






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()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  DPTConfig()

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

      if "ade" in checkpoint_url:
            UpperCamelCase					       =  True

            UpperCamelCase					       =  1_50
            UpperCamelCase					       =  'huggingface/label-files'
            UpperCamelCase					       =  'ade20k-id2label.json'
            UpperCamelCase					       =  json.load(open(cached_download(hf_hub_url(_UpperCamelCase       ,							_UpperCamelCase       ,							repo_type='dataset'))       ,							'r'))
            UpperCamelCase					       =  {int(_UpperCamelCase): v for k, v in idalabel.items()}
            UpperCamelCase					       =  idalabel
            UpperCamelCase					       =  {v: k for k, v in idalabel.items()}
            UpperCamelCase					       =  [1, 1_50, 4_80, 4_80]

      return config, expected_shape
def 		lowercase__  (				_UpperCamelCase) ->			str:
      """simple docstring"""

      UpperCamelCase					       =  ['pretrained.model.head.weight', 'pretrained.model.head.bias']
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase       ,							_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase) ->			Union[str, 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__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """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[int]:
      """simple docstring"""

      UpperCamelCase					       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      UpperCamelCase					       =  Image.open(requests.get(_UpperCamelCase       ,							stream=_UpperCamelCase).raw)
      return im
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """simple docstring"""

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

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

      # Check outputs on an image
      UpperCamelCase					       =  4_80 if 'ade' in checkpoint_url else 3_84
      UpperCamelCase					       =  DPTImageProcessor(size=_UpperCamelCase)

      UpperCamelCase					       =  prepare_img()
      UpperCamelCase					       =  image_processor(_UpperCamelCase       ,							return_tensors='pt')

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

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

      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      print(F'Saving model to {pytorch_dump_folder_path}')
      model.save_pretrained(_UpperCamelCase)
      print(F'Saving image processor to {pytorch_dump_folder_path}')
      image_processor.save_pretrained(_UpperCamelCase)

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


if __name__ == "__main__":
       __magic_name__   :  Optional[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=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.''',
       )

       __magic_name__   :  Optional[int]        =							parser.parse_args()
       convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str = ""   ,						_SCREAMING_SNAKE_CASE      :    bool = False ):






           """simple docstring"""




           UpperCamelCase					       =  {}

           # A node will be a leaf if the tree contains its word
           UpperCamelCase					       =  is_leaf

           UpperCamelCase					       =  prefix







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for q, w in zip(self.prefix   ,						_SCREAMING_SNAKE_CASE ):
                 if q != w:
                       break

                 x += 1

           return self.prefix[:x], self.prefix[x:], word[x:]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    list[str] ):






           """simple docstring"""




           for word in words:
                 self.insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if self.prefix == word:
                 UpperCamelCase					       =  True

           # Case 2: The node has no edges that have a prefix to the word
           # Solution: We create an edge from the current node to a new one
           # containing the word
           elif word[0] not in self.nodes:
                 UpperCamelCase					       =  RadixNode(prefix=_SCREAMING_SNAKE_CASE   ,						is_leaf=_SCREAMING_SNAKE_CASE )

           else:
                 UpperCamelCase					       =  self.nodes[word[0]]
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                       self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                       UpperCamelCase					       =  remaining_prefix

                       UpperCamelCase					       =  self.nodes[matching_string[0]]
                       UpperCamelCase					       =  RadixNode(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  aux_node

                       if remaining_word == "":
                             UpperCamelCase					       =  True
                       else:
                             self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                       return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                       return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                       return incoming_node.find(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                       return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                       return incoming_node.delete(_SCREAMING_SNAKE_CASE )
                 else:
                       # If it is not a leaf, we don't have to delete
                       if not incoming_node.is_leaf:
                             return False
                       else:
                             # We delete the nodes if no edges go from it
                             if len(incoming_node.nodes ) == 0:
                                   del self.nodes[word[0]]
                                   # We merge the current node with its only child
                                   if len(self.nodes ) == 1 and not self.is_leaf:
                                         UpperCamelCase					       =  list(self.nodes.values() )[0]
                                         UpperCamelCase					       =  merging_node.is_leaf
                                         self.prefix += merging_node.prefix
                                         UpperCamelCase					       =  merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                             elif len(incoming_node.nodes ) > 1:
                                   UpperCamelCase					       =  False
                             # If there is 1 edge, we merge it with its child
                             else:
                                   UpperCamelCase					       =  list(incoming_node.nodes.values() )[0]
                                   UpperCamelCase					       =  merging_node.is_leaf
                                   incoming_node.prefix += merging_node.prefix
                                   UpperCamelCase					       =  merging_node.nodes

                             return True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    int = 0 ):






           """simple docstring"""




           if self.prefix != "":
                 print('-' * height   ,						self.prefix   ,						'  (leaf)' if self.is_leaf else '' )

           for value in self.nodes.values():
                 value.print_tree(height + 1 )

def 		lowercase__  (				) ->			bool:
      """simple docstring"""

      UpperCamelCase					       =  'banana bananas bandana band apple all beast'.split()
      UpperCamelCase					       =  RadixNode()
      root.insert_many(_UpperCamelCase)

      assert all(root.find(_UpperCamelCase) for word in words)
      assert not root.find('bandanas')
      assert not root.find('apps')
      root.delete('all')
      assert not root.find('all')
      root.delete('banana')
      assert not root.find('banana')
      assert root.find('bananas')

      return True
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      assert test_trie()
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      UpperCamelCase					       =  RadixNode()
      UpperCamelCase					       =  'banana bananas bandanas bandana band apple all beast'.split()
      root.insert_many(_UpperCamelCase)

      print('Words:'       ,							_UpperCamelCase)
      print('Tree:')
      root.print_tree()


if __name__ == "__main__":
       main()



