

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      _lowerCAmelCase(	__A			):
  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(__A   ,       __A   ,       repo_type="dataset"			)			)   ,       "r"			)			)
    UpperCAmelCase					      =  {int(__A			): 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      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(__A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  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      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	):
  UpperCAmelCase					      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase					      =  Image.open(requests.get(__A   ,       stream=__A			).raw			)
  return im
@torch.no_grad()
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dpt_config(__A			)
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)
  # remove certain keys
  remove_ignore_keys_(__A			)
  # rename keys
  for key in state_dict.copy().keys():
    UpperCAmelCase					      =  state_dict.pop(__A			)
    UpperCAmelCase					      =  val
  # read in qkv matrices
  read_in_q_k_v(__A   ,       __A			)

  # load HuggingFace model
  UpperCAmelCase					      =  DPTForSemanticSegmentation(__A			) if "ade" in checkpoint_url else DPTForDepthEstimation(__A			)
  model.load_state_dict(__A			)
  model.eval()

  # Check outputs on an image
  UpperCAmelCase					      =  480 if "ade" in checkpoint_url else 384
  UpperCAmelCase					      =  DPTImageProcessor(size=__A			)

  UpperCAmelCase					      =  prepare_img()
  UpperCAmelCase					      =  image_processor(__A   ,       return_tensors="pt"			)

  # forward pass
  UpperCAmelCase					      =  model(**__A			).logits if "ade" in checkpoint_url else model(**__A			).predicted_depth

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

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

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

  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
      )






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class 						__magic_name__    :


 def __init__(						self		: Optional[int]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  []
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  2_5_6
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  cva.imread(lowerCAmelCase__			,						0    )
   UpperCAmelCase					      =  copy.deepcopy(self.img    )
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]			,						label="x"    )
   UpperCAmelCase					      =  np.sum(lowerCAmelCase__    )
   for i in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  x[i] / self.k
     self.sk += prk
     UpperCAmelCase					      =  (self.L - 1) * self.sk
     if self.rem != 0:
       UpperCAmelCase					      =  int(last % last    )
     UpperCAmelCase					      =  int(last + 1 if self.rem >= 0.5 else last    )
     self.last_list.append(lowerCAmelCase__    )
     UpperCAmelCase					      =  int(np.ma.count(self.img    ) / self.img[1].size    )
     UpperCAmelCase					      =  self.img[1].size
   for i in range(self.number_of_cols    ):
     for j in range(self.number_of_rows    ):
       UpperCAmelCase					      =  self.img[j][i]
       if num != self.last_list[num]:
         UpperCAmelCase					      =  self.last_list[num]
   cva.imwrite("output_data/output.jpg"			,						self.img    )


 def _UpperCamelCase	(						self		: str    )    ->      int:
   plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[Any]:
   cva.imshow("Output-Image"			,						self.img    )
   cva.imshow("Input-Image"			,						self.original_image    )
   cva.waitKey(5_0_0_0    )
   cva.destroyAllWindows()


if __name__ == "__main__":
      lowerCAmelCase__       = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
      lowerCAmelCase__       = ConstantStretch()
      stretcher.stretch(file_path)
      stretcher.plot_histogram()
      stretcher.show_image()



