

'''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()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    DPTConfig(embedding_type='''hybrid'''			)

     if "large" in checkpoint_url:
          __magic_name__						       =    1024
          __magic_name__						       =    4096
          __magic_name__						       =    24
          __magic_name__						       =    16
          __magic_name__						       =    [5, 11, 17, 23]
          __magic_name__						       =    [256, 512, 1024, 1024]
          __magic_name__						       =    (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          __magic_name__						       =    768
          __magic_name__						       =    [1, 1, 1, 0.5]
          __magic_name__						       =    [256, 512, 768, 768]
          __magic_name__						       =    150
          __magic_name__						       =    16
          __magic_name__						       =    (1, 384, 384)
          __magic_name__						       =    False
          __magic_name__						       =    '''project'''

     if "ade" in checkpoint_url:
          __magic_name__						       =    True
          __magic_name__						       =    768
          __magic_name__						       =    [1, 1, 1, 0.5]
          __magic_name__						       =    150
          __magic_name__						       =    16
          __magic_name__						       =    '''huggingface/label-files'''
          __magic_name__						       =    '''ade20k-id2label.json'''
          __magic_name__						       =    json.load(open(cached_download(hf_hub_url(a		,						a		,						repo_type='''dataset'''			)			)		,						'''r'''			)			)
          __magic_name__						       =    {int(a			): v for k, v in idalabel.items()}
          __magic_name__						       =    idalabel
          __magic_name__						       =    {v: k for k, v in idalabel.items()}
          __magic_name__						       =    [1, 150, 480, 480]

     return config, expected_shape




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(a		,						a			)




def 						UpperCamelCase	(			a			)				->     int:






     '''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
     ):
          __magic_name__						       =    name.replace('''pretrained.model'''		,						'''dpt.encoder'''			)
     if "pretrained.model" in name:
          __magic_name__						       =    name.replace('''pretrained.model'''		,						'''dpt.embeddings'''			)
     if "patch_embed" in name:
          __magic_name__						       =    name.replace('''patch_embed'''		,						''''''			)
     if "pos_embed" in name:
          __magic_name__						       =    name.replace('''pos_embed'''		,						'''position_embeddings'''			)
     if "attn.proj" in name:
          __magic_name__						       =    name.replace('''attn.proj'''		,						'''attention.output.dense'''			)
     if "proj" in name and "project" not in name:
          __magic_name__						       =    name.replace('''proj'''		,						'''projection'''			)
     if "blocks" in name:
          __magic_name__						       =    name.replace('''blocks'''		,						'''layer'''			)
     if "mlp.fc1" in name:
          __magic_name__						       =    name.replace('''mlp.fc1'''		,						'''intermediate.dense'''			)
     if "mlp.fc2" in name:
          __magic_name__						       =    name.replace('''mlp.fc2'''		,						'''output.dense'''			)
     if "norm1" in name and "backbone" not in name:
          __magic_name__						       =    name.replace('''norm1'''		,						'''layernorm_before'''			)
     if "norm2" in name and "backbone" not in name:
          __magic_name__						       =    name.replace('''norm2'''		,						'''layernorm_after'''			)
     if "scratch.output_conv" in name:
          __magic_name__						       =    name.replace('''scratch.output_conv'''		,						'''head'''			)
     if "scratch" in name:
          __magic_name__						       =    name.replace('''scratch'''		,						'''neck'''			)
     if "layer1_rn" in name:
          __magic_name__						       =    name.replace('''layer1_rn'''		,						'''convs.0'''			)
     if "layer2_rn" in name:
          __magic_name__						       =    name.replace('''layer2_rn'''		,						'''convs.1'''			)
     if "layer3_rn" in name:
          __magic_name__						       =    name.replace('''layer3_rn'''		,						'''convs.2'''			)
     if "layer4_rn" in name:
          __magic_name__						       =    name.replace('''layer4_rn'''		,						'''convs.3'''			)
     if "refinenet" in name:
          __magic_name__						       =    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
          __magic_name__						       =    name.replace(F'''refinenet{layer_idx}'''		,						F'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
     if "out_conv" in name:
          __magic_name__						       =    name.replace('''out_conv'''		,						'''projection'''			)
     if "resConfUnit1" in name:
          __magic_name__						       =    name.replace('''resConfUnit1'''		,						'''residual_layer1'''			)
     if "resConfUnit2" in name:
          __magic_name__						       =    name.replace('''resConfUnit2'''		,						'''residual_layer2'''			)
     if "conv1" in name:
          __magic_name__						       =    name.replace('''conv1'''		,						'''convolution1'''			)
     if "conv2" in name:
          __magic_name__						       =    name.replace('''conv2'''		,						'''convolution2'''			)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess1.0.project.0'''		,						'''neck.reassemble_stage.readout_projects.0.0'''			)
     if "pretrained.act_postprocess2.0.project.0" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess2.0.project.0'''		,						'''neck.reassemble_stage.readout_projects.1.0'''			)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess3.0.project.0'''		,						'''neck.reassemble_stage.readout_projects.2.0'''			)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess4.0.project.0'''		,						'''neck.reassemble_stage.readout_projects.3.0'''			)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess1.3'''		,						'''neck.reassemble_stage.layers.0.projection'''			)
     if "pretrained.act_postprocess1.4" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess1.4'''		,						'''neck.reassemble_stage.layers.0.resize'''			)
     if "pretrained.act_postprocess2.3" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess2.3'''		,						'''neck.reassemble_stage.layers.1.projection'''			)
     if "pretrained.act_postprocess2.4" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess2.4'''		,						'''neck.reassemble_stage.layers.1.resize'''			)
     if "pretrained.act_postprocess3.3" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess3.3'''		,						'''neck.reassemble_stage.layers.2.projection'''			)
     if "pretrained.act_postprocess4.3" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess4.3'''		,						'''neck.reassemble_stage.layers.3.projection'''			)
     if "pretrained.act_postprocess4.4" in name:
          __magic_name__						       =    name.replace('''pretrained.act_postprocess4.4'''		,						'''neck.reassemble_stage.layers.3.resize'''			)
     if "pretrained" in name:
          __magic_name__						       =    name.replace('''pretrained'''		,						'''dpt'''			)
     if "bn" in name:
          __magic_name__						       =    name.replace('''bn'''		,						'''batch_norm'''			)
     if "head" in name:
          __magic_name__						       =    name.replace('''head'''		,						'''head.head'''			)
     if "encoder.norm" in name:
          __magic_name__						       =    name.replace('''encoder.norm'''		,						'''layernorm'''			)
     if "auxlayer" in name:
          __magic_name__						       =    name.replace('''auxlayer'''		,						'''auxiliary_head.head'''			)
     if "backbone" in name:
          __magic_name__						       =    name.replace('''backbone'''		,						'''backbone.bit.encoder'''			)

