





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()
SCREAMING_SNAKE_CASE_   :      int			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     DPTConfig()

		if "large" in checkpoint_url:
				__lowercase											=     1_024
				__lowercase											=     4_096
				__lowercase											=     24
				__lowercase											=     16
				__lowercase											=     [5, 11, 17, 23]
				__lowercase											=     [256, 512, 1_024, 1_024]
				__lowercase											=     (1, 384, 384)

		if "ade" in checkpoint_url:
				__lowercase											=     True

				__lowercase											=     150
				__lowercase											=     'huggingface/label-files'
				__lowercase											=     'ade20k-id2label.json'
				__lowercase											=     json.load(open(cached_download(hf_hub_url(snake_case						,   snake_case						,   repo_type='dataset'							)							)						,   'r'							)							)
				__lowercase											=     {int(snake_case							): v for k, v in idalabel.items()}
				__lowercase											=     idalabel
				__lowercase											=     {v: k for k, v in idalabel.items()}
				__lowercase											=     [1, 150, 480, 480]

		return config, expected_shape



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     ['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(snake_case						,   snake_case							)



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

		return name



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



def  SCREAMING_SNAKE_CASE				(      )		->    str:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Union[str, Any]:
		__lowercase						,      __lowercase											=     get_dpt_config(snake_case							)
		# load original state_dict from URL
		__lowercase											=     torch.hub.load_state_dict_from_url(snake_case						,   map_location='cpu'							)
		# remove certain keys
		remove_ignore_keys_(snake_case							)
		# rename keys
		for key in state_dict.copy().keys():
				__lowercase											=     state_dict.pop(snake_case							)
				__lowercase											=     val
		# read in qkv matrices
		read_in_q_k_v(snake_case						,   snake_case							)

		# load HuggingFace model
		__lowercase											=     DPTForSemanticSegmentation(snake_case							) if 'ade' in checkpoint_url else DPTForDepthEstimation(snake_case							)
		model.load_state_dict(snake_case							)
		model.eval()

		# Check outputs on an image
		__lowercase											=     480 if 'ade' in checkpoint_url else 384
		__lowercase											=     DPTImageProcessor(size=snake_case							)

		__lowercase											=     prepare_img()
		__lowercase											=     image_processor(snake_case						,   return_tensors='pt'							)

		# forward pass
		__lowercase											=     model(**snake_case							).logits if 'ade' in checkpoint_url else model(**snake_case							).predicted_depth

		# Assert logits
		__lowercase											=     torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]							)
		if "ade" in checkpoint_url:
				__lowercase											=     torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]							)
		assert outputs.shape == torch.Size(snake_case							)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]						,   snake_case						,   atol=1E-4							)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]						,   snake_case							)
		)

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Tuple			=   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.''',
		)

		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:
		return not any(
		    neighbour == 1 and colored_vertices[i] == color
		    for i, neighbour in enumerate(snake_case							)							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    bool:

		# Base Case
		if index == len(snake_case							):
				return True

		# Recursive Step
		for i in range(snake_case							):
				if valid_coloring(graph[index]						,   snake_case						,   snake_case							):
						# Color current vertex
						__lowercase											=     i
						# Validate coloring
						if util_color(snake_case						,   snake_case						,   snake_case						,   index + 1							):
								return True
						# Backtrack
						__lowercase											=     -1
		return False



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[int]:
		__lowercase											=     [-1] * len(snake_case							)

		if util_color(snake_case						,   snake_case						,   snake_case						,   0							):
				return colored_vertices

		return []




