


class   UpperCamelCase       :


   '''simple docstring'''







   def __init__(  self ,				UpperCamelCase_ ,				UpperCamelCase_=None ,				UpperCamelCase_=None		):
        lowercase_							:Dict			     =							data
        lowercase_							:Dict			     =							previous
        lowercase_							:int			     =							next_node
   def __str__(  self		):
        return f"{self.data}"
   def 		UpperCamelCase	(  self		):
        return self.data
   def 		UpperCamelCase	(  self		):
        return self.next




   def 		UpperCamelCase	(  self		):
        return self.previous






class   UpperCamelCase       :


   '''simple docstring'''







   def __init__(  self ,				UpperCamelCase_		):
        lowercase_							:Optional[Any]			     =							head
   def __iter__(  self		):
        return self




   def 		UpperCamelCase	(  self		):
        if not self.current:
             raise StopIteration
        else:
             lowercase_							:str			     =							self.current.get_data()
             lowercase_							:List[str]			     =							self.current.get_next()
             return value






class   UpperCamelCase       :


   '''simple docstring'''







   def __init__(  self		):
        lowercase_							:str			     =							None  # First node in list
        lowercase_							:Tuple			     =							None  # Last node in list
   def __str__(  self		):
        lowercase_							:int			     =							self.head
        lowercase_							:str			     =							[]
        while current is not None:
             nodes.append(current.get_data()		)
             lowercase_							:Any			     =							current.get_next()
        return " ".join(str(lowerCAmelCase_		) for node in nodes		)
   def __contains__(  self ,				UpperCamelCase_		):
        lowercase_							:Union[str, Any]			     =							self.head
        while current:
             if current.get_data() == value:
                  return True
             lowercase_							:Union[str, Any]			     =							current.get_next()
        return False
   def __iter__(  self		):
        return LinkedListIterator(self.head		)
   def 		UpperCamelCase	(  self		):
        if self.head:
             return self.head.get_data()
        return None
   def 		UpperCamelCase	(  self		):
        if self.tail:
             return self.tail.get_data()
        return None
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        if self.head is None:
             lowercase_							:str			     =							node
             lowercase_							:Optional[int]			     =							node
        else:
             self.insert_before_node(self.head ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        if self.head is None:
             self.set_head(lowerCAmelCase_		)
        else:
             self.insert_after_node(self.tail ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        lowercase_							:List[Any]			     =							Node(lowerCAmelCase_		)
        if self.head is None:
             self.set_head(lowerCAmelCase_		)
        else:
             self.set_tail(lowerCAmelCase_		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_		):
        lowercase_							:str			     =							node
        lowercase_							:Union[str, Any]			     =							node.previous

        if node.get_previous() is None:
             lowercase_							:Dict			     =							node_to_insert
        else:
             lowercase_							:Optional[Any]			     =							node_to_insert

        lowercase_							:Any			     =							node_to_insert
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_		):
        lowercase_							:Dict			     =							node
        lowercase_							:Optional[Any]			     =							node.next

        if node.get_next() is None:
             lowercase_							:Optional[int]			     =							node_to_insert
        else:
             lowercase_							:List[Any]			     =							node_to_insert

        lowercase_							:Optional[Any]			     =							node_to_insert
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_		):
        lowercase_							:str			     =							1
        lowercase_							:Tuple			     =							Node(lowerCAmelCase_		)
        lowercase_							:Dict			     =							self.head
        while node:
             if current_position == position:
                  self.insert_before_node(lowerCAmelCase_ ,				lowerCAmelCase_		)
                  return
             current_position += 1
             lowercase_							:List[str]			     =							node.next
        self.insert_after_node(self.tail ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        lowercase_							:Dict			     =							self.head
        while node:
             if node.get_data() == item:
                  return node
             lowercase_							:Optional[int]			     =							node.get_next()
        raise Exception('''Node not found'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        if (node := self.get_node(lowerCAmelCase_		)) is not None:
             if node == self.head:
                  lowercase_							:Tuple			     =							self.head.get_next()

