
												print('''Pushing model to hub...'''  )
												model.push_to_hub(
												    repo_path_or_name=Path(A   ,     A  )   ,     organization='''nielsr'''   ,     commit_message='''Add model'''   ,     use_temp_dir=A   ,     )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(A   ,     A  )   ,     organization='''nielsr'''   ,     commit_message='''Add image processor'''   ,     use_temp_dir=A   ,     )


if __name__ == "__main__":
	_lowercase    :    Optional[Any]			     =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)

	_lowercase    :    int			     =  parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						# for each node and his adjacency list add them and the rank of the node to queue
						# using heapq module the queue will be filled like a Priority Queue
						# heapq works with a min priority queue, so I used -1*len(v) to build it
						for key, value in graph.items():
												# O(log(n))
												heapq.heappush(A   ,     [-1 * len(A  ), (key, value)]  )

						# chosen_vertices = set of chosen vertices
						UpperCAmelCase         =			set()

						# while queue isn't empty and there are still edges
						#   (queue[0][0] is the rank of the node with max rank)
						while queue and queue[0][0] != 0:
												# extract vertex with max rank from queue and add it to chosen_vertices
												UpperCAmelCase         =			heapq.heappop(A  )[1][0]
												chosen_vertices.add(A  )

												# Remove all arcs adjacent to argmax
												for elem in queue:
																		# if v haven't adjacent node, skip
																		if elem[0] == 0:
																								continue
																		# if argmax is reachable from elem
																		# remove argmax from elem's adjacent list and update his rank
																		if argmax in elem[1][1]:
																								UpperCAmelCase         =			elem[1][1].index(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")





