





'''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()
__magic_name__   :						str    =			logging.get_logger(__name__)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				DPTConfig(embedding_type="hybrid"       )

  if "large" in checkpoint_url:
    _snake_case     		=				10_24
    _snake_case     		=				40_96
    _snake_case     		=				24
    _snake_case     		=				16
    _snake_case     		=				[5, 11, 17, 23]
    _snake_case     		=				[2_56, 5_12, 10_24, 10_24]
    _snake_case     		=				(1, 3_84, 3_84)

  if "nyu" or "midas" in checkpoint_url:
    _snake_case     		=				7_68
    _snake_case     		=				[1, 1, 1, 0.5]
    _snake_case     		=				[2_56, 5_12, 7_68, 7_68]
    _snake_case     		=				1_50
    _snake_case     		=				16
    _snake_case     		=				(1, 3_84, 3_84)
    _snake_case     		=				False
    _snake_case     		=				"project"

  if "ade" in checkpoint_url:
    _snake_case     		=				True
    _snake_case     		=				7_68
    _snake_case     		=				[1, 1, 1, 0.5]
    _snake_case     		=				1_50
    _snake_case     		=				16
    _snake_case     		=				"huggingface/label-files"
    _snake_case     		=				"ade20k-id2label.json"
    _snake_case     		=				json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )       )					,	"r"       )       )
    _snake_case     		=				{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
    _snake_case     		=				idalabel
    _snake_case     		=				{v: k for k, v in idalabel.items()}
    _snake_case     		=				[1, 1_50, 4_80, 4_80]

  return config, expected_shape




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







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

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

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







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




def 				snake_case_      (		):







  '''simple docstring'''



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




@torch.no_grad()
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

  # Check outputs on an image
  _snake_case     		=				4_80 if "ade" in checkpoint_url else 3_84
  _snake_case     		=				DPTImageProcessor(size=SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				prepare_img()
  _snake_case     		=				image_processor(SCREAMING_SNAKE_CASE__					,	return_tensors="pt"       )

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

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

    Image.fromarray((prediction / prediction.max()) * 2_55       ).show()

  if pytorch_dump_folder_path is not None:
    Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
    print(f'''Saving model to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(SCREAMING_SNAKE_CASE__       )
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

  if push_to_hub:
    model.push_to_hub("ybelkada/dpt-hybrid-midas"       )
    image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"       )


if __name__ == "__main__":
 __magic_name__   :						Union[str, 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=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""",
 )

 __magic_name__   :						Optional[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
 )









'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


__magic_name__   :						List[str]    =			(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       ), f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mrpc":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "sts-b":
    return pearson_and_spearman(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "qqp":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''       )
  if task_name == "xnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )