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

     if "stem.conv" in name:
          __magic_name__						       =    name.replace('''stem.conv'''		,						'''bit.embedder.convolution'''			)
     if "blocks" in name:
          __magic_name__						       =    name.replace('''blocks'''		,						'''layers'''			)
     if "convolution" in name and "backbone" in name:
          __magic_name__						       =    name.replace('''convolution'''		,						'''conv'''			)
     if "layer" in name and "backbone" in name:
          __magic_name__						       =    name.replace('''layer'''		,						'''layers'''			)
     if "backbone.bit.encoder.bit" in name:
          __magic_name__						       =    name.replace('''backbone.bit.encoder.bit'''		,						'''backbone.bit'''			)
     if "embedder.conv" in name:
          __magic_name__						       =    name.replace('''embedder.conv'''		,						'''embedder.convolution'''			)
     if "backbone.bit.encoder.stem.norm" in name:
          __magic_name__						       =    name.replace('''backbone.bit.encoder.stem.norm'''		,						'''backbone.bit.embedder.norm'''			)
     return name




def 						UpperCamelCase	(			a		,						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)
          __magic_name__						       =    state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
          __magic_name__						       =    state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __magic_name__						       =    in_proj_weight[: config.hidden_size, :]
          __magic_name__						       =    in_proj_bias[: config.hidden_size]
          __magic_name__						       =    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __magic_name__						       =    in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __magic_name__						       =    in_proj_weight[
              -config.hidden_size :, :
          ]
          __magic_name__						       =    in_proj_bias[-config.hidden_size :]




def 						UpperCamelCase	(			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a			)				->     Union[str, Any]:






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

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

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

     __magic_name__						       =    prepare_img()
     __magic_name__						       =    image_processor(a		,						return_tensors='''pt'''			)

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

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

          Image.fromarray((prediction / prediction.max()) * 255			).show()

     if pytorch_dump_folder_path is not None:
          Path(a			).mkdir(exist_ok=a			)
          print(F'''Saving model to {pytorch_dump_folder_path}'''			)
          model.save_pretrained(a			)
          print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
          image_processor.save_pretrained(a			)

     if push_to_hub:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)


if __name__ == "__main__":
      _lowerCAmelCase         =						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",
      )

