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_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  DPTConfig(embedding_type="hybrid"							)

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

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

      if "ade" in checkpoint_url:
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  [1, 1, 1, 0.5]
            UpperCAmelCase__								=  150
            UpperCAmelCase__								=  16
            UpperCAmelCase__								=  "huggingface/label-files"
            UpperCAmelCase__								=  "ade20k-id2label.json"
            UpperCAmelCase__								=  json.load(open(cached_download(hf_hub_url(__A,  __A,  repo_type="dataset"							)							),  "r"							)							)
            UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
            UpperCAmelCase__								=  idalabel
            UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
            UpperCAmelCase__								=  [1, 150, 480, 480]

      return config, expected_shape




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A							)					->    Optional[int]:







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

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess1.3",  "neck.reassemble_stage.layers.0.projection"							)
      if "pretrained.act_postprocess1.4" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess1.4",  "neck.reassemble_stage.layers.0.resize"							)
      if "pretrained.act_postprocess2.3" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess2.3",  "neck.reassemble_stage.layers.1.projection"							)
      if "pretrained.act_postprocess2.4" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess2.4",  "neck.reassemble_stage.layers.1.resize"							)
      if "pretrained.act_postprocess3.3" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess3.3",  "neck.reassemble_stage.layers.2.projection"							)
      if "pretrained.act_postprocess4.3" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess4.3",  "neck.reassemble_stage.layers.3.projection"							)
      if "pretrained.act_postprocess4.4" in name:
            UpperCAmelCase__								=  name.replace("pretrained.act_postprocess4.4",  "neck.reassemble_stage.layers.3.resize"							)
      if "pretrained" in name:
            UpperCAmelCase__								=  name.replace("pretrained",  "dpt"							)
      if "bn" in name:
            UpperCAmelCase__								=  name.replace("bn",  "batch_norm"							)
      if "head" in name:
            UpperCAmelCase__								=  name.replace("head",  "head.head"							)
      if "encoder.norm" in name:
            UpperCAmelCase__								=  name.replace("encoder.norm",  "layernorm"							)
      if "auxlayer" in name:
            UpperCAmelCase__								=  name.replace("auxlayer",  "auxiliary_head.head"							)
      if "backbone" in name:
            UpperCAmelCase__								=  name.replace("backbone",  "backbone.bit.encoder"							)

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

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




def        lowerCAmelCase_			(					__A,  __A							)					->    str:







      '''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)
            UpperCAmelCase__								=  state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""							)
            UpperCAmelCase__								=  state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""							)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase__								=  in_proj_weight[: config.hidden_size, :]
            UpperCAmelCase__								=  in_proj_bias[: config.hidden_size]
            UpperCAmelCase__								=  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCAmelCase__								=  in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            UpperCAmelCase__								=  in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCAmelCase__								=  in_proj_bias[-config.hidden_size :]




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


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




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A							)					->    Tuple:







      '''simple docstring'''


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

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

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

      UpperCAmelCase__								=  prepare_img()
      UpperCAmelCase__								=  image_processor(__A,  return_tensors="pt"							)

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

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

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

      if pytorch_dump_folder_path is not None:
            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:
            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
      )




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



