

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__ :     Optional[int]				    =    logging.get_logger(__name__)






def 					__lowercase      (     _A			)     ->						Optional[int]:
      SCREAMING_SNAKE_CASE :      Dict       =   DPTConfig()

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

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE :      List[Any]       =   True

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

      return config, expected_shape






def 					__lowercase      (     _A			)     ->						Optional[int]:
      SCREAMING_SNAKE_CASE :      int       =   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_A						,    _A			)






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

      return name






def 					__lowercase      (     _A						,    _A			)     ->						int:
      for i in range(config.num_hidden_layers			):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE :      str       =   state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"			)
            SCREAMING_SNAKE_CASE :      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
            SCREAMING_SNAKE_CASE :      List[str]       =   in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE :      Dict       =   in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE :      Dict       =   in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE :      Tuple       =   in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE :      List[str]       =   in_proj_bias[-config.hidden_size :]






def 					__lowercase      (     )     ->						Tuple:
      SCREAMING_SNAKE_CASE :      int       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE :      str       =   Image.open(requests.get(_A						,    stream=_A			).raw			)
      return im






@torch.no_grad()
def 					__lowercase      (     _A						,    _A						,    _A						,    _A			)     ->						Tuple:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Any       =   get_dpt_config(_A			)
      # load original state_dict from URL
      SCREAMING_SNAKE_CASE :      Tuple       =   torch.hub.load_state_dict_from_url(_A						,    map_location="""cpu"""			)
      # remove certain keys
      remove_ignore_keys_(_A			)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE :      List[Any]       =   state_dict.pop(_A			)
            SCREAMING_SNAKE_CASE :      List[Any]       =   val
      # read in qkv matrices
      read_in_q_k_v(_A						,    _A			)

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

      # Check outputs on an image
      SCREAMING_SNAKE_CASE :      Optional[int]       =   480 if """ade""" in checkpoint_url else 384
      SCREAMING_SNAKE_CASE :      Any       =   DPTImageProcessor(size=_A			)

      SCREAMING_SNAKE_CASE :      List[Any]       =   prepare_img()
      SCREAMING_SNAKE_CASE :      Tuple       =   image_processor(_A						,    return_tensors="""pt"""			)

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

      # Assert logits
      SCREAMING_SNAKE_CASE :      str       =   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:
            SCREAMING_SNAKE_CASE :      Tuple       =   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(_A			)
      assert (
          torch.allclose(outputs[0, 0, :3, :3]						,    _A						,    atol=1e-4			)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3]						,    _A			)
      )

      Path(_A			).mkdir(exist_ok=_A			)
      print(F"Saving model to {pytorch_dump_folder_path}"			)
      model.save_pretrained(_A			)
      print(F"Saving image processor to {pytorch_dump_folder_path}"			)
      image_processor.save_pretrained(_A			)

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


if __name__ == "__main__":
  UpperCAmelCase__ :     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=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.""",
  )

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







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