      _lowerCAmelCase         =						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 __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    0
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   int      ):
      return self.head == self.tail



 def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
      self.data.append(a__      )
      __magic_name__						       =    self.tail + 1



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.data[self.head]
      __magic_name__						       =    self.head + 1
      return ret



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.tail - self.head



 def 			snake_case__  (   self		:   List[Any]      ):
      print(self.data      )
      print('''**************'''      )
      print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   Any      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    1



 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.data



 def 			snake_case__  (   self		:   List[Any]      ):
      return self.left



 def 			snake_case__  (   self		:   Tuple      ):
      return self.right



 def 			snake_case__  (   self		:   Any      ):
      return self.height



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
      __magic_name__						       =    data



 def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
      __magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     if node is None:
          return 0
     return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     if a > b:
          return a
     return b




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''left rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()			)
     ret.set_right(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''right rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()			)
     ret.set_left(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(a			)			)
     return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(a			)			)
     return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     if node is None:
          return MyNode(a			)
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()		,						a			)			)
          if (
              get_height(node.get_left()			) - get_height(node.get_right()			) == 2
          ):  # an unbalance detected
               __magic_name__						       =    node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    __magic_name__						       =    right_rotation(a			)
               else:
                    __magic_name__						       =    lr_rotation(a			)
     else:
          node.set_right(insert_node(node.get_right()		,						a			)			)
          if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
               __magic_name__						       =    node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    __magic_name__						       =    rl_rotation(a			)
               else:
                    __magic_name__						       =    left_rotation(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     return node




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_right()
          if right_child is None:
               break
          __magic_name__						       =    right_child
     return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_left()
          if left_child is None:
               break
          __magic_name__						       =    left_child
     return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     __magic_name__						       =    root.get_left()
     __magic_name__						       =    root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               __magic_name__						       =    get_left_most(a			)
               root.set_data(a			)
               root.set_right(del_node(a		,						a			)			)
          elif left_child is not None:
               __magic_name__						       =    left_child
          elif right_child is not None:
               __magic_name__						       =    right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print('''No such data'''			)
               return root
          else:
               root.set_left(del_node(a		,						a			)			)
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(a		,						a			)			)

     if get_height(a			) - get_height(a			) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
               __magic_name__						       =    left_rotation(a			)
          else:
               __magic_name__						       =    rl_rotation(a			)
     elif get_height(a			) - get_height(a			) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
               __magic_name__						       =    right_rotation(a			)
          else:
               __magic_name__						       =    lr_rotation(a			)
     __magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
     root.set_height(a			)
     return root






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]      ):
      __magic_name__						       =    None



 def 			snake_case__  (   self		:   List[Any]      ):
      return get_height(self.root      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
      print('''insert:''' + str(a__      )      )
      __magic_name__						       =    insert_node(self.root			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
      print('''delete:''' + str(a__      )      )
      if self.root is None:
           print('''Tree is empty!'''      )
           return
      __magic_name__						       =    del_node(self.root			,				a__      )



 def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
      __magic_name__						       =    ''''''
      __magic_name__						       =    MyQueue()
      q.push(self.root      )
      __magic_name__						       =    self.get_height()
      if layer == 0:
           return output
      __magic_name__						       =    0
      while not q.is_empty():
           __magic_name__						       =    q.pop()
           __magic_name__						       =    ''' ''' * int(math.pow(2			,				layer - 1      )      )
           output += space
           if node is None:
                output += "*"
                q.push(a__      )
                q.push(a__      )
           else:
                output += str(node.get_data()      )
                q.push(node.get_left()      )
                q.push(node.get_right()      )
           output += space
           __magic_name__						       =    cnt + 1
           for i in range(100      ):
                if cnt == math.pow(2			,				a__      ) - 1:
                     __magic_name__						       =    layer - 1
                     if layer == 0:
                          output += "\n*************************************"
                          return output
                     output += "\n"
                     break
      output += "\n*************************************"
      return output




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     import doctest

     doctest.testmod()


if __name__ == "__main__":
      _test()
      _lowerCAmelCase         =						AVLtree()
      _lowerCAmelCase         =						list(range(10))
      random.shuffle(lst)
      for i in lst:
            t.insert(i)
            print(str(t))
      random.shuffle(lst)
      for i in lst:
            t.del_node(i)
            print(str(t))






