





'''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()
lowercase__    :      int						=    logging.get_logger(__name__)






def        _lowerCAmelCase			(  __snake_case  : Optional[int]			)     ->     Any:
     __A						: Union[str, Any]      =       DPTConfig(embedding_type='hybrid'			)

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

     if "nyu" or "midas" in checkpoint_url:
          __A						: Optional[int]      =       7_68
          __A						: str      =       [1, 1, 1, 0.5]
          __A						: int      =       [2_56, 5_12, 7_68, 7_68]
          __A						: List[Any]      =       1_50
          __A						: Dict      =       16
          __A						: List[Any]      =       (1, 3_84, 3_84)
          __A						: Optional[Any]      =       False
          __A						: Optional[Any]      =       'project'

     if "ade" in checkpoint_url:
          __A						: List[Any]      =       True
          __A						: int      =       7_68
          __A						: Any      =       [1, 1, 1, 0.5]
          __A						: List[Any]      =       1_50
          __A						: Union[str, Any]      =       16
          __A						: Dict      =       'huggingface/label-files'
          __A						: Optional[int]      =       'ade20k-id2label.json'
          __A						: Union[str, Any]      =       json.load(open(cached_download(hf_hub_url(__snake_case ,							__snake_case ,							repo_type='dataset'			)			) ,							'r'			)			)
          __A						: Optional[Any]      =       {int(__snake_case			): v for k, v in idalabel.items()}
          __A						: str      =       idalabel
          __A						: Any      =       {v: k for k, v in idalabel.items()}
          __A						: Optional[int]      =       [1, 1_50, 4_80, 4_80]

     return config, expected_shape






def        _lowerCAmelCase			(  __snake_case  : Optional[int]			)     ->     Dict:
     __A						: 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  : Dict			)     ->     str:
     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          __A						: Optional[int]      =       name.replace('pretrained.model' ,							'dpt.encoder'			)
     if "pretrained.model" in name:
          __A						: List[Any]      =       name.replace('pretrained.model' ,							'dpt.embeddings'			)
     if "patch_embed" in name:
          __A						: Dict      =       name.replace('patch_embed' ,							''			)
     if "pos_embed" in name:
          __A						: int      =       name.replace('pos_embed' ,							'position_embeddings'			)
     if "attn.proj" in name:
          __A						: int      =       name.replace('attn.proj' ,							'attention.output.dense'			)
     if "proj" in name and "project" not in name:
          __A						: Any      =       name.replace('proj' ,							'projection'			)
     if "blocks" in name:
          __A						: Tuple      =       name.replace('blocks' ,							'layer'			)
     if "mlp.fc1" in name:
          __A						: List[str]      =       name.replace('mlp.fc1' ,							'intermediate.dense'			)
     if "mlp.fc2" in name:
          __A						: Dict      =       name.replace('mlp.fc2' ,							'output.dense'			)
     if "norm1" in name and "backbone" not in name:
          __A						: int      =       name.replace('norm1' ,							'layernorm_before'			)
     if "norm2" in name and "backbone" not in name:
          __A						: str      =       name.replace('norm2' ,							'layernorm_after'			)
     if "scratch.output_conv" in name:
          __A						: Any      =       name.replace('scratch.output_conv' ,							'head'			)
     if "scratch" in name:
          __A						: List[str]      =       name.replace('scratch' ,							'neck'			)
     if "layer1_rn" in name:
          __A						: Any      =       name.replace('layer1_rn' ,							'convs.0'			)
     if "layer2_rn" in name:
          __A						: Any      =       name.replace('layer2_rn' ,							'convs.1'			)
     if "layer3_rn" in name:
          __A						: Dict      =       name.replace('layer3_rn' ,							'convs.2'			)
     if "layer4_rn" in name:
          __A						: Dict      =       name.replace('layer4_rn' ,							'convs.3'			)
     if "refinenet" in name:
          __A						: List[Any]      =       int(name[len('neck.refinenet'			) : len('neck.refinenet'			) + 1]			)
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          __A						: int      =       name.replace(f'refinenet{layer_idx}' ,							f'fusion_stage.layers.{abs(layer_idx-4			)}'			)
     if "out_conv" in name:
          __A						: str      =       name.replace('out_conv' ,							'projection'			)
     if "resConfUnit1" in name:
          __A						: str      =       name.replace('resConfUnit1' ,							'residual_layer1'			)
     if "resConfUnit2" in name:
          __A						: int      =       name.replace('resConfUnit2' ,							'residual_layer2'			)
     if "conv1" in name:
          __A						: Optional[int]      =       name.replace('conv1' ,							'convolution1'			)
     if "conv2" in name:
          __A						: Dict      =       name.replace('conv2' ,							'convolution2'			)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __A						: Dict      =       name.replace('pretrained.act_postprocess1.0.project.0' ,							'neck.reassemble_stage.readout_projects.0.0'			)
     if "pretrained.act_postprocess2.0.project.0" in name:
          __A						: int      =       name.replace('pretrained.act_postprocess2.0.project.0' ,							'neck.reassemble_stage.readout_projects.1.0'			)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __A						: Optional[Any]      =       name.replace('pretrained.act_postprocess3.0.project.0' ,							'neck.reassemble_stage.readout_projects.2.0'			)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __A						: List[Any]      =       name.replace('pretrained.act_postprocess4.0.project.0' ,							'neck.reassemble_stage.readout_projects.3.0'			)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __A						: List[Any]      =       name.replace('pretrained.act_postprocess1.3' ,							'neck.reassemble_stage.layers.0.projection'			)
     if "pretrained.act_postprocess1.4" in name:
          __A						: Dict      =       name.replace('pretrained.act_postprocess1.4' ,							'neck.reassemble_stage.layers.0.resize'			)
     if "pretrained.act_postprocess2.3" in name:
          __A						: List[Any]      =       name.replace('pretrained.act_postprocess2.3' ,							'neck.reassemble_stage.layers.1.projection'			)
     if "pretrained.act_postprocess2.4" in name:
          __A						: Dict      =       name.replace('pretrained.act_postprocess2.4' ,							'neck.reassemble_stage.layers.1.resize'			)
     if "pretrained.act_postprocess3.3" in name:
          __A						: Dict      =       name.replace('pretrained.act_postprocess3.3' ,							'neck.reassemble_stage.layers.2.projection'			)
     if "pretrained.act_postprocess4.3" in name:
          __A						: Dict      =       name.replace('pretrained.act_postprocess4.3' ,							'neck.reassemble_stage.layers.3.projection'			)
     if "pretrained.act_postprocess4.4" in name:
          __A						: Union[str, Any]      =       name.replace('pretrained.act_postprocess4.4' ,							'neck.reassemble_stage.layers.3.resize'			)
     if "pretrained" in name:
          __A						: int      =       name.replace('pretrained' ,							'dpt'			)
     if "bn" in name:
          __A						: Dict      =       name.replace('bn' ,							'batch_norm'			)
     if "head" in name:
          __A						: str      =       name.replace('head' ,							'head.head'			)
     if "encoder.norm" in name:
          __A						: Tuple      =       name.replace('encoder.norm' ,							'layernorm'			)
     if "auxlayer" in name:
          __A						: Any      =       name.replace('auxlayer' ,							'auxiliary_head.head'			)
     if "backbone" in name:
          __A						: Any      =       name.replace('backbone' ,							'backbone.bit.encoder'			)

