

'''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()
lowercase__			    =			logging.get_logger(__name__)




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

 return new_state_dict




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):




 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       :	int											= state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""	)
   UpperCAmelCase       :	List[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
   UpperCAmelCase       :	Optional[int]											= kv_weight[
       : config.hidden_sizes[i], :
   ]
   UpperCAmelCase       :	Optional[int]											= kv_bias[: config.hidden_sizes[i]]
   UpperCAmelCase       :	int											= kv_weight[
       config.hidden_sizes[i] :, :
   ]
   UpperCAmelCase       :	Dict											= kv_bias[config.hidden_sizes[i] :]




def       UpperCamelCase(     ):
 UpperCAmelCase       :	int											= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCAmelCase       :	Union[str, Any]											= Image.open(requests.get(UpperCAmelCase_  ,       stream=UpperCAmelCase_	).raw	)

 return image




@torch.no_grad()
def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=False  ,       UpperCAmelCase_=None	):
 UpperCAmelCase       :	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)
 UpperCAmelCase       :	Union[str, Any]											= GLPNImageProcessor()

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

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

 # load original state dict
 UpperCAmelCase       :	Tuple											= 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       :	Tuple											= GLPNForDepthEstimation(UpperCAmelCase_	)
 model.load_state_dict(UpperCAmelCase_	)
 model.eval()

 # forward pass
 UpperCAmelCase       :	Tuple											= model(UpperCAmelCase_	)
 UpperCAmelCase       :	Dict											= outputs.predicted_depth

 # verify output
 if model_name is not None:
  if "nyu" in model_name:
   UpperCAmelCase       :	Optional[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:
   UpperCAmelCase       :	Optional[int]											= 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, 4_80, 6_40]	)

  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__":
 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)











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE__					=   {}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   ["BartphoTokenizer"]

if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bartpho import BartphoTokenizer

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






