






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__      			=						logging.get_logger(__name__)







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					__a					:						List[Any]      =					DPTConfig()

					if "large" in checkpoint_url:
										__a					:						Optional[int]      =					1_024
										__a					:						Optional[Any]      =					4_096
										__a					:						Any      =					24
										__a					:						Optional[Any]      =					16
										__a					:						Optional[int]      =					[5, 11, 17, 23]
										__a					:						List[Any]      =					[256, 512, 1_024, 1_024]
										__a					:						int      =					(1, 384, 384)

					if "ade" in checkpoint_url:
										__a					:						Any      =					True

										__a					:						str      =					150
										__a					:						Tuple      =					"huggingface/label-files"
										__a					:						List[str]      =					"ade20k-id2label.json"
										__a					:						Any      =					json.load(open(cached_download(hf_hub_url(__A				,       __A				,       repo_type="dataset"      )      )				,       "r"      )      )
										__a					:						Tuple      =					{int(__A      ): v for k, v in idalabel.items()}
										__a					:						Optional[Any]      =					idalabel
										__a					:						Optional[int]      =					{v: k for k, v in idalabel.items()}
										__a					:						Any      =					[1, 150, 480, 480]

					return config, expected_shape







def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
					for k in ignore_keys:
										state_dict.pop(__A				,       __A      )







