






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     (							__lowerCAmelCase							:					Dict							)     ->  List[str]:
   __UpperCamelCase	:			int					=					DPTConfig()

   if "large" in checkpoint_url:
      __UpperCamelCase	:			List[Any]					=					1024
      __UpperCamelCase	:			List[Any]					=					4096
      __UpperCamelCase	:			Any					=					24
      __UpperCamelCase	:			Optional[Any]					=					16
      __UpperCamelCase	:			Tuple					=					[5, 11, 17, 23]
      __UpperCamelCase	:			Dict					=					[256, 512, 1024, 1024]
      __UpperCamelCase	:			Optional[Any]					=					(1, 384, 384)

   if "ade" in checkpoint_url:
      __UpperCamelCase	:			Dict					=					True

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

   return config, expected_shape


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  Any:
   __UpperCamelCase	:			Tuple					=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase					,							__lowerCAmelCase							)


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

   return name


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  Optional[Any]:
   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	:			Tuple					=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
      __UpperCamelCase	:			int					=					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	:			Tuple					=					in_proj_weight[: config.hidden_size, :]
      __UpperCamelCase	:			List[str]					=					in_proj_bias[: config.hidden_size]
      __UpperCamelCase	:			List[str]					=					in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __UpperCamelCase	:			Tuple					=					in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __UpperCamelCase	:			List[Any]					=					in_proj_weight[
          -config.hidden_size :, :
      ]
      __UpperCamelCase	:			str					=					in_proj_bias[-config.hidden_size :]


def 				__lowerCamelCase     (							)     ->  str:
   __UpperCamelCase	:			List[Any]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __UpperCamelCase	:			Tuple					=					Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase							).raw							)
   return im


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  List[str]:
   __UpperCamelCase	,     __UpperCamelCase	:			str					=					get_dpt_config(__lowerCAmelCase							)
   # load original state_dict from URL
   __UpperCamelCase	:			int					=					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	:			List[str]					=					state_dict.pop(__lowerCAmelCase							)
      __UpperCamelCase	:			Dict					=					val
   # read in qkv matrices
   read_in_q_k_v(__lowerCAmelCase					,							__lowerCAmelCase							)

   # load HuggingFace model
   __UpperCamelCase	:			Union[str, Any]					=					DPTForSemanticSegmentation(__lowerCAmelCase							) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase							)
   model.load_state_dict(__lowerCAmelCase							)
   model.eval()

   # Check outputs on an image
   __UpperCamelCase	:			List[Any]					=					480 if """ade""" in checkpoint_url else 384
   __UpperCamelCase	:			List[str]					=					DPTImageProcessor(size=__lowerCAmelCase							)

   __UpperCamelCase	:			int					=					prepare_img()
   __UpperCamelCase	:			Optional[Any]					=					image_processor(__lowerCAmelCase					,							return_tensors="""pt"""							)

   # forward pass
   __UpperCamelCase	:			Dict					=					model(**__lowerCAmelCase							).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase							).predicted_depth

   # Assert logits
   __UpperCamelCase	:			str					=					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	:			List[str]					=					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__":
       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=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.',
       )

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











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



