


"""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  :     List[Any]    					=    logging.get_logger(__name__)


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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

			return new_state_dict


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

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


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase       					=    Image.open(requests.get(_UpperCamelCase					,				stream=_UpperCamelCase   ).raw   )

			return image


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=False					,				_UpperCamelCase=None   ):

			'''simple docstring'''




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

			# load image processor (only resize + rescale)
			__lowerCAmelCase       					=    GLPNImageProcessor()

			# prepare image
			__lowerCAmelCase       					=    prepare_img()
			__lowerCAmelCase       					=    image_processor(images=_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

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

			# load original state dict
			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location=torch.device("cpu"   )   )

			# rename keys
			__lowerCAmelCase       					=    rename_keys(_UpperCamelCase   )

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

			# create HuggingFace model and load state dict
			__lowerCAmelCase       					=    GLPNForDepthEstimation(_UpperCamelCase   )
			model.load_state_dict(_UpperCamelCase   )
			model.eval()

			# forward pass
			__lowerCAmelCase       					=    model(_UpperCamelCase   )
			__lowerCAmelCase       					=    outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									__lowerCAmelCase       					=    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       					=    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       					=    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  :     Union[str, Any]    					=    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)








"""simple docstring"""


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


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