def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if (
					    "pretrained.model" in name
					    and "cls_token" not in name
					    and "pos_embed" not in name
					    and "patch_embed" not in name
					):
										__a					:						Optional[int]      =					name.replace("pretrained.model"				,       "dpt.encoder"      )
					if "pretrained.model" in name:
										__a					:						Union[str, Any]      =					name.replace("pretrained.model"				,       "dpt.embeddings"      )
					if "patch_embed" in name:
										__a					:						Optional[Any]      =					name.replace("patch_embed"				,       "patch_embeddings"      )
					if "pos_embed" in name:
										__a					:						Union[str, Any]      =					name.replace("pos_embed"				,       "position_embeddings"      )
					if "attn.proj" in name:
										__a					:						List[str]      =					name.replace("attn.proj"				,       "attention.output.dense"      )
					if "proj" in name and "project" not in name:
										__a					:						List[Any]      =					name.replace("proj"				,       "projection"      )
					if "blocks" in name:
										__a					:						Dict      =					name.replace("blocks"				,       "layer"      )
					if "mlp.fc1" in name:
										__a					:						str      =					name.replace("mlp.fc1"				,       "intermediate.dense"      )
					if "mlp.fc2" in name:
										__a					:						int      =					name.replace("mlp.fc2"				,       "output.dense"      )
					if "norm1" in name:
										__a					:						Optional[Any]      =					name.replace("norm1"				,       "layernorm_before"      )
					if "norm2" in name:
										__a					:						Optional[int]      =					name.replace("norm2"				,       "layernorm_after"      )
					if "scratch.output_conv" in name:
										__a					:						Optional[Any]      =					name.replace("scratch.output_conv"				,       "head"      )
					if "scratch" in name:
										__a					:						Optional[int]      =					name.replace("scratch"				,       "neck"      )
					if "layer1_rn" in name:
										__a					:						List[str]      =					name.replace("layer1_rn"				,       "convs.0"      )
					if "layer2_rn" in name:
										__a					:						List[str]      =					name.replace("layer2_rn"				,       "convs.1"      )
					if "layer3_rn" in name:
										__a					:						Tuple      =					name.replace("layer3_rn"				,       "convs.2"      )
					if "layer4_rn" in name:
										__a					:						int      =					name.replace("layer4_rn"				,       "convs.3"      )
					if "refinenet" in name:
										__a					:						Optional[int]      =					int(name[len("neck.refinenet"      ) : len("neck.refinenet"      ) + 1]      )
										# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
										__a					:						Optional[Any]      =					name.replace(f'refinenet{layer_idx}'				,       f'fusion_stage.layers.{abs(layer_idx-4      )}'      )
					if "out_conv" in name:
										__a					:						int      =					name.replace("out_conv"				,       "projection"      )
					if "resConfUnit1" in name:
										__a					:						Dict      =					name.replace("resConfUnit1"				,       "residual_layer1"      )
					if "resConfUnit2" in name:
										__a					:						Any      =					name.replace("resConfUnit2"				,       "residual_layer2"      )
					if "conv1" in name:
										__a					:						Optional[int]      =					name.replace("conv1"				,       "convolution1"      )
					if "conv2" in name:
										__a					:						Optional[int]      =					name.replace("conv2"				,       "convolution2"      )
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										__a					:						Union[str, Any]      =					name.replace("pretrained.act_postprocess1.0.project.0"				,       "neck.reassemble_stage.readout_projects.0.0"      )
					if "pretrained.act_postprocess2.0.project.0" in name:
										__a					:						List[Any]      =					name.replace("pretrained.act_postprocess2.0.project.0"				,       "neck.reassemble_stage.readout_projects.1.0"      )
					if "pretrained.act_postprocess3.0.project.0" in name:
										__a					:						str      =					name.replace("pretrained.act_postprocess3.0.project.0"				,       "neck.reassemble_stage.readout_projects.2.0"      )
					if "pretrained.act_postprocess4.0.project.0" in name:
										__a					:						Optional[int]      =					name.replace("pretrained.act_postprocess4.0.project.0"				,       "neck.reassemble_stage.readout_projects.3.0"      )
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										__a					:						Any      =					name.replace("pretrained.act_postprocess1.3"				,       "neck.reassemble_stage.layers.0.projection"      )
					if "pretrained.act_postprocess1.4" in name:
										__a					:						Optional[int]      =					name.replace("pretrained.act_postprocess1.4"				,       "neck.reassemble_stage.layers.0.resize"      )
					if "pretrained.act_postprocess2.3" in name:
										__a					:						Any      =					name.replace("pretrained.act_postprocess2.3"				,       "neck.reassemble_stage.layers.1.projection"      )
					if "pretrained.act_postprocess2.4" in name:
										__a					:						Any      =					name.replace("pretrained.act_postprocess2.4"				,       "neck.reassemble_stage.layers.1.resize"      )
					if "pretrained.act_postprocess3.3" in name:
										__a					:						Optional[Any]      =					name.replace("pretrained.act_postprocess3.3"				,       "neck.reassemble_stage.layers.2.projection"      )
					if "pretrained.act_postprocess4.3" in name:
										__a					:						str      =					name.replace("pretrained.act_postprocess4.3"				,       "neck.reassemble_stage.layers.3.projection"      )
					if "pretrained.act_postprocess4.4" in name:
										__a					:						str      =					name.replace("pretrained.act_postprocess4.4"				,       "neck.reassemble_stage.layers.3.resize"      )
					if "pretrained" in name:
										__a					:						str      =					name.replace("pretrained"				,       "dpt"      )
					if "bn" in name:
										__a					:						Dict      =					name.replace("bn"				,       "batch_norm"      )
					if "head" in name:
										__a					:						List[str]      =					name.replace("head"				,       "head.head"      )
					if "encoder.norm" in name:
										__a					:						List[str]      =					name.replace("encoder.norm"				,       "layernorm"      )
					if "auxlayer" in name:
										__a					:						Optional[int]      =					name.replace("auxlayer"				,       "auxiliary_head.head"      )

					return name







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					for i in range(config.num_hidden_layers      ):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__a					:						Dict      =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'      )
										__a					:						Union[str, Any]      =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'      )
										# next, add query, keys and values (in that order) to the state dict
										__a					:						Dict      =					in_proj_weight[: config.hidden_size, :]
										__a					:						Dict      =					in_proj_bias[: config.hidden_size]
										__a					:						Optional[int]      =					in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__a					:						int      =					in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__a					:						int      =					in_proj_weight[
										    -config.hidden_size :, :
										]
										__a					:						List[Any]      =					in_proj_bias[-config.hidden_size :]







def     a_   ()				->						str:



					"""simple docstring"""
					__a					:						Optional[int]      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
					__a					:						Tuple      =					Image.open(requests.get(__A				,       stream=__A      ).raw      )
					return im







@torch.no_grad()
def     a_   (__A				,       __A				,       __A				,       __A      )				->						List[str]:



