





"""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()
a		:					int	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->Union[str, Any]:






	'''simple docstring'''






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

	return new_state_dict






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]       ,			_lowercase		:       List[str]   )					->Any:






	'''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)
			a :						str      =							state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
			a :						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
			a :						Tuple      =							kv_weight[
			    : config.hidden_sizes[i], :
			]
			a :						List[Any]      =							kv_bias[: config.hidden_sizes[i]]
			a :						Union[str, Any]      =							kv_weight[
			    config.hidden_sizes[i] :, :
			]
			a :						Optional[int]      =							kv_bias[config.hidden_sizes[i] :]






def  _SCREAMING_SNAKE_CASE						(      )					->Union[str, Any]:






	'''simple docstring'''






	a :						Optional[Any]      =							"http://images.cocodataset.org/val2017/000000039769.jpg"
	a :						Any      =							Image.open(requests.get(_lowercase       ,			stream=_lowercase   ).raw   )

	return image






@torch.no_grad()
def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any       ,			_lowercase		:       Dict       ,			_lowercase		:       Any=False       ,			_lowercase		:       Any=None   )					->List[Any]:






	'''simple docstring'''






	a :						Dict      =							GLPNConfig(hidden_sizes=[64, 128, 320, 512]       ,			decoder_hidden_size=64       ,			depths=[3, 8, 27, 3]   )

	# load image processor (only resize + rescale)
	a :						Any      =							GLPNImageProcessor()

	# prepare image
	a :						str      =							prepare_img()
	a :						int      =							image_processor(images=_lowercase       ,			return_tensors="pt"   ).pixel_values

	logger.info("Converting model..."   )

	# load original state dict
	a :						List[str]      =							torch.load(_lowercase       ,			map_location=torch.device("cpu"   )   )

	# rename keys
	a :						int      =							rename_keys(_lowercase   )

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

	# create HuggingFace model and load state dict
	a :						Dict      =							GLPNForDepthEstimation(_lowercase   )
	model.load_state_dict(_lowercase   )
	model.eval()

	# forward pass
	a :						List[Any]      =							model(_lowercase   )
	a :						str      =							outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			a :						int      =							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:
			a :						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}"""   )

		a :						Tuple      =							torch.Size([1, 480, 640]   )

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


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









'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				""""""
      for word_or_phrase in separated:
            if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
                  raise Exception("""join() accepts only strings to be joined"""    )
            joined += word_or_phrase + separator
      return joined.strip(lowerCAmelCase    )


if __name__ == "__main__":
      from doctest import testmod

      testmod()






