


"""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()
UpperCAmelCase__  							=logging.get_logger(__name__)


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any] ):






 """simple docstring"""







 __lowercase									=	DPTConfig(embedding_type="""hybrid""" )

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

 if "nyu" or "midas" in checkpoint_url:
  __lowercase									=	768
  __lowercase									=	[1, 1, 1, 0.5]
  __lowercase									=	[256, 512, 768, 768]
  __lowercase									=	150
  __lowercase									=	16
  __lowercase									=	(1, 384, 384)
  __lowercase									=	False
  __lowercase									=	"""project"""

 if "ade" in checkpoint_url:
  __lowercase									=	True
  __lowercase									=	768
  __lowercase									=	[1, 1, 1, 0.5]
  __lowercase									=	150
  __lowercase									=	16
  __lowercase									=	"""huggingface/label-files"""
  __lowercase									=	"""ade20k-id2label.json"""
  __lowercase									=	json.load(open(cached_download(hf_hub_url(UpperCamelCase__				,   UpperCamelCase__				,   repo_type="""dataset""" ) )				,   """r""" ) )
  __lowercase									=	{int(UpperCamelCase__ ): 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 lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple ):






 """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
 ):
  __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"""				,   """""" )
 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 and "backbone" not in name:
  __lowercase									=	name.replace("""norm1"""				,   """layernorm_before""" )
 if "norm2" in name and "backbone" not 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""" )
 if "backbone" in name:
  __lowercase									=	name.replace("""backbone"""				,   """backbone.bit.encoder""" )

 if ".." in name:
  __lowercase									=	name.replace(""".."""				,   """.""" )

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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Union[str, Any] ):






 """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)
  __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 lowerCAmelCase_      (				):






 """simple docstring"""







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


@torch.no_grad()
def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: List[Any] ):






 """simple docstring"""







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

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

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

 __lowercase									=	prepare_img()
 __lowercase									=	image_processor(UpperCamelCase__				,   return_tensors="""pt""" )

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

 if show_prediction:
  __lowercase									=	(
      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()) * 255 ).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__":
       UpperCAmelCase__  							=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",
       )

       UpperCAmelCase__  							=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"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
