





'''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()
lowercase__   :				List[Any]      =   logging.get_logger(__name__)






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					Union[str, Any]:
     __A						:			List[Any]								=	DPTConfig(embedding_type='hybrid'      )

     if "large" in checkpoint_url:
          __A						:			Optional[Any]								=	10_24
          __A						:			Optional[int]								=	40_96
          __A						:			Optional[int]								=	24
          __A						:			Tuple								=	16
          __A						:			Tuple								=	[5, 11, 17, 23]
          __A						:			List[Any]								=	[2_56, 5_12, 10_24, 10_24]
          __A						:			List[str]								=	(1, 3_84, 3_84)

     if "nyu" or "midas" in checkpoint_url:
          __A						:			Any								=	7_68
          __A						:			Union[str, Any]								=	[1, 1, 1, 0.5]
          __A						:			Any								=	[2_56, 5_12, 7_68, 7_68]
          __A						:			Optional[Any]								=	1_50
          __A						:			Optional[int]								=	16
          __A						:			List[Any]								=	(1, 3_84, 3_84)
          __A						:			Any								=	False
          __A						:			Any								=	'project'

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

     return config, expected_shape






def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Tuple:
     __A						:			Union[str, Any]								=	['pretrained.model.head.weight', 'pretrained.model.head.bias']
     for k in ignore_keys:
          state_dict.pop(__snake_case			,							__snake_case      )






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

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __A						:			List[Any]								=	name.replace('pretrained.act_postprocess1.3'			,							'neck.reassemble_stage.layers.0.projection'      )
     if "pretrained.act_postprocess1.4" in name:
          __A						:			List[Any]								=	name.replace('pretrained.act_postprocess1.4'			,							'neck.reassemble_stage.layers.0.resize'      )
     if "pretrained.act_postprocess2.3" in name:
          __A						:			Optional[Any]								=	name.replace('pretrained.act_postprocess2.3'			,							'neck.reassemble_stage.layers.1.projection'      )
     if "pretrained.act_postprocess2.4" in name:
          __A						:			Optional[int]								=	name.replace('pretrained.act_postprocess2.4'			,							'neck.reassemble_stage.layers.1.resize'      )
     if "pretrained.act_postprocess3.3" in name:
          __A						:			List[Any]								=	name.replace('pretrained.act_postprocess3.3'			,							'neck.reassemble_stage.layers.2.projection'      )
     if "pretrained.act_postprocess4.3" in name:
          __A						:			int								=	name.replace('pretrained.act_postprocess4.3'			,							'neck.reassemble_stage.layers.3.projection'      )
     if "pretrained.act_postprocess4.4" in name:
          __A						:			List[Any]								=	name.replace('pretrained.act_postprocess4.4'			,							'neck.reassemble_stage.layers.3.resize'      )
     if "pretrained" in name:
          __A						:			List[Any]								=	name.replace('pretrained'			,							'dpt'      )
     if "bn" in name:
          __A						:			Union[str, Any]								=	name.replace('bn'			,							'batch_norm'      )
     if "head" in name:
          __A						:			Any								=	name.replace('head'			,							'head.head'      )
     if "encoder.norm" in name:
          __A						:			List[Any]								=	name.replace('encoder.norm'			,							'layernorm'      )
     if "auxlayer" in name:
          __A						:			Dict								=	name.replace('auxlayer'			,							'auxiliary_head.head'      )
     if "backbone" in name:
          __A						:			List[Any]								=	name.replace('backbone'			,							'backbone.bit.encoder'      )

     if ".." in name:
          __A						:			Tuple								=	name.replace('..'			,							'.'      )

     if "stem.conv" in name:
          __A						:			Any								=	name.replace('stem.conv'			,							'bit.embedder.convolution'      )
     if "blocks" in name:
          __A						:			List[Any]								=	name.replace('blocks'			,							'layers'      )
     if "convolution" in name and "backbone" in name:
          __A						:			Any								=	name.replace('convolution'			,							'conv'      )
     if "layer" in name and "backbone" in name:
          __A						:			Dict								=	name.replace('layer'			,							'layers'      )
     if "backbone.bit.encoder.bit" in name:
          __A						:			Any								=	name.replace('backbone.bit.encoder.bit'			,							'backbone.bit'      )
     if "embedder.conv" in name:
          __A						:			Optional[Any]								=	name.replace('embedder.conv'			,							'embedder.convolution'      )
     if "backbone.bit.encoder.stem.norm" in name:
          __A						:			List[str]								=	name.replace('backbone.bit.encoder.stem.norm'			,							'backbone.bit.embedder.norm'      )
     return name






def 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			List[Any]      )					->					Optional[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)
          __A						:			Dict								=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'      )
          __A						:			Optional[Any]								=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'      )
          # next, add query, keys and values (in that order) to the state dict
          __A						:			Optional[Any]								=	in_proj_weight[: config.hidden_size, :]
          __A						:			List[str]								=	in_proj_bias[: config.hidden_size]
          __A						:			str								=	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __A						:			List[Any]								=	in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __A						:			Dict								=	in_proj_weight[
              -config.hidden_size :, :
          ]
          __A						:			Any								=	in_proj_bias[-config.hidden_size :]






def 	_lowerCAmelCase      (  )					->					List[str]:
     __A						:			Tuple								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						:			List[str]								=	Image.open(requests.get(__snake_case			,							stream=__snake_case      ).raw      )
     return im






@torch.no_grad()
def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Dict			,							__snake_case  :			int			,							__snake_case  :			Optional[int]			,							__snake_case  :			List[str]      )					->					List[Any]:
     __A			,__A						:			Any								=	get_dpt_config(__snake_case      )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     __A						:			Dict								=	torch.load(__snake_case			,							map_location='cpu'      )
     # remove certain keys
     remove_ignore_keys_(__snake_case      )
     # rename keys
     for key in state_dict.copy().keys():
          __A						:			List[str]								=	state_dict.pop(__snake_case      )
          __A						:			List[Any]								=	val
     # read in qkv matrices
     read_in_q_k_v(__snake_case			,							__snake_case      )

     # load HuggingFace model
     __A						:			List[str]								=	DPTForSemanticSegmentation(__snake_case      ) if 'ade' in checkpoint_url else DPTForDepthEstimation(__snake_case      )
     model.load_state_dict(__snake_case      )
     model.eval()

     # Check outputs on an image
     __A						:			Optional[Any]								=	4_80 if 'ade' in checkpoint_url else 3_84
     __A						:			Dict								=	DPTImageProcessor(size=__snake_case      )

     __A						:			Optional[int]								=	prepare_img()
     __A						:			List[str]								=	image_processor(__snake_case			,							return_tensors='pt'      )

     # forward pass
     __A						:			Any								=	model(**__snake_case      ).logits if 'ade' in checkpoint_url else model(**__snake_case      ).predicted_depth

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

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

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

  lowercase__   :				Union[str, 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'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )