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







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Tuple								=   DPTConfig(embedding_type="hybrid"							)

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

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

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

      return config, expected_shape







def 		A_       (	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   ["pretrained.model.head.weight", "pretrained.model.head.bias"]
      for k in ignore_keys:
            state_dict.pop(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict							):



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

      if ".." in name:
            _lowerCamelCase				:   Dict								=   name.replace(".."				,	"."							)

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







def 		A_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Optional[Any]							):



      """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)
            _lowerCamelCase				:   Optional[int]								=   state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'							)
            _lowerCamelCase				:   int								=   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				:   Any								=   in_proj_weight[: config.hidden_size, :]
            _lowerCamelCase				:   int								=   in_proj_bias[: config.hidden_size]
            _lowerCamelCase				:   str								=   in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase				:   Any								=   in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase				:   Dict								=   in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase				:   List[Any]								=   in_proj_bias[-config.hidden_size :]







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase				:   List[str]								=   Image.open(requests.get(_lowerCAmelCase				,	stream=_lowerCAmelCase							).raw							)
      return im







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   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				:   Optional[int]								=   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				:   Union[str, Any]								=   val
      # read in qkv matrices
      read_in_q_k_v(_lowerCAmelCase				,	_lowerCAmelCase							)

      # load HuggingFace model
      _lowerCamelCase				:   Any								=   DPTForSemanticSegmentation(_lowerCAmelCase							) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase							)
      model.load_state_dict(_lowerCAmelCase							)
      model.eval()

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

      _lowerCamelCase				:   str								=   prepare_img()
      _lowerCamelCase				:   Any								=   image_processor(_lowerCAmelCase				,	return_tensors="pt"							)

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

      if show_prediction:
            _lowerCamelCase				:   Dict								=   (
                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__":
 UpperCAmelCase_       :   Union[str, Any]   =       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',
 )

 UpperCAmelCase_       :   Optional[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 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   math.loga(math.sqrt(4 * positive_integer + 1							) / 2 + 1 / 2							)

      return exponent == int(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    float = 1 / 12345							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   int								=   0

      _lowerCamelCase				:   List[str]								=   3
      while True:
            _lowerCamelCase				:   List[Any]								=   (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(_lowerCAmelCase							):
                  _lowerCamelCase				:   Any								=   int(_lowerCAmelCase							)
                  total_partitions += 1
                  if check_partition_perfect(_lowerCAmelCase							):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(_lowerCAmelCase							)
            integer += 1


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