




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase		:  List[str]	=   {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   ["PoolFormerFeatureExtractor"]
     _UpperCAmelCase		:  int	=   ["PoolFormerImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  str	=   [
         "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "PoolFormerForImageClassification",
         "PoolFormerModel",
         "PoolFormerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase		:  Optional[int]	=   _LazyModule(__name__, globals()["__file__"], _import_structure)









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		:  Union[str, Any]	=   logging.get_logger(__name__)



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:int							=  DPTConfig(embedding_type="hybrid"      )

  if "large" in checkpoint_url:
    lowercase			:Tuple							=  1024
    lowercase			:Tuple							=  4096
    lowercase			:Union[str, Any]							=  24
    lowercase			:Tuple							=  16
    lowercase			:Optional[Any]							=  [5, 11, 17, 23]
    lowercase			:Optional[Any]							=  [256, 512, 1024, 1024]
    lowercase			:Tuple							=  (1, 384, 384)

  if "nyu" or "midas" in checkpoint_url:
    lowercase			:Any							=  768
    lowercase			:Optional[int]							=  [1, 1, 1, 0.5]
    lowercase			:int							=  [256, 512, 768, 768]
    lowercase			:str							=  150
    lowercase			:Tuple							=  16
    lowercase			:List[str]							=  (1, 384, 384)
    lowercase			:Any							=  False
    lowercase			:Optional[int]							=  "project"

  if "ade" in checkpoint_url:
    lowercase			:Dict							=  True
    lowercase			:str							=  768
    lowercase			:Tuple							=  [1, 1, 1, 0.5]
    lowercase			:Dict							=  150
    lowercase			:Any							=  16
    lowercase			:List[Any]							=  "huggingface/label-files"
    lowercase			:Union[str, Any]							=  "ade20k-id2label.json"
    lowercase			:List[str]							=  json.load(open(cached_download(hf_hub_url(lowerCamelCase,	lowerCamelCase,	repo_type="dataset"      )      ),	"r"      )      )
    lowercase			:Optional[Any]							=  {int(lowerCamelCase      ): v for k, v in idalabel.items()}
    lowercase			:Union[str, Any]							=  idalabel
    lowercase			:Optional[Any]							=  {v: k for k, v in idalabel.items()}
    lowercase			:List[str]							=  [1, 150, 480, 480]

  return config, expected_shape



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:List[str]							=  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(lowerCamelCase,	lowerCamelCase      )



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

  if ".." in name:
    lowercase			:Optional[int]							=  name.replace("..",	"."      )

  if "stem.conv" in name:
    lowercase			:Union[str, Any]							=  name.replace("stem.conv",	"bit.embedder.convolution"      )
  if "blocks" in name:
    lowercase			:List[str]							=  name.replace("blocks",	"layers"      )
  if "convolution" in name and "backbone" in name:
    lowercase			:int							=  name.replace("convolution",	"conv"      )
  if "layer" in name and "backbone" in name:
    lowercase			:List[str]							=  name.replace("layer",	"layers"      )
  if "backbone.bit.encoder.bit" in name:
    lowercase			:Dict							=  name.replace("backbone.bit.encoder.bit",	"backbone.bit"      )
  if "embedder.conv" in name:
    lowercase			:Dict							=  name.replace("embedder.conv",	"embedder.convolution"      )
  if "backbone.bit.encoder.stem.norm" in name:
    lowercase			:str							=  name.replace("backbone.bit.encoder.stem.norm",	"backbone.bit.embedder.norm"      )
  return name



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



def 					UpperCAmelCase__     (   ):
  lowercase			:List[str]							=  "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase			:Any							=  Image.open(requests.get(lowerCamelCase,	stream=lowerCamelCase      ).raw      )
  return im



@torch.no_grad()
def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  lowercase		,			lowercase			:Optional[int]							=  get_dpt_config(lowerCamelCase      )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  lowercase			:List[str]							=  torch.load(lowerCamelCase,	map_location="cpu"      )
  # remove certain keys
  remove_ignore_keys_(lowerCamelCase      )
  # rename keys
  for key in state_dict.copy().keys():
    lowercase			:int							=  state_dict.pop(lowerCamelCase      )
    lowercase			:Dict							=  val
  # read in qkv matrices
  read_in_q_k_v(lowerCamelCase,	lowerCamelCase      )

  # load HuggingFace model
  lowercase			:int							=  DPTForSemanticSegmentation(lowerCamelCase      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase      )
  model.load_state_dict(lowerCamelCase      )
  model.eval()

  # Check outputs on an image
  lowercase			:Union[str, Any]							=  480 if "ade" in checkpoint_url else 384
  lowercase			:Optional[int]							=  DPTImageProcessor(size=lowerCamelCase      )

  lowercase			:Dict							=  prepare_img()
  lowercase			:Optional[Any]							=  image_processor(lowerCamelCase,	return_tensors="pt"      )

  # forward pass
  lowercase			:Any							=  model(**lowerCamelCase      ).logits if "ade" in checkpoint_url else model(**lowerCamelCase      ).predicted_depth

  if show_prediction:
    lowercase			:List[Any]							=  (
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1      ),	size=(image.size[1], image.size[0]),	mode="bicubic",	align_corners=lowerCamelCase,	)
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 255      ).show()

  if pytorch_dump_folder_path is not None:
    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:
    model.push_to_hub("ybelkada/dpt-hybrid-midas"      )
    image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"      )


if __name__ == "__main__":
     _UpperCAmelCase		:  str	=   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		:  List[Any]	=   parser.parse_args()
     convert_dpt_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
     )