					"""simple docstring"""
					__a      ,			__a					:						List[Any]      =					get_dpt_config(__A      )
					# load original state_dict from URL
					__a					:						int      =					torch.hub.load_state_dict_from_url(__A				,       map_location="cpu"      )
					# remove certain keys
					remove_ignore_keys_(__A      )
					# rename keys
					for key in state_dict.copy().keys():
										__a					:						Union[str, Any]      =					state_dict.pop(__A      )
										__a					:						Optional[int]      =					val
					# read in qkv matrices
					read_in_q_k_v(__A				,       __A      )

					# load HuggingFace model
					__a					:						Dict      =					DPTForSemanticSegmentation(__A      ) if "ade" in checkpoint_url else DPTForDepthEstimation(__A      )
					model.load_state_dict(__A      )
					model.eval()

					# Check outputs on an image
					__a					:						List[str]      =					480 if "ade" in checkpoint_url else 384
					__a					:						Dict      =					DPTImageProcessor(size=__A      )

					__a					:						Dict      =					prepare_img()
					__a					:						Any      =					image_processor(__A				,       return_tensors="pt"      )

					# forward pass
					__a					:						Union[str, Any]      =					model(**__A      ).logits if "ade" in checkpoint_url else model(**__A      ).predicted_depth

					# Assert logits
					__a					:						List[str]      =					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      )
					if "ade" in checkpoint_url:
										__a					:						int      =					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      )
					assert outputs.shape == torch.Size(__A      )
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]				,       __A				,       atol=1e-4      )
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]				,       __A      )
					)

					Path(__A      ).mkdir(exist_ok=__A      )
					print(f'Saving model to {pytorch_dump_folder_path}'      )
					model.save_pretrained(__A      )
					print(f'Saving image processor to {pytorch_dump_folder_path}'      )
					image_processor.save_pretrained(__A      )

					if push_to_hub:
										print("Pushing model to hub..."      )
										model.push_to_hub(
										    repo_path_or_name=Path(__A				,       __A      )				,       organization="nielsr"				,       commit_message="Add model"				,       use_temp_dir=__A				,       )
										image_processor.push_to_hub(
										    repo_path_or_name=Path(__A				,       __A      )				,       organization="nielsr"				,       commit_message="Add image processor"				,       use_temp_dir=__A				,       )


if __name__ == "__main__":
			UpperCAmelCase__      			=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
			    type=str,
			    help='''URL of the original DPT checkpoint you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			)
			parser.add_argument(
			    '''--model_name''',
			    default='''dpt-large''',
			    type=str,
			    help='''Name of the model, in case you\'re pushing to the hub.''',
			)

			UpperCAmelCase__      			=						parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)













from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """glpn"""



						def __init__(self:					Tuple ,					__UpperCAmelCase:					Tuple=3 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Any=[2, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[8, 4, 2, 1] ,					__UpperCAmelCase:					Dict=[32, 64, 160, 256] ,					__UpperCAmelCase:					List[str]=[7, 3, 3, 3] ,					__UpperCAmelCase:					Dict=[4, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[1, 2, 5, 8] ,					__UpperCAmelCase:					Dict=[4, 4, 4, 4] ,					__UpperCAmelCase:					List[str]="gelu" ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					List[Any]=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=1E-6 ,					__UpperCAmelCase:					Dict=64 ,					__UpperCAmelCase:					Dict=10 ,					__UpperCAmelCase:					Union[str, Any]=-1 ,					**__UpperCAmelCase:					Dict ,					)    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )

											__a					:						Optional[Any]      =					num_channels
											__a					:						Tuple      =					num_encoder_blocks
											__a					:						int      =					depths
											__a					:						Union[str, Any]      =					sr_ratios
											__a					:						str      =					hidden_sizes
											__a					:						List[Any]      =					patch_sizes
											__a					:						Optional[Any]      =					strides
											__a					:						Any      =					mlp_ratios
											__a					:						Any      =					num_attention_heads
											__a					:						Any      =					hidden_act
											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						int      =					attention_probs_dropout_prob
											__a					:						List[str]      =					initializer_range
											__a					:						Optional[int]      =					drop_path_rate
											__a					:						Any      =					layer_norm_eps
											__a					:						List[Any]      =					decoder_hidden_size
											__a					:						Any      =					max_depth
											__a					:						int      =					head_in_index





