





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()
_a				:	List[str]           =       logging.get_logger(__name__)



def  UpperCamelCase__     (   _A:    int				):




      '''simple docstring'''







      __lowerCamelCase				   =       DPTConfig()

      if "large" in checkpoint_url:
            __lowerCamelCase				   =       1024
            __lowerCamelCase				   =       4096
            __lowerCamelCase				   =       24
            __lowerCamelCase				   =       16
            __lowerCamelCase				   =       [5, 11, 17, 23]
            __lowerCamelCase				   =       [256, 512, 1024, 1024]
            __lowerCamelCase				   =       (1, 384, 384)

      if "ade" in checkpoint_url:
            __lowerCamelCase				   =       True

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

      return config, expected_shape



def  UpperCamelCase__     (   _A:    Optional[Any]				):




      '''simple docstring'''







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



def  UpperCamelCase__     (   _A:    str				):




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

      return name



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



def  UpperCamelCase__     (   ):




      '''simple docstring'''







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



@torch.no_grad()
def  UpperCamelCase__     (   _A:    int							,    _A:    Optional[int]							,    _A:    Optional[int]							,    _A:    List[Any]				):




      '''simple docstring'''







      __lowerCamelCase				   =       get_dpt_config(__A				)
      # load original state_dict from URL
      __lowerCamelCase				   =       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():
            __lowerCamelCase				   =       state_dict.pop(__A				)
            __lowerCamelCase				   =       val
      # read in qkv matrices
      read_in_q_k_v(__A							,    __A				)

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

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

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

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

      # Assert logits
      __lowerCamelCase				   =       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:
            __lowerCamelCase				   =       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__":
   _a				:	int           =       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.',
   )

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














from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      			=						'''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''

UpperCAmelCase__      			=						'''
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric("mean_iou")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
'''

UpperCAmelCase__      			=						'''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					if label_map is not None:
										for old_id, new_id in label_map.items():
															__a					:						str      =					new_id

    # turn into Numpy arrays
					__a					:						Union[str, Any]      =					np.array(__A      )
					__a					:						Any      =					np.array(__A      )

					if reduce_labels:
										__a					:						Dict      =					255
										__a					:						Union[str, Any]      =					label - 1
										__a					:						int      =					255

					__a					:						Optional[Any]      =					label != ignore_index
					__a					:						int      =					np.not_equal(__A				,       __A      )
					__a					:						str      =					pred_label[mask]
					__a					:						List[str]      =					np.array(__A      )[mask]

					__a					:						Optional[int]      =					pred_label[pred_label == label]

					__a					:						Dict      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						Union[str, Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						List[Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]

					__a					:						Optional[int]      =					area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						List[str]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[int]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					for result, gt_seg_map in zip(__A				,       __A      ):
										__a      ,			__a      ,			__a      ,			__a					:						Dict      =					intersect_and_union(
										    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )
										total_area_intersect += area_intersect
										total_area_union += area_union
										total_area_pred_label += area_pred_label
										total_area_label += area_label
					return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = None				,       __A = False				,       )				->						Optional[int]:



					"""simple docstring"""
					__a      ,			__a      ,			__a      ,			__a					:						Optional[int]      =					total_intersect_and_union(
					    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )

					# compute metrics
					__a					:						Any      =					{}

					__a					:						str      =					total_area_intersect.sum() / total_area_label.sum()
					__a					:						List[Any]      =					total_area_intersect / total_area_union
					__a					:						Union[str, Any]      =					total_area_intersect / total_area_label

					__a					:						Optional[int]      =					np.nanmean(__A      )
					__a					:						str      =					np.nanmean(__A      )
					__a					:						List[str]      =					all_acc
					__a					:						Dict      =					iou
					__a					:						Union[str, Any]      =					acc

					if nan_to_num is not None:
										__a					:						Tuple      =					{metric: np.nan_to_num(__A				,       nan=__A      ) for metric, metric_value in metrics.items()}

					return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,							_KWARGS_DESCRIPTION      )
class 							snake_case_       (    datasets.Metric      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''simple docstring'''



											return datasets.MetricInfo(
											    description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
											        # 1st Seq - height dim, 2nd - width dim
											        {
											            "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"   )   )   ),
											            "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"   )   )   ),
											        }   ) ,					reference_urls=[
											        "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
											    ] ,					)



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					bool ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					Optional[Dict[int, int]] = None ,					__UpperCAmelCase:					bool = False ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					mean_iou(
											    results=__UpperCAmelCase ,					gt_seg_maps=__UpperCAmelCase ,					num_labels=__UpperCAmelCase ,					ignore_index=__UpperCAmelCase ,					nan_to_num=__UpperCAmelCase ,					label_map=__UpperCAmelCase ,					reduce_labels=__UpperCAmelCase ,					)
											return iou_result