             if node == self.tail:
                  lowercase_							:Dict			     =							self.tail.get_previous()

             self.remove_node_pointers(lowerCAmelCase_		)
   @staticmethod
   def 		UpperCamelCase	(  UpperCamelCase_		):
        if node.get_next():
             lowercase_							:Any			     =							node.previous

        if node.get_previous():
             lowercase_							:int			     =							node.next

        lowercase_							:str			     =							None
        lowercase_							:int			     =							None




   def 		UpperCamelCase	(  self		):
        return self.head is None




def      UpperCamelCase     (   )       ->				Optional[Any]:
     '''simple docstring'''





     pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()









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






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = DPTConfig()

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

	if "ade" in checkpoint_url:
		A_		:    Tuple						     = True

		A_		:    Optional[Any]						     = 150
		A_		:    str						     = """huggingface/label-files"""
		A_		:    List[Any]						     = """ade20k-id2label.json"""
		A_		:    List[str]						     = json.load(open(cached_download(hf_hub_url(snake_case__ , snake_case__ , repo_type="""dataset"""       )       ) , """r"""       )       )
		A_		:    Dict						     = {int(snake_case__       ): v for k, v in idalabel.items()}
		A_		:    int						     = idalabel
		A_		:    Dict						     = {v: k for k, v in idalabel.items()}
		A_		:    Optional[Any]						     = [1, 150, 480, 480]

	return config, expected_shape






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		A_		:    Any						     = name.replace("""pretrained.model""" , """dpt.encoder"""       )
	if "pretrained.model" in name:
		A_		:    int						     = name.replace("""pretrained.model""" , """dpt.embeddings"""       )
	if "patch_embed" in name:
		A_		:    List[Any]						     = name.replace("""patch_embed""" , """patch_embeddings"""       )
	if "pos_embed" in name:
		A_		:    Optional[int]						     = name.replace("""pos_embed""" , """position_embeddings"""       )
	if "attn.proj" in name:
		A_		:    str						     = name.replace("""attn.proj""" , """attention.output.dense"""       )
	if "proj" in name and "project" not in name:
		A_		:    Tuple						     = name.replace("""proj""" , """projection"""       )
	if "blocks" in name:
		A_		:    Dict						     = name.replace("""blocks""" , """layer"""       )
	if "mlp.fc1" in name:
		A_		:    List[str]						     = name.replace("""mlp.fc1""" , """intermediate.dense"""       )
	if "mlp.fc2" in name:
		A_		:    Any						     = name.replace("""mlp.fc2""" , """output.dense"""       )
	if "norm1" in name:
		A_		:    Tuple						     = name.replace("""norm1""" , """layernorm_before"""       )
	if "norm2" in name:
		A_		:    Optional[int]						     = name.replace("""norm2""" , """layernorm_after"""       )
	if "scratch.output_conv" in name:
		A_		:    Dict						     = name.replace("""scratch.output_conv""" , """head"""       )
	if "scratch" in name:
		A_		:    int						     = name.replace("""scratch""" , """neck"""       )
	if "layer1_rn" in name:
		A_		:    Any						     = name.replace("""layer1_rn""" , """convs.0"""       )
	if "layer2_rn" in name:
		A_		:    List[str]						     = name.replace("""layer2_rn""" , """convs.1"""       )
	if "layer3_rn" in name:
		A_		:    int						     = name.replace("""layer3_rn""" , """convs.2"""       )
	if "layer4_rn" in name:
		A_		:    Optional[Any]						     = name.replace("""layer4_rn""" , """convs.3"""       )
	if "refinenet" in name:
		A_		:    Dict						     = 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_		:    Tuple						     = name.replace(F"""refinenet{layer_idx}""" , F"""fusion_stage.layers.{abs(layer_idx-4       )}"""       )
	if "out_conv" in name:
		A_		:    int						     = name.replace("""out_conv""" , """projection"""       )
	if "resConfUnit1" in name:
		A_		:    str						     = name.replace("""resConfUnit1""" , """residual_layer1"""       )
	if "resConfUnit2" in name:
		A_		:    Optional[int]						     = name.replace("""resConfUnit2""" , """residual_layer2"""       )
	if "conv1" in name:
		A_		:    Any						     = name.replace("""conv1""" , """convolution1"""       )
	if "conv2" in name:
		A_		:    Dict						     = name.replace("""conv2""" , """convolution2"""       )
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		A_		:    List[str]						     = 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_		:    List[str]						     = 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_		:    int						     = 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_		:    Union[str, Any]						     = 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_		:    Union[str, Any]						     = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection"""       )
	if "pretrained.act_postprocess1.4" in name:
		A_		:    Optional[Any]						     = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize"""       )
	if "pretrained.act_postprocess2.3" in name:
		A_		:    List[str]						     = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection"""       )
	if "pretrained.act_postprocess2.4" in name:
		A_		:    Optional[int]						     = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize"""       )
	if "pretrained.act_postprocess3.3" in name:
		A_		:    Any						     = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection"""       )
	if "pretrained.act_postprocess4.3" in name:
		A_		:    int						     = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection"""       )
	if "pretrained.act_postprocess4.4" in name:
		A_		:    Dict						     = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize"""       )
	if "pretrained" in name:
		A_		:    List[str]						     = name.replace("""pretrained""" , """dpt"""       )
	if "bn" in name:
		A_		:    str						     = name.replace("""bn""" , """batch_norm"""       )
	if "head" in name:
		A_		:    Any						     = name.replace("""head""" , """head.head"""       )
	if "encoder.norm" in name:
		A_		:    Optional[Any]						     = name.replace("""encoder.norm""" , """layernorm"""       )
	if "auxlayer" in name:
		A_		:    List[Any]						     = name.replace("""auxlayer""" , """auxiliary_head.head"""       )

