





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       :List[str]       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  Any     ):





						'''simple docstring'''


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

						return new_state_dict







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  List[str]     ):





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







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE__     :				List[str]   =					Image.open(requests.get(_lowerCamelCase			,   stream=_lowerCamelCase     ).raw     )

						return image







@torch.no_grad()
def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Optional[Any]=False			,   _lowerCamelCase      :  str=None     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]			,   decoder_hidden_size=64			,   depths=[3, 8, 27, 3]     )

						# load image processor (only resize + rescale)
						SCREAMING_SNAKE_CASE__     :				Dict   =					GLPNImageProcessor()

						# prepare image
						SCREAMING_SNAKE_CASE__     :				Any   =					prepare_img()
						SCREAMING_SNAKE_CASE__     :				str   =					image_processor(images=_lowerCamelCase			,   return_tensors="pt"     ).pixel_values

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

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

						# rename keys
						SCREAMING_SNAKE_CASE__     :				int   =					rename_keys(_lowerCamelCase     )

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

						# create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					GLPNForDepthEstimation(_lowerCamelCase     )
						model.load_state_dict(_lowerCamelCase     )
						model.eval()

						# forward pass
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.tensor(
																		    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]     )
												elif "kitti" in model_name:
																		SCREAMING_SNAKE_CASE__     :				Any   =					torch.tensor(
																		    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]     )
												else:
																		raise ValueError(f"""Unknown model name: {model_name}"""     )

												SCREAMING_SNAKE_CASE__     :				str   =					torch.Size([1, 480, 640]     )

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


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase       :str       =  {
    "configuration_upernet": ["UperNetConfig"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Union[str, Any]       =  [
				    "UperNetForSemanticSegmentation",
				    "UperNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_upernet import UperNetConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
				import sys

				__lowercase       :str       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)