




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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

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

       return new_state_dict







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """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)
                     A__       =    state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""						)
                     A__       =    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
                     A__       =    kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     A__       =    kv_bias[: config.hidden_sizes[i]]
                     A__       =    kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     A__       =    kv_bias[config.hidden_sizes[i] :]







def SCREAMING_SNAKE_CASE				(       )   ->       Optional[Any]:



       """simple docstring"""

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

       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=False ,				lowercase_=None						)   ->       Any:



       """simple docstring"""

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

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

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

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

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

       # rename keys
       A__       =    rename_keys(lowercase_						)

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

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

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

       # verify output
       if model_name is not None:
              if "nyu" in model_name:
                     A__       =    torch.tensor(
                         [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]						)
              elif "kitti" in model_name:
                     A__       =    torch.tensor(
                         [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						)
              else:
                     raise ValueError(f"""Unknown model name: {model_name}"""						)

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

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


if __name__ == "__main__":
  _lowerCamelCase	:		int							       =   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	:		Any							       =   parser.parse_args()
  convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


