


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



def     __lowerCAmelCase						(							snake_case	:    Optional[int] )       ->							Union[str, Any]:
   __lowerCamelCase:				List[str]     =	DPTConfig()

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

   if "ade" in checkpoint_url:
      __lowerCamelCase:				int     =	True

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

   return config, expected_shape



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



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Optional[int]:
   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      __lowerCamelCase:				Tuple     =	name.replace("""pretrained.model"""						,      """dpt.encoder""" )
   if "pretrained.model" in name:
      __lowerCamelCase:				Optional[int]     =	name.replace("""pretrained.model"""						,      """dpt.embeddings""" )
   if "patch_embed" in name:
      __lowerCamelCase:				List[str]     =	name.replace("""patch_embed"""						,      """patch_embeddings""" )
   if "pos_embed" in name:
      __lowerCamelCase:				List[str]     =	name.replace("""pos_embed"""						,      """position_embeddings""" )
   if "attn.proj" in name:
      __lowerCamelCase:				int     =	name.replace("""attn.proj"""						,      """attention.output.dense""" )
   if "proj" in name and "project" not in name:
      __lowerCamelCase:				Optional[Any]     =	name.replace("""proj"""						,      """projection""" )
   if "blocks" in name:
      __lowerCamelCase:				Union[str, Any]     =	name.replace("""blocks"""						,      """layer""" )
   if "mlp.fc1" in name:
      __lowerCamelCase:				List[Any]     =	name.replace("""mlp.fc1"""						,      """intermediate.dense""" )
   if "mlp.fc2" in name:
      __lowerCamelCase:				int     =	name.replace("""mlp.fc2"""						,      """output.dense""" )
   if "norm1" in name:
      __lowerCamelCase:				Tuple     =	name.replace("""norm1"""						,      """layernorm_before""" )
   if "norm2" in name:
      __lowerCamelCase:				int     =	name.replace("""norm2"""						,      """layernorm_after""" )
   if "scratch.output_conv" in name:
      __lowerCamelCase:				Optional[int]     =	name.replace("""scratch.output_conv"""						,      """head""" )
   if "scratch" in name:
      __lowerCamelCase:				List[Any]     =	name.replace("""scratch"""						,      """neck""" )
   if "layer1_rn" in name:
      __lowerCamelCase:				List[Any]     =	name.replace("""layer1_rn"""						,      """convs.0""" )
   if "layer2_rn" in name:
      __lowerCamelCase:				Dict     =	name.replace("""layer2_rn"""						,      """convs.1""" )
   if "layer3_rn" in name:
      __lowerCamelCase:				Dict     =	name.replace("""layer3_rn"""						,      """convs.2""" )
   if "layer4_rn" in name:
      __lowerCamelCase:				Union[str, Any]     =	name.replace("""layer4_rn"""						,      """convs.3""" )
   if "refinenet" in name:
      __lowerCamelCase:				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
      __lowerCamelCase:				Tuple     =	name.replace(f'refinenet{layer_idx}'						,      f'fusion_stage.layers.{abs(layer_idx-4 )}' )
   if "out_conv" in name:
      __lowerCamelCase:				Dict     =	name.replace("""out_conv"""						,      """projection""" )
   if "resConfUnit1" in name:
      __lowerCamelCase:				Optional[Any]     =	name.replace("""resConfUnit1"""						,      """residual_layer1""" )
   if "resConfUnit2" in name:
      __lowerCamelCase:				List[str]     =	name.replace("""resConfUnit2"""						,      """residual_layer2""" )
   if "conv1" in name:
      __lowerCamelCase:				List[Any]     =	name.replace("""conv1"""						,      """convolution1""" )
   if "conv2" in name:
      __lowerCamelCase:				int     =	name.replace("""conv2"""						,      """convolution2""" )
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      __lowerCamelCase:				List[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:
      __lowerCamelCase:				Union[str, 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:
      __lowerCamelCase:				List[str]     =	name.replace("""pretrained.act_postprocess3.0.project.0"""						,      """neck.reassemble_stage.readout_projects.2.0""" )
   if "pretrained.act_postprocess4.0.project.0" in name:
      __lowerCamelCase:				str     =	name.replace("""pretrained.act_postprocess4.0.project.0"""						,      """neck.reassemble_stage.readout_projects.3.0""" )
   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      __lowerCamelCase:				List[str]     =	name.replace("""pretrained.act_postprocess1.3"""						,      """neck.reassemble_stage.layers.0.projection""" )
   if "pretrained.act_postprocess1.4" in name:
      __lowerCamelCase:				Any     =	name.replace("""pretrained.act_postprocess1.4"""						,      """neck.reassemble_stage.layers.0.resize""" )
   if "pretrained.act_postprocess2.3" in name:
      __lowerCamelCase:				Union[str, Any]     =	name.replace("""pretrained.act_postprocess2.3"""						,      """neck.reassemble_stage.layers.1.projection""" )
   if "pretrained.act_postprocess2.4" in name:
      __lowerCamelCase:				Optional[Any]     =	name.replace("""pretrained.act_postprocess2.4"""						,      """neck.reassemble_stage.layers.1.resize""" )
   if "pretrained.act_postprocess3.3" in name:
      __lowerCamelCase:				Optional[Any]     =	name.replace("""pretrained.act_postprocess3.3"""						,      """neck.reassemble_stage.layers.2.projection""" )
   if "pretrained.act_postprocess4.3" in name:
      __lowerCamelCase:				Any     =	name.replace("""pretrained.act_postprocess4.3"""						,      """neck.reassemble_stage.layers.3.projection""" )
   if "pretrained.act_postprocess4.4" in name:
      __lowerCamelCase:				Optional[int]     =	name.replace("""pretrained.act_postprocess4.4"""						,      """neck.reassemble_stage.layers.3.resize""" )
   if "pretrained" in name:
      __lowerCamelCase:				Optional[Any]     =	name.replace("""pretrained"""						,      """dpt""" )
   if "bn" in name:
      __lowerCamelCase:				int     =	name.replace("""bn"""						,      """batch_norm""" )
   if "head" in name:
      __lowerCamelCase:				Any     =	name.replace("""head"""						,      """head.head""" )
   if "encoder.norm" in name:
      __lowerCamelCase:				Any     =	name.replace("""encoder.norm"""						,      """layernorm""" )
   if "auxlayer" in name:
      __lowerCamelCase:				Tuple     =	name.replace("""auxlayer"""						,      """auxiliary_head.head""" )