     if ".." in name:
          __A						: List[str]      =       name.replace('..' ,							'.'			)

     if "stem.conv" in name:
          __A						: Union[str, Any]      =       name.replace('stem.conv' ,							'bit.embedder.convolution'			)
     if "blocks" in name:
          __A						: int      =       name.replace('blocks' ,							'layers'			)
     if "convolution" in name and "backbone" in name:
          __A						: Union[str, Any]      =       name.replace('convolution' ,							'conv'			)
     if "layer" in name and "backbone" in name:
          __A						: Optional[Any]      =       name.replace('layer' ,							'layers'			)
     if "backbone.bit.encoder.bit" in name:
          __A						: Union[str, Any]      =       name.replace('backbone.bit.encoder.bit' ,							'backbone.bit'			)
     if "embedder.conv" in name:
          __A						: Union[str, Any]      =       name.replace('embedder.conv' ,							'embedder.convolution'			)
     if "backbone.bit.encoder.stem.norm" in name:
          __A						: Dict      =       name.replace('backbone.bit.encoder.stem.norm' ,							'backbone.bit.embedder.norm'			)
     return name






def        _lowerCAmelCase			(  __snake_case  : Optional[int] ,							__snake_case  : List[str]			)     ->     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)
          __A						: Union[str, Any]      =       state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'			)
          __A						: Union[str, Any]      =       state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'			)
          # next, add query, keys and values (in that order) to the state dict
          __A						: Optional[int]      =       in_proj_weight[: config.hidden_size, :]
          __A						: Union[str, Any]      =       in_proj_bias[: config.hidden_size]
          __A						: Any      =       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __A						: List[str]      =       in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __A						: Union[str, Any]      =       in_proj_weight[
              -config.hidden_size :, :
          ]
          __A						: Union[str, Any]      =       in_proj_bias[-config.hidden_size :]






def        _lowerCAmelCase			(  )     ->     int:
     __A						: List[str]      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						: Optional[Any]      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)
     return im






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

     # load HuggingFace model
     __A						: Tuple      =       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
     __A						: int      =       4_80 if 'ade' in checkpoint_url else 3_84
     __A						: List[Any]      =       DPTImageProcessor(size=__snake_case			)

     __A						: str      =       prepare_img()
     __A						: Any      =       image_processor(__snake_case ,							return_tensors='pt'			)

     # forward pass
     __A						: Optional[int]      =       model(**__snake_case			).logits if 'ade' in checkpoint_url else model(**__snake_case			).predicted_depth

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

          Image.fromarray((prediction / prediction.max()) * 2_55			).show()

     if pytorch_dump_folder_path is not None:
          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:
          model.push_to_hub('ybelkada/dpt-hybrid-midas'			)
          image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'			)


if __name__ == "__main__":
  lowercase__    :      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''',
  )

  lowercase__    :      Union[str, Any]						=    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'''




import math






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5 ,							int(math.sqrt(__snake_case			) + 1			) ,							6			):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def        _lowerCAmelCase			(  __snake_case  : float = 0.1			)     ->     int:
     __A						: Tuple      =       3
     __A						: Optional[int]      =       3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1 ,							(j + 2) * (j + 2) ,							j + 1			):
               primes += is_prime(__snake_case			)
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()