

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__       = logging.get_logger(__name__)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  OrderedDict()
  for key, value in state_dict.items():
    if key.startswith("module.encoder"			):
      UpperCAmelCase					      =  key.replace("module.encoder"   ,       "glpn.encoder"			)
    if key.startswith("module.decoder"			):
      UpperCAmelCase					      =  key.replace("module.decoder"   ,       "decoder.stages"			)
    if "patch_embed" in key:
      # replace for example patch_embed1 by patch_embeddings.0
      UpperCAmelCase					      =  key[key.find("patch_embed"			) + len("patch_embed"			)]
      UpperCAmelCase					      =  key.replace(F"patch_embed{idx}"   ,       F"patch_embeddings.{int(_lowerCAmelCase			)-1}"			)
    if "norm" in key:
      UpperCAmelCase					      =  key.replace("norm"   ,       "layer_norm"			)
    if "glpn.encoder.layer_norm" in key:
      # replace for example layer_norm1 by layer_norm.0
      UpperCAmelCase					      =  key[key.find("glpn.encoder.layer_norm"			) + len("glpn.encoder.layer_norm"			)]
      UpperCAmelCase					      =  key.replace(F"layer_norm{idx}"   ,       F"layer_norm.{int(_lowerCAmelCase			)-1}"			)
    if "layer_norm1" in key:
      UpperCAmelCase					      =  key.replace("layer_norm1"   ,       "layer_norm_1"			)
    if "layer_norm2" in key:
      UpperCAmelCase					      =  key.replace("layer_norm2"   ,       "layer_norm_2"			)
    if "block" in key:
      # replace for example block1 by block.0
      UpperCAmelCase					      =  key[key.find("block"			) + len("block"			)]
      UpperCAmelCase					      =  key.replace(F"block{idx}"   ,       F"block.{int(_lowerCAmelCase			)-1}"			)
    if "attn.q" in key:
      UpperCAmelCase					      =  key.replace("attn.q"   ,       "attention.self.query"			)
    if "attn.proj" in key:
      UpperCAmelCase					      =  key.replace("attn.proj"   ,       "attention.output.dense"			)
    if "attn" in key:
      UpperCAmelCase					      =  key.replace("attn"   ,       "attention.self"			)
    if "fc1" in key:
      UpperCAmelCase					      =  key.replace("fc1"   ,       "dense1"			)
    if "fc2" in key:
      UpperCAmelCase					      =  key.replace("fc2"   ,       "dense2"			)
    if "linear_pred" in key:
      UpperCAmelCase					      =  key.replace("linear_pred"   ,       "classifier"			)
    if "linear_fuse" in key:
      UpperCAmelCase					      =  key.replace("linear_fuse.conv"   ,       "linear_fuse"			)
      UpperCAmelCase					      =  key.replace("linear_fuse.bn"   ,       "batch_norm"			)
    if "linear_c" in key:
      # replace for example linear_c4 by linear_c.3
      UpperCAmelCase					      =  key[key.find("linear_c"			) + len("linear_c"			)]
      UpperCAmelCase					      =  key.replace(F"linear_c{idx}"   ,       F"linear_c.{int(_lowerCAmelCase			)-1}"			)
    if "bot_conv" in key:
      UpperCAmelCase					      =  key.replace("bot_conv"   ,       "0.convolution"			)
    if "skip_conv1" in key:
      UpperCAmelCase					      =  key.replace("skip_conv1"   ,       "1.convolution"			)
    if "skip_conv2" in key:
      UpperCAmelCase					      =  key.replace("skip_conv2"   ,       "2.convolution"			)
    if "fusion1" in key:
      UpperCAmelCase					      =  key.replace("fusion1"   ,       "1.fusion"			)
    if "fusion2" in key:
      UpperCAmelCase					      =  key.replace("fusion2"   ,       "2.fusion"			)
    if "fusion3" in key:
      UpperCAmelCase					      =  key.replace("fusion3"   ,       "3.fusion"			)
    if "fusion" in key and "conv" in key:
      UpperCAmelCase					      =  key.replace("conv"   ,       "convolutional_layer"			)
    if key.startswith("module.last_layer_depth"			):
      UpperCAmelCase					      =  key.replace("module.last_layer_depth"   ,       "head.head"			)
    UpperCAmelCase					      =  value

