
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     _a					(						lowerCamelCase							):
 lowerCamelCase		:	int							=      DPTConfig(embedding_type="""hybrid"""							)

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

 if "nyu" or "midas" in checkpoint_url:
  lowerCamelCase		:	Optional[int]							=      768
  lowerCamelCase		:	List[str]							=      [1, 1, 1, 0.5]
  lowerCamelCase		:	List[Any]							=      [256, 512, 768, 768]
  lowerCamelCase		:	Optional[Any]							=      150
  lowerCamelCase		:	List[Any]							=      16
  lowerCamelCase		:	Tuple							=      (1, 384, 384)
  lowerCamelCase		:	Any							=      False
  lowerCamelCase		:	Tuple							=      """project"""

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

 return config, expected_shape





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Dict							=      ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase,	lowerCamelCase							)





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

 if ".." in name:
  lowerCamelCase		:	Optional[int]							=      name.replace("""..""",	"""."""							)

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





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 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[Any]							=      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		:	Dict							=      in_proj_weight[: config.hidden_size, :]
  lowerCamelCase		:	int							=      in_proj_bias[: config.hidden_size]
  lowerCamelCase		:	int							=      in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowerCamelCase		:	Union[str, Any]							=      in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowerCamelCase		:	int							=      in_proj_weight[
      -config.hidden_size :, :
  ]
  lowerCamelCase		:	Union[str, Any]							=      in_proj_bias[-config.hidden_size :]





def     _a					(						):
 lowerCamelCase		:	Optional[Any]							=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCamelCase		:	Optional[Any]							=      Image.open(requests.get(lowerCamelCase,	stream=lowerCamelCase							).raw							)
 return im





@torch.no_grad()
def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase			,    lowerCamelCase		:	Dict							=      get_dpt_config(lowerCamelCase							)
 # load original state_dict from URL
 # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
 lowerCamelCase		:	Any							=      torch.load(lowerCamelCase,	map_location="""cpu"""							)
 # remove certain keys
 remove_ignore_keys_(lowerCamelCase							)
 # rename keys
 for key in state_dict.copy().keys():
  lowerCamelCase		:	List[Any]							=      state_dict.pop(lowerCamelCase							)
  lowerCamelCase		:	str							=      val
 # read in qkv matrices
 read_in_q_k_v(lowerCamelCase,	lowerCamelCase							)

 # load HuggingFace model
 lowerCamelCase		:	Optional[int]							=      DPTForSemanticSegmentation(lowerCamelCase							) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase							)
 model.load_state_dict(lowerCamelCase							)
 model.eval()

 # Check outputs on an image
 lowerCamelCase		:	Union[str, Any]							=      480 if """ade""" in checkpoint_url else 384
 lowerCamelCase		:	List[str]							=      DPTImageProcessor(size=lowerCamelCase							)

 lowerCamelCase		:	Union[str, Any]							=      prepare_img()
 lowerCamelCase		:	Dict							=      image_processor(lowerCamelCase,	return_tensors="""pt"""							)

 # forward pass
 lowerCamelCase		:	int							=      model(**lowerCamelCase							).logits if """ade""" in checkpoint_url else model(**lowerCamelCase							).predicted_depth

 if show_prediction:
  lowerCamelCase		:	Union[str, Any]							=      (
      torch.nn.functional.interpolate(
          outputs.unsqueeze(1							),	size=(image.size[1], image.size[0]),	mode="""bicubic""",	align_corners=lowerCamelCase,	)
      .squeeze()
      .cpu()
      .numpy()
  )

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

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

 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
      )






def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




