





'''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()
a__					:			Dict          =     logging.get_logger(__name__)




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Any:
	snake_case__	       =   DPTConfig(embedding_type='hybrid'							)

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

	if "nyu" or "midas" in checkpoint_url:
		snake_case__	       =   7_68
		snake_case__	       =   [1, 1, 1, 0.5]
		snake_case__	       =   [2_56, 5_12, 7_68, 7_68]
		snake_case__	       =   1_50
		snake_case__	       =   16
		snake_case__	       =   (1, 3_84, 3_84)
		snake_case__	       =   False
		snake_case__	       =   'project'

	if "ade" in checkpoint_url:
		snake_case__	       =   True
		snake_case__	       =   7_68
		snake_case__	       =   [1, 1, 1, 0.5]
		snake_case__	       =   1_50
		snake_case__	       =   16
		snake_case__	       =   'huggingface/label-files'
		snake_case__	       =   'ade20k-id2label.json'
		snake_case__	       =   json.load(open(cached_download(hf_hub_url(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)							)					,				'r'							)							)
		snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
		snake_case__	       =   idalabel
		snake_case__	       =   {v: k for k, v in idalabel.items()}
		snake_case__	       =   [1, 1_50, 4_80, 4_80]

	return config, expected_shape




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   ['pretrained.model.head.weight', 'pretrained.model.head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_							)




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

	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess1.3'					,				'neck.reassemble_stage.layers.0.projection'							)
	if "pretrained.act_postprocess1.4" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess1.4'					,				'neck.reassemble_stage.layers.0.resize'							)
	if "pretrained.act_postprocess2.3" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess2.3'					,				'neck.reassemble_stage.layers.1.projection'							)
	if "pretrained.act_postprocess2.4" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess2.4'					,				'neck.reassemble_stage.layers.1.resize'							)
	if "pretrained.act_postprocess3.3" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess3.3'					,				'neck.reassemble_stage.layers.2.projection'							)
	if "pretrained.act_postprocess4.3" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess4.3'					,				'neck.reassemble_stage.layers.3.projection'							)
	if "pretrained.act_postprocess4.4" in name:
		snake_case__	       =   name.replace('pretrained.act_postprocess4.4'					,				'neck.reassemble_stage.layers.3.resize'							)
	if "pretrained" in name:
		snake_case__	       =   name.replace('pretrained'					,				'dpt'							)
	if "bn" in name:
		snake_case__	       =   name.replace('bn'					,				'batch_norm'							)
	if "head" in name:
		snake_case__	       =   name.replace('head'					,				'head.head'							)
	if "encoder.norm" in name:
		snake_case__	       =   name.replace('encoder.norm'					,				'layernorm'							)
	if "auxlayer" in name:
		snake_case__	       =   name.replace('auxlayer'					,				'auxiliary_head.head'							)
	if "backbone" in name:
		snake_case__	       =   name.replace('backbone'					,				'backbone.bit.encoder'							)

	if ".." in name:
		snake_case__	       =   name.replace('..'					,				'.'							)

	if "stem.conv" in name:
		snake_case__	       =   name.replace('stem.conv'					,				'bit.embedder.convolution'							)
	if "blocks" in name:
		snake_case__	       =   name.replace('blocks'					,				'layers'							)
	if "convolution" in name and "backbone" in name:
		snake_case__	       =   name.replace('convolution'					,				'conv'							)
	if "layer" in name and "backbone" in name:
		snake_case__	       =   name.replace('layer'					,				'layers'							)
	if "backbone.bit.encoder.bit" in name:
		snake_case__	       =   name.replace('backbone.bit.encoder.bit'					,				'backbone.bit'							)
	if "embedder.conv" in name:
		snake_case__	       =   name.replace('embedder.conv'					,				'embedder.convolution'							)
	if "backbone.bit.encoder.stem.norm" in name:
		snake_case__	       =   name.replace('backbone.bit.encoder.stem.norm'					,				'backbone.bit.embedder.norm'							)
	return name




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




def 						__lowerCamelCase			(	)   ->Any:
	snake_case__	       =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case__	       =   Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_							).raw							)
	return im




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Tuple:
	snake_case__       ,  snake_case__	       =   get_dpt_config(UpperCAmelCase_							)
	# load original state_dict from URL
	# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
	snake_case__	       =   torch.load(UpperCAmelCase_					,				map_location='cpu'							)
	# remove certain keys
	remove_ignore_keys_(UpperCAmelCase_							)
	# rename keys
	for key in state_dict.copy().keys():
		snake_case__	       =   state_dict.pop(UpperCAmelCase_							)
		snake_case__	       =   val
	# read in qkv matrices
	read_in_q_k_v(UpperCAmelCase_					,				UpperCAmelCase_							)

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

	# Check outputs on an image
	snake_case__	       =   4_80 if 'ade' in checkpoint_url else 3_84
	snake_case__	       =   DPTImageProcessor(size=UpperCAmelCase_							)

	snake_case__	       =   prepare_img()
	snake_case__	       =   image_processor(UpperCAmelCase_					,				return_tensors='pt'							)

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

	if show_prediction:
		snake_case__	       =   (
		    torch.nn.functional.interpolate(
		        outputs.unsqueeze(1							)					,				size=(image.size[1], image.size[0])					,				mode='bicubic'					,				align_corners=UpperCAmelCase_					,				)
		    .squeeze()
		    .cpu()
		    .numpy()
		)

		Image.fromarray((prediction / prediction.max()) * 2_55							).show()

	if pytorch_dump_folder_path is not None:
		Path(UpperCAmelCase_							).mkdir(exist_ok=UpperCAmelCase_							)
		print(f'''Saving model to {pytorch_dump_folder_path}'''							)
		model.save_pretrained(UpperCAmelCase_							)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
		image_processor.save_pretrained(UpperCAmelCase_							)

	if push_to_hub:
		model.push_to_hub('ybelkada/dpt-hybrid-midas'							)
		image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'							)


if __name__ == "__main__":
			a__					:			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=False,
			    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.''',
			)
			parser.add_argument(
			    '''--show_prediction''',
			    action='''store_true''',
			)

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










'''simple docstring'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



