'''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()
UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  DPTConfig()

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

   if "ade" in checkpoint_url:
      UpperCAmelCase__							:				Tuple   				=  True

      UpperCAmelCase__							:				List[Any]   				=  1_50
      UpperCAmelCase__							:				Optional[int]   				=  """huggingface/label-files"""
      UpperCAmelCase__							:				Union[str, Any]   				=  """ade20k-id2label.json"""
      UpperCAmelCase__							:				Dict   				=  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__							:				str   				=  idalabel
      UpperCAmelCase__							:				Dict   				=  {v: k for k, v in idalabel.items()}
      UpperCAmelCase__							:				Union[str, Any]   				=  [1, 1_50, 4_80, 4_80]

   return config, expected_shape







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase  ,			_lowerCamelCase    )







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

   return name







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







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
   UpperCAmelCase__							:				Tuple   				=  Image.open(requests.get(_lowerCamelCase  ,			stream=_lowerCamelCase    ).raw    )
   return im







@torch.no_grad()
def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Optional[int]  ,			_A					:				Dict  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  get_dpt_config(_lowerCamelCase    )
   # load original state_dict from URL
   UpperCAmelCase__							:				Optional[Any]   				=  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__							:				Dict   				=  state_dict.pop(_lowerCamelCase    )
      UpperCAmelCase__							:				Dict   				=  val
   # read in qkv matrices
   read_in_q_k_v(_lowerCamelCase  ,			_lowerCamelCase    )

   # load HuggingFace model
   UpperCAmelCase__							:				int   				=  DPTForSemanticSegmentation(_lowerCamelCase    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase    )
   model.load_state_dict(_lowerCamelCase    )
   model.eval()

   # Check outputs on an image
   UpperCAmelCase__							:				int   				=  4_80 if """ade""" in checkpoint_url else 3_84
   UpperCAmelCase__							:				str   				=  DPTImageProcessor(size=_lowerCamelCase    )

   UpperCAmelCase__							:				Dict   				=  prepare_img()
   UpperCAmelCase__							:				List[Any]   				=  image_processor(_lowerCamelCase  ,			return_tensors='''pt'''    )

   # forward pass
   UpperCAmelCase__							:				Union[str, Any]   				=  model(**_lowerCamelCase    ).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase    ).predicted_depth

   # Assert logits
   UpperCAmelCase__							:				Optional[Any]   				=  torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]    )
   if "ade" in checkpoint_url:
      UpperCAmelCase__							:				Optional[Any]   				=  torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]    )
   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__     :	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.',
  )

  UpperCamelCase__     :	Any				=			parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_="sayef/fsner-bert-base-uncased"							)			->			Dict:



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



         return self.softmax(T * self.cos(lowerCamelCase_					,lowerCamelCase_							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




