




from torch import nn







class  UpperCAmelCase(	nn.Module	):



	"""simple docstring"""






	def __init__(	self			, lowerCamelCase			, lowerCamelCase							)   ->     Union[str, Any]:





								"""simple docstring"""


								super().__init__()
								lowercase__    :			str     =				class_size
								lowercase__    :			Any     =				embed_size
								# self.mlp1 = nn.Linear(embed_size, embed_size)
								# self.mlp2 = (nn.Linear(embed_size, class_size))
								lowercase__    :			Any     =				nn.Linear(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE							)







	def        __a      (	self			, lowerCamelCase							)   ->     Optional[Any]:





								"""simple docstring"""


								lowercase__    :			Optional[int]     =				self.mlp(__SCREAMING_SNAKE_CASE							)
								return logits

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  =logging.get_logger(__name__)


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

      return new_state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      # 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)
                  UpperCamelCase__     : Optional[Any]											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__     : int											=					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__     : Union[str, Any]											=					kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__     : Tuple											=					kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__     : Tuple											=					kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__     : Optional[Any]											=					kv_bias[config.hidden_sizes[i] :]


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

      return image


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=None		):
      UpperCamelCase__     : Any											=					GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,		decoder_hidden_size=6_4			,		depths=[3, 8, 2_7, 3]		)

      # load image processor (only resize + rescale)
      UpperCamelCase__     : str											=					GLPNImageProcessor()

      # prepare image
      UpperCamelCase__     : List[str]											=					prepare_img()
      UpperCamelCase__     : List[str]											=					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__     : Optional[Any]											=					rename_keys(UpperCamelCase__		)

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

      # create HuggingFace model and load state dict
      UpperCamelCase__     : str											=					GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

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

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__     : 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:
                  UpperCamelCase__     : 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}'''		)

            UpperCamelCase__     : Optional[Any]											=					torch.Size([1, 4_8_0, 6_4_0]		)

            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  =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  =parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


