





'''simple docstring'''






import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
      import torch.distributed.checkpoint as dist_cp
      from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
      from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
      from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
      from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


__lowerCamelCase						: Union[str, Any]       =   get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=0   ):


  """simple docstring"""

  os.makedirs(SCREAMING_SNAKE_CASE_  ,      exist_ok=SCREAMING_SNAKE_CASE_   )
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE_  ,      fsdp_plugin.state_dict_type  ,      fsdp_plugin.state_dict_config  ,      fsdp_plugin.optim_state_dict_config   ):
    _UpperCamelCase       			=model.state_dict()
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      _UpperCamelCase       			=f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      if accelerator.process_index == 0:
        logger.info(f'''Saving model to {output_model_file}'''   )
        torch.save(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
        logger.info(f'''Model saved to {output_model_file}'''   )
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      _UpperCamelCase       			=(
          f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
          if model_index == 0
          else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
      )
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Saving model to {output_model_file}'''   )
      torch.save(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Model saved to {output_model_file}'''   )
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      f'''{MODEL_NAME}_{model_index}'''   )
      os.makedirs(SCREAMING_SNAKE_CASE_  ,      exist_ok=SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Saving model to {ckpt_dir}'''   )
      _UpperCamelCase       			={'''model''': state_dict}

      dist_cp.save_state_dict(
          state_dict=SCREAMING_SNAKE_CASE_  ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_   )  ,      planner=DefaultSavePlanner()  ,      )
      logger.info(f'''Model saved to {ckpt_dir}'''   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=0   ):


  """simple docstring"""

  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE_  ,      fsdp_plugin.state_dict_type  ,      fsdp_plugin.state_dict_config  ,      fsdp_plugin.optim_state_dict_config   ):
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if type(SCREAMING_SNAKE_CASE_   ) != FSDP and accelerator.process_index != 0:
        if not fsdp_plugin.sync_module_states:
          raise ValueError(
              '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
              '''initializing FSDP object'''   )
        return
      _UpperCamelCase       			=f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Loading model from {input_model_file}'''   )
      _UpperCamelCase       			=torch.load(SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Model loaded from {input_model_file}'''   )
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      _UpperCamelCase       			=(
          f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
          if model_index == 0
          else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
      )
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Loading model from {input_model_file}'''   )
      _UpperCamelCase       			=torch.load(SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Model loaded from {input_model_file}'''   )
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      _UpperCamelCase       			=(
          os.path.join(SCREAMING_SNAKE_CASE_  ,      f'''{MODEL_NAME}_{model_index}'''   )
          if f'''{MODEL_NAME}''' not in input_dir
          else input_dir
      )
      logger.info(f'''Loading model from {ckpt_dir}'''   )
      _UpperCamelCase       			={'''model''': model.state_dict()}
      dist_cp.load_state_dict(
          state_dict=SCREAMING_SNAKE_CASE_  ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_   )  ,      planner=DefaultLoadPlanner()  ,      )
      _UpperCamelCase       			=state_dict['''model''']
      logger.info(f'''Model loaded from {ckpt_dir}'''   )
    model.load_state_dict(SCREAMING_SNAKE_CASE_   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=0   ):


  """simple docstring"""

  os.makedirs(SCREAMING_SNAKE_CASE_  ,      exist_ok=SCREAMING_SNAKE_CASE_   )
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE_  ,      fsdp_plugin.state_dict_type  ,      fsdp_plugin.state_dict_config  ,      fsdp_plugin.optim_state_dict_config   ):
    _UpperCamelCase       			=FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if accelerator.process_index == 0:
        _UpperCamelCase       			=(
            f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
        )
        _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
        logger.info(f'''Saving Optimizer state to {output_optimizer_file}'''   )
        torch.save(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
        logger.info(f'''Optimizer state saved in {output_optimizer_file}'''   )
    else:
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      f'''{OPTIMIZER_NAME}_{optimizer_index}'''   )
      os.makedirs(SCREAMING_SNAKE_CASE_  ,      exist_ok=SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Saving Optimizer state to {ckpt_dir}'''   )
      dist_cp.save_state_dict(
          state_dict={'''optimizer''': optim_state}  ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_   )  ,      planner=DefaultSavePlanner()  ,      )
      logger.info(f'''Optimizer state saved in {ckpt_dir}'''   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=0   ):


  """simple docstring"""

  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE_  ,      fsdp_plugin.state_dict_type  ,      fsdp_plugin.state_dict_config  ,      fsdp_plugin.optim_state_dict_config   ):
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      _UpperCamelCase       			=None
      # below check should work but currently it isn't working (mostly opytorch issue),
      # in the meantime disabling it at the cost of excess memory usage
      # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
      _UpperCamelCase       			=(
          f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
      )
      _UpperCamelCase       			=os.path.join(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''   )
      _UpperCamelCase       			=torch.load(SCREAMING_SNAKE_CASE_   )
      logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''   )
    else:
      _UpperCamelCase       			=(
          os.path.join(SCREAMING_SNAKE_CASE_  ,      f'''{OPTIMIZER_NAME}_{optimizer_index}'''   )
          if f'''{OPTIMIZER_NAME}''' not in input_dir
          else input_dir
      )
      logger.info(f'''Loading Optimizer from {ckpt_dir}'''   )
      _UpperCamelCase       			=load_sharded_optimizer_state_dict(
          model_state_dict=model.state_dict()  ,      optimizer_key='''optimizer'''  ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_   )  ,      )
      _UpperCamelCase       			=optim_state['''optimizer''']
      logger.info(f'''Optimizer loaded from {ckpt_dir}'''   )
    _UpperCamelCase       			=FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
    optimizer.load_state_dict(SCREAMING_SNAKE_CASE_   )













'''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						: Optional[Any]		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=DPTConfig(embedding_type='''hybrid'''   )

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

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

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

  return config, expected_shape


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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'''  ,      ''''''   )
  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 and "backbone" not in name:
    _UpperCamelCase       			=name.replace('''norm1'''  ,      '''layernorm_before'''   )
  if "norm2" in name and "backbone" not 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'''   )
  if "backbone" in name:
    _UpperCamelCase       			=name.replace('''backbone'''  ,      '''backbone.bit.encoder'''   )

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

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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """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 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _UpperCamelCase       			=Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,      stream=__SCREAMING_SNAKE_CASE   ).raw   )
  return im


@torch.no_grad()
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

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

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

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

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

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

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

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

  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						: List[str]		=   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						: Dict		=   parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )






