

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()
lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   Tuple     )    ->      int:





      """simple docstring"""



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

      return new_state_dict
def      a      (			A__	:   Union[str, Any]      ,   A__	:   Any     )    ->      List[Any]:





      """simple docstring"""



      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)
                  _lowercase	       =state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''     )
                  _lowercase	       =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
                  _lowercase	       =kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  _lowercase	       =kv_bias[: config.hidden_sizes[i]]
                  _lowercase	       =kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  _lowercase	       =kv_bias[config.hidden_sizes[i] :]
def      a      (			)    ->      List[str]:





      """simple docstring"""



      _lowercase	       ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase	       =Image.open(requests.get(A__      ,   stream=A__     ).raw     )

      return image
@torch.no_grad()
def      a      (			A__	:   List[str]      ,   A__	:   Optional[Any]      ,   A__	:   str=False      ,   A__	:   str=None     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =GLPNConfig(hidden_sizes=[64, 128, 320, 512]      ,   decoder_hidden_size=64      ,   depths=[3, 8, 27, 3]     )

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

      # prepare image
      _lowercase	       =prepare_img()
      _lowercase	       =image_processor(images=A__      ,   return_tensors='pt'     ).pixel_values

      logger.info('Converting model...'     )

      # load original state dict
      _lowercase	       =torch.load(A__      ,   map_location=torch.device('cpu'     )     )

      # rename keys
      _lowercase	       =rename_keys(A__     )

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

      # create HuggingFace model and load state dict
      _lowercase	       =GLPNForDepthEstimation(A__     )
      model.load_state_dict(A__     )
      model.eval()

      # forward pass
      _lowercase	       =model(A__     )
      _lowercase	       =outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  _lowercase	       =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:
                  _lowercase	       =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}'''     )

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

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]      ,   A__      ,   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(A__      ,   A__     )      ,   organization='nielsr'      ,   commit_message='Add model'      ,   use_temp_dir=A__      ,   )
            image_processor.push_to_hub(
                repo_path_or_name=Path(A__      ,   A__     )      ,   organization='nielsr'      ,   commit_message='Add image processor'      ,   use_temp_dir=A__      ,   )


if __name__ == "__main__":
  lowercase_									=   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.',
  )
  lowercase_									=   parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


