



"""simple docstring"""
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    :int     =					logging.get_logger(__name__)





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

    return new_state_dict





def  lowerCamelCase_						(UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Any							):
    # 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							:  Optional[int]											=    state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'							)
            _UpperCAmelCase							:  Tuple											=    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							:  Dict											=    kv_weight[
                : config.hidden_sizes[i], :
            ]
            _UpperCAmelCase							:  Optional[Any]											=    kv_bias[: config.hidden_sizes[i]]
            _UpperCAmelCase							:  str											=    kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _UpperCAmelCase							:  Tuple											=    kv_bias[config.hidden_sizes[i] :]





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Optional[int]											=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase							:  int											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							)

    return image





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Dict=False     ,       UpperCamelCase__		:		Optional[int]=None							):
    _UpperCAmelCase							:  Union[str, Any]											=    GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,       decoder_hidden_size=64     ,       depths=[3, 8, 27, 3]							)

    # load image processor (only resize + rescale)
    _UpperCAmelCase							:  List[str]											=    GLPNImageProcessor()

    # prepare image
    _UpperCAmelCase							:  Tuple											=    prepare_img()
    _UpperCAmelCase							:  Tuple											=    image_processor(images=UpperCamelCase__     ,       return_tensors='''pt'''							).pixel_values

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

    # load original state dict
    _UpperCAmelCase							:  Any											=    torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)

    # rename keys
    _UpperCAmelCase							:  Tuple											=    rename_keys(UpperCamelCase__							)

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

    # create HuggingFace model and load state dict
    _UpperCAmelCase							:  str											=    GLPNForDepthEstimation(UpperCamelCase__							)
    model.load_state_dict(UpperCamelCase__							)
    model.eval()

    # forward pass
    _UpperCAmelCase							:  List[str]											=    model(UpperCamelCase__							)
    _UpperCAmelCase							:  Dict											=    outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _UpperCAmelCase							:  int											=    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							:  List[Any]											=    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							:  Union[str, Any]											=    torch.Size([1, 480, 640]							)

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


if __name__ == "__main__":
  _lowerCAmelCase    :str     =					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    :Union[str, Any]     =					parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


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


