'''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	:Any						=     logging.get_logger(__name__)


def      __lowerCAmelCase    (							a_		)   ->						List[Any]:





     '''simple docstring'''





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

     return new_state_dict


def      __lowerCAmelCase    (							a_							,   a_		)   ->						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)
               SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""		)
               SCREAMING_SNAKE_CASE       :      Dict     						=							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
               SCREAMING_SNAKE_CASE       :      Tuple     						=							kv_weight[
                   : config.hidden_sizes[i], :
               ]
               SCREAMING_SNAKE_CASE       :      Tuple     						=							kv_bias[: config.hidden_sizes[i]]
               SCREAMING_SNAKE_CASE       :      int     						=							kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               SCREAMING_SNAKE_CASE       :      Optional[int]     						=							kv_bias[config.hidden_sizes[i] :]


def      __lowerCAmelCase    (							)   ->						List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE       :      Any     						=							'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE       :      int     						=							Image.open(requests.get(__UpperCamelCase							,   stream=__UpperCamelCase		).raw		)

     return image


@torch.no_grad()
def      __lowerCAmelCase    (							a_							,   a_							,   a_=False							,   a_=None		)   ->						Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE       :      List[Any]     						=							GLPNConfig(hidden_sizes=[64, 128, 320, 512]							,   decoder_hidden_size=64							,   depths=[3, 8, 27, 3]		)

     # load image processor (only resize + rescale)
     SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							GLPNImageProcessor()

     # prepare image
     SCREAMING_SNAKE_CASE       :      List[str]     						=							prepare_img()
     SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							image_processor(images=__UpperCamelCase							,   return_tensors='pt'		).pixel_values

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

     # load original state dict
     SCREAMING_SNAKE_CASE       :      str     						=							torch.load(__UpperCamelCase							,   map_location=torch.device('cpu'		)		)

     # rename keys
     SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							rename_keys(__UpperCamelCase		)

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

     # create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							GLPNForDepthEstimation(__UpperCamelCase		)
     model.load_state_dict(__UpperCamelCase		)
     model.eval()

     # forward pass
     SCREAMING_SNAKE_CASE       :      Any     						=							model(__UpperCamelCase		)
     SCREAMING_SNAKE_CASE       :      Tuple     						=							outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               SCREAMING_SNAKE_CASE       :      Tuple     						=							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:
               SCREAMING_SNAKE_CASE       :      Union[str, 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}"""		)

          SCREAMING_SNAKE_CASE       :      Optional[int]     						=							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	:List[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

def 					lowerCamelCase_ ( __UpperCamelCase					:       dict							,    __UpperCamelCase					:       str		)					->	set[str]:







     """simple docstring"""
     _A		,				_A				=							set(__UpperCamelCase		), [start]

     while stack:
          _A				=							stack.pop()
          explored.add(__UpperCamelCase		)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]		):
               if adj not in explored:
                    stack.append(__UpperCamelCase		)
     return explored


lowerCAmelCase						=					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, """A"""))





