"""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()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



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

     return new_state_dict



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     # 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)
               lowercase_			:       List[str]									=			state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''   )
               lowercase_			:       Union[str, Any]									=			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_			:       str									=			kv_weight[
                   : config.hidden_sizes[i], :
               ]
               lowercase_			:       Dict									=			kv_bias[: config.hidden_sizes[i]]
               lowercase_			:       Dict									=			kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               lowercase_			:       Tuple									=			kv_bias[config.hidden_sizes[i] :]



def     lowercase__( ):
     lowercase_			:       List[Any]									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_			:       Union[str, Any]									=			Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,    stream=__SCREAMING_SNAKE_CASE   ).raw   )

     return image



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Dict=None   ):
     lowercase_			:       int									=			GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]  ,    decoder_hidden_size=64  ,    depths=[3, 8, 27, 3]   )

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

     # prepare image
     lowercase_			:       Union[str, Any]									=			prepare_img()
     lowercase_			:       Tuple									=			image_processor(images=__SCREAMING_SNAKE_CASE  ,    return_tensors='pt'   ).pixel_values

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

     # load original state dict
     lowercase_			:       List[str]									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location=torch.device('cpu'   )   )

     # rename keys
     lowercase_			:       Optional[Any]									=			rename_keys(__SCREAMING_SNAKE_CASE   )

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

     # create HuggingFace model and load state dict
     lowercase_			:       List[str]									=			GLPNForDepthEstimation(__SCREAMING_SNAKE_CASE   )
     model.load_state_dict(__SCREAMING_SNAKE_CASE   )
     model.eval()

     # forward pass
     lowercase_			:       Union[str, Any]									=			model(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Union[str, Any]									=			outputs.predicted_depth

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

          lowercase_			:       str									=			torch.Size([1, 4_80, 6_40]   )

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


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


"""simple docstring"""







import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__SCREAMING_SNAKE_CASE								={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__SCREAMING_SNAKE_CASE								=logging.WARNING



def     lowercase__( ):
     lowercase_			:       List[Any]									=			os.getenv('DATASETS_VERBOSITY'  ,    __SCREAMING_SNAKE_CASE   )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                   F'''has to be one of: { ", ".join(log_levels.keys()   ) }'''   )
     return _default_log_level



def     lowercase__( ):
     return __name__.split('.'   )[0]



def     lowercase__( ):
     return logging.getLogger(_get_library_name()   )



def     lowercase__( ):
     # Apply our default configuration to the library root logger.
     lowercase_			:       Optional[int]									=			_get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()   )



def     lowercase__( ):
     lowercase_			:       int									=			_get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[str] = None   ):
     if name is None:
          lowercase_			:       Union[str, Any]									=			_get_library_name()
     return logging.getLogger(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return _get_library_root_logger().getEffectiveLevel()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     _get_library_root_logger().setLevel(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     lowercase_			:       Tuple									=			False



def     lowercase__( ):
     lowercase_			:       Any									=			True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()







class  UpperCamelCase	:
   def __init__(    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:  # pylint: disable=unused-argument


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			args[0] if args else None
   def __iter__(    self    )			->   Union[str, Any]:


        '''simple docstring'''


        return iter(self._iterator    )
   def __getattr__(    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        def empty_fn(*__UpperCamelCase  ,**__UpperCamelCase    ):  # pylint: disable=unused-argument
             return

        return empty_fn
   def __enter__(    self    )			->   Optional[int]:


        '''simple docstring'''


        return self







   def __exit__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        return






__SCREAMING_SNAKE_CASE								=True







class  UpperCamelCase	:
   def __call__(    self  ,*__UpperCamelCase  ,__UpperCamelCase=False  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if _tqdm_active and not disable:
             return tqdm_lib.tqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
        else:
             return EmptyTqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			None
        if _tqdm_active:
             return tqdm_lib.tqdm.set_lock(*__UpperCamelCase  ,**__UpperCamelCase    )







   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        if _tqdm_active:
             return tqdm_lib.tqdm.get_lock()






__SCREAMING_SNAKE_CASE								=_tqdm_cls()



def     lowercase__( ):
     global _tqdm_active
     return bool(_tqdm_active   )



def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Union[str, Any]									=			True







def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Optional[int]									=			False