  return new_state_dict
def      _lowerCAmelCase(	__A   ,       __A			):
  # for each of the encoder blocks:
  for i in range(config.num_encoder_blocks			):
    for j in range(config.depths[i]			):
      # read in weights + bias of keys and values (which is a single matrix in the original implementation)
      UpperCAmelCase					      =  state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"			)
      UpperCAmelCase					      =  state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.bias"			)
      # next, add keys and values (in that order) to the state dict
      UpperCAmelCase					      =  kv_weight[
          : config.hidden_sizes[i], :
      ]
      UpperCAmelCase					      =  kv_bias[: config.hidden_sizes[i]]
      UpperCAmelCase					      =  kv_weight[
          config.hidden_sizes[i] :, :
      ]
      UpperCAmelCase					      =  kv_bias[config.hidden_sizes[i] :]
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase					      =  Image.open(requests.get(_lowerCAmelCase   ,       stream=_lowerCAmelCase			).raw			)

  return image
@torch.no_grad()
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=False   ,       __A=None			):
  UpperCAmelCase					      =  GLPNConfig(hidden_sizes=[64, 128, 320, 512]   ,       decoder_hidden_size=64   ,       depths=[3, 8, 27, 3]			)

  # load image processor (only resize + rescale)
  UpperCAmelCase					      =  GLPNImageProcessor()

  # prepare image
  UpperCAmelCase					      =  prepare_img()
  UpperCAmelCase					      =  image_processor(images=_lowerCAmelCase   ,       return_tensors="pt"			).pixel_values

  logger.info("Converting model..."			)

  # load original state dict
  UpperCAmelCase					      =  torch.load(_lowerCAmelCase   ,       map_location=torch.device("cpu"			)			)

  # rename keys
  UpperCAmelCase					      =  rename_keys(_lowerCAmelCase			)

  # key and value matrices need special treatment
  read_in_k_v(_lowerCAmelCase   ,       _lowerCAmelCase			)

  # create HuggingFace model and load state dict
  UpperCAmelCase					      =  GLPNForDepthEstimation(_lowerCAmelCase			)
  model.load_state_dict(_lowerCAmelCase			)
  model.eval()

  # forward pass
  UpperCAmelCase					      =  model(_lowerCAmelCase			)
  UpperCAmelCase					      =  outputs.predicted_depth

  # verify output
  if model_name is not None:
    if "nyu" in model_name:
      UpperCAmelCase					      =  torch.tensor(
          [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]			)
    elif "kitti" in model_name:
      UpperCAmelCase					      =  torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]			)
    else:
      raise ValueError(F"Unknown model name: {model_name}"			)

    UpperCAmelCase					      =  torch.Size([1, 480, 640]			)

    assert predicted_depth.shape == expected_shape
    assert torch.allclose(predicted_depth[0, :3, :3]   ,       _lowerCAmelCase   ,       atol=1E-4			)
    print("Looks ok!"			)

  # finally, push to hub if required
  if push_to_hub:
    logger.info("Pushing model and image processor to the hub..."			)
    model.push_to_hub(
        repo_path_or_name=Path(_lowerCAmelCase   ,       _lowerCAmelCase			)   ,       organization="nielsr"   ,       commit_message="Add model"   ,       use_temp_dir=_lowerCAmelCase   ,       )
    image_processor.push_to_hub(
        repo_path_or_name=Path(_lowerCAmelCase   ,       _lowerCAmelCase			)   ,       organization="nielsr"   ,       commit_message="Add image processor"   ,       use_temp_dir=_lowerCAmelCase   ,       )


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          default=None,
          type=str,
          help="Path to the original PyTorch checkpoint (.pth file).",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
      )
      parser.add_argument(
          "--model_name",
          default="glpn-kitti",
          type=str,
          help="Name of the model in case you're pushing to the hub.",
      )
      lowerCAmelCase__       = parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






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()



