






"""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       :	Any             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  DPTConfig()

					if "large" in checkpoint_url:
										a__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16
										a__          =  [5, 11, 17, 23]
										a__          =  [256, 512, 1024, 1024]
										a__          =  (1, 384, 384)

					if "ade" in checkpoint_url:
										a__          =  True

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

					return config, expected_shape





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(lowerCamelCase_ ,     lowerCamelCase_)





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

					return name





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





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





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

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

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

					a__          =  prepare_img()
					a__          =  image_processor(lowerCamelCase_ ,     return_tensors='''pt''')

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

					# Assert logits
					a__          =  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:
										a__          =  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(lowerCamelCase_)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3] ,     lowerCamelCase_ ,     atol=1E-4)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3] ,     lowerCamelCase_)
					)

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

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


if __name__ == "__main__":
	__a       :	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.',
	)

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










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


