





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()
_A :   str  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					):







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

   return new_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					):







   """simple docstring"""
   # 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)
         lowerCamelCase__       :						Optional[Any]        =							state_dict.pop(f"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"					)
         lowerCamelCase__       :						List[str]        =							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
         lowerCamelCase__       :						Optional[Any]        =							kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCamelCase__       :						Any        =							kv_bias[: config.hidden_sizes[i]]
         lowerCamelCase__       :						Optional[int]        =							kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCamelCase__       :						Optional[int]        =							kv_bias[config.hidden_sizes[i] :]



def 	_a						(   ):







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCamelCase__       :						Dict        =							Image.open(requests.get(UpperCAmelCase      ,  stream=UpperCAmelCase					).raw					)

   return image



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False      ,  UpperCAmelCase=None					):







   """simple docstring"""
   lowerCamelCase__       :						str        =							GLPNConfig(hidden_sizes=[64, 128, 320, 512]      ,  decoder_hidden_size=64      ,  depths=[3, 8, 27, 3]					)

   # load image processor (only resize + rescale)
   lowerCamelCase__       :						int        =							GLPNImageProcessor()

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

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

   # load original state dict
   lowerCamelCase__       :						Optional[Any]        =							torch.load(UpperCAmelCase      ,  map_location=torch.device('''cpu'''					)					)

   # rename keys
   lowerCamelCase__       :						Any        =							rename_keys(UpperCAmelCase					)

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

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

   # forward pass
   lowerCamelCase__       :						Optional[int]        =							model(UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCamelCase__       :						Optional[Any]        =							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:
         lowerCamelCase__       :						str        =							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}"					)

      lowerCamelCase__       :						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__":
       _A :   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.',
       )
       _A :   Dict  			=  parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A :   Tuple  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							num_channels
          lowerCamelCase__       :						Any        =							embed_dim
          lowerCamelCase__       :						str        =							depths
          lowerCamelCase__       :						Union[str, Any]        =							len(A      )
          lowerCamelCase__       :						int        =							num_heads
          lowerCamelCase__       :						Optional[int]        =							kernel_size
          lowerCamelCase__       :						Optional[int]        =							mlp_ratio
          lowerCamelCase__       :						List[Any]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							drop_path_rate
          lowerCamelCase__       :						Union[str, Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							layer_norm_eps
          lowerCamelCase__       :						List[Any]        =							initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__       :						str        =							int(embed_dim * 2 ** (len(A      ) - 1)      )
          lowerCamelCase__       :						Dict        =							layer_scale_init_value
          lowerCamelCase__       :						Dict        =							['''stem'''] + [F"stage{idx}" for idx in range(1		, len(A      ) + 1      )]
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							get_aligned_output_features_output_indices(
              out_features=A		, out_indices=A		, stage_names=self.stage_names      )