	return name






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	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_		:    Union[str, Any]						     = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""       )
		A_		:    Any						     = 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_		:    Tuple						     = in_proj_weight[: config.hidden_size, :]
		A_		:    str						     = in_proj_bias[: config.hidden_size]
		A_		:    List[Any]						     = in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A_		:    List[Any]						     = in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A_		:    int						     = in_proj_weight[
		    -config.hidden_size :, :
		]
		A_		:    List[Any]						     = in_proj_bias[-config.hidden_size :]






def       __UpperCamelCase						(		):
	A_		:    Optional[Any]						     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:    Optional[Any]						     = Image.open(requests.get(snake_case__ , stream=snake_case__       ).raw       )
	return im






@torch.no_grad()
def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_     ,  A_		:    int						     = get_dpt_config(snake_case__       )
	# load original state_dict from URL
	A_		:    Dict						     = 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():
		A_		:    str						     = state_dict.pop(snake_case__       )
		A_		:    str						     = val
	# read in qkv matrices
	read_in_q_k_v(snake_case__ , snake_case__       )

	# load HuggingFace model
	A_		:    Union[str, Any]						     = 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
	A_		:    str						     = 480 if """ade""" in checkpoint_url else 384
	A_		:    List[str]						     = DPTImageProcessor(size=snake_case__       )

	A_		:    Dict						     = prepare_img()
	A_		:    List[Any]						     = image_processor(snake_case__ , return_tensors="""pt"""       )

	# forward pass
	A_		:    Tuple						     = model(**snake_case__       ).logits if """ade""" in checkpoint_url else model(**snake_case__       ).predicted_depth

	# Assert logits
	A_		:    List[str]						     = 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:
		A_		:    str						     = 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(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__":
							_lowerCAmelCase       =				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.",
							)

							_lowerCAmelCase       =				parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

