
"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"upernet"




      def __init__(							self   ,       lowercase_=None   ,       lowercase_=5_1_2   ,       lowercase_=0.0_2   ,       lowercase_=[1, 2, 3, 6]   ,       lowercase_=True   ,       lowercase_=0.4   ,       lowercase_=3_8_4   ,       lowercase_=2_5_6   ,       lowercase_=1   ,       lowercase_=False   ,       lowercase_=2_5_5   ,       **lowercase_   ,       )						->					Tuple:
          super().__init__(**lowercase_							)

          if backbone_config is None:
              logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'							)
              UpperCAmelCase									=					CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']							)
          elif isinstance(lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					backbone_config.get('model_type'							)
              UpperCAmelCase									=					CONFIG_MAPPING[backbone_model_type]
              UpperCAmelCase									=					config_class.from_dict(lowercase_							)

          UpperCAmelCase									=					backbone_config
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					pool_scales
          UpperCAmelCase									=					use_auxiliary_head
          UpperCAmelCase									=					auxiliary_loss_weight
          UpperCAmelCase									=					auxiliary_in_channels
          UpperCAmelCase									=					auxiliary_channels
          UpperCAmelCase									=					auxiliary_num_convs
          UpperCAmelCase									=					auxiliary_concat_input
          UpperCAmelCase									=					loss_ignore_index




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.backbone_config.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output





"""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()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    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 "ade" in checkpoint_url:
        UpperCAmelCase									=					True

        UpperCAmelCase									=					150
        UpperCAmelCase									=					'huggingface/label-files'
        UpperCAmelCase									=					'ade20k-id2label.json'
        UpperCAmelCase									=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ) ,				'r' ) )
        UpperCAmelCase									=					{int(lowerCAmelCase ): 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     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[Any]:






    """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' ,				'patch_embeddings' )
    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:
        UpperCAmelCase									=					name.replace('norm1' ,				'layernorm_before' )
    if "norm2" 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' )

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






    """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     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase									=					Image.open(requests.get(lowerCAmelCase ,				stream=lowerCAmelCase ).raw )
    return im




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




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

    # load HuggingFace model
    UpperCAmelCase									=					DPTForSemanticSegmentation(lowerCAmelCase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase )
    model.load_state_dict(lowerCAmelCase )
    model.eval()

    # Check outputs on an image
    UpperCAmelCase									=					480 if 'ade' in checkpoint_url else 384
    UpperCAmelCase									=					DPTImageProcessor(size=lowerCAmelCase )

    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					image_processor(lowerCAmelCase ,				return_tensors='pt' )

    # forward pass
    UpperCAmelCase									=					model(**lowerCAmelCase ).logits if 'ade' in checkpoint_url else model(**lowerCAmelCase ).predicted_depth

    # Assert logits
    UpperCAmelCase									=					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:
        UpperCAmelCase									=					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(lowerCAmelCase )
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,				lowerCAmelCase ,				atol=1E-4 )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,				lowerCAmelCase )
    )

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						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.''',
   )

   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