   return name



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



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



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

   # load HuggingFace model
   __lowerCamelCase:				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
   __lowerCamelCase:				List[Any]     =	480 if """ade""" in checkpoint_url else 384
   __lowerCamelCase:				int     =	DPTImageProcessor(size=snake_case )

   __lowerCamelCase:				Any     =	prepare_img()
   __lowerCamelCase:				int     =	image_processor(snake_case						,      return_tensors="""pt""" )

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

   # Assert logits
   __lowerCamelCase:				List[Any]     =	torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] )
   if "ade" in checkpoint_url:
      __lowerCamelCase:				int     =	torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] )
   assert outputs.shape == torch.Size(snake_case )
   assert (
       torch.allclose(outputs[0, 0, :3, :3]						,      snake_case						,      atol=1e-4 )
       if "ade" in checkpoint_url
       else torch.allclose(outputs[0, :3, :3]						,      snake_case )
   )

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


if __name__ == "__main__":
   _A		:				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.''',
   )

   _A		:				Union[str, Any]									=       parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






from __future__ import annotations

from typing import Any



class        a					:





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 0							):
        __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	row, column
        __lowerCamelCase:				Optional[Any]     =	[[default_value for c in range(SCREAMING_SNAKE_CASE_							)] for r in range(SCREAMING_SNAKE_CASE_							)]





     def __str__(    self					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	F'Matrix consist of {self.row} rows and {self.column} columns\n'

        # Make string identifier
        __lowerCamelCase:				Optional[int]     =	0
        for row_vector in self.array:
           for obj in row_vector:
              __lowerCamelCase:				int     =	max(SCREAMING_SNAKE_CASE_    ,	len(str(SCREAMING_SNAKE_CASE_							)							)							)
        __lowerCamelCase:				Union[str, Any]     =	F'%{max_element_length}s'

        # Make string and return
        def single_line(SCREAMING_SNAKE_CASE_					:				list[float]							) -> str:
           nonlocal string_format_identifier
           __lowerCamelCase:				Any     =	"""["""
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector							)
           line += "]"
           return line

        s += "\n".join(single_line(SCREAMING_SNAKE_CASE_							) for row_vector in self.array							)
        return s





     def __repr__(    self					:				Union[str, Any]							):
        return str(self							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        if not (isinstance(SCREAMING_SNAKE_CASE_    ,	(list, tuple)							) and len(SCREAMING_SNAKE_CASE_							) == 2):
           return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
        else:
           return True





     def __getitem__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        return self.array[loc[0]][loc[1]]





     def __setitem__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]    ,	SCREAMING_SNAKE_CASE_					:				float							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	value





     def __add__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == another.row and self.column == another.column

        # Add
        __lowerCamelCase:				Any     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				Tuple     =	self[r, c] + another[r, c]
        return result





     def __neg__(    self					:				Any							):
        __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				int     =	-self[r, c]
        return result





     def __sub__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        return self + (-another)





     def __mul__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int | float | Matrix							):

        if isinstance(SCREAMING_SNAKE_CASE_    ,	(int, float)							):  # Scalar multiplication
           __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
           for r in range(self.row							):
              for c in range(self.column							):
                 __lowerCamelCase:				List[Any]     =	self[r, c] * another
           return result
        elif isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):  # Matrix multiplication
           assert self.column == another.row
           __lowerCamelCase:				Optional[int]     =	Matrix(self.row    ,	another.column							)
           for r in range(self.row							):
              for c in range(another.column							):
                 for i in range(self.column							):
                    result[r, c] += self[r, i] * another[i, c]
           return result
        else:
           __lowerCamelCase:				int     =	F'Unsupported type given for another ({type(SCREAMING_SNAKE_CASE_							)})'
           raise TypeError(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				int     =	Matrix(self.column    ,	self.row							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				List[str]     =	self[r, c]
        return result







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Matrix    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) and isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        __lowerCamelCase:				Optional[Any]     =	v.transpose()
        __lowerCamelCase:				Any     =	(v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
           return None  # It's not invertable
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



   def     __lowerCAmelCase						(							)       ->							None:
      # a^(-1)
      __lowerCamelCase:				int     =	Matrix(3						,      3						,      0 )
      for i in range(3 ):
         __lowerCamelCase:				List[Any]     =	1
      print(f'a^(-1) is {ainv}' )
      # u, v
      __lowerCamelCase:				List[str]     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[Any]     =	1, 2, -3
      __lowerCamelCase:				Dict     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	4, -2, 5
      print(f'u is {u}' )
      print(f'v is {v}' )
      print(f'uv^T is {u * v.transpose()}' )
      # Sherman Morrison
      print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case						,      snake_case )}' )



   def     __lowerCAmelCase						(							)       ->							None:
      import doctest

      doctest.testmod()







   testa()


