
'''simple docstring'''







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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= DPTConfig()

						if "large" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= 1024
												_SCREAMING_SNAKE_CASE				= 4096
												_SCREAMING_SNAKE_CASE				= 24
												_SCREAMING_SNAKE_CASE				= 16
												_SCREAMING_SNAKE_CASE				= [5, 11, 17, 23]
												_SCREAMING_SNAKE_CASE				= [256, 512, 1024, 1024]
												_SCREAMING_SNAKE_CASE				= (1, 384, 384)

						if "ade" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= True

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

						return config, expected_shape



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						_SCREAMING_SNAKE_CASE				= ["pretrained.model.head.weight", "pretrained.model.head.bias"]
						for k in ignore_keys:
												state_dict.pop(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.model"	,			"dpt.encoder"  )
						if "pretrained.model" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.model"	,			"dpt.embeddings"  )
						if "patch_embed" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("patch_embed"	,			"patch_embeddings"  )
						if "pos_embed" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pos_embed"	,			"position_embeddings"  )
						if "attn.proj" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("attn.proj"	,			"attention.output.dense"  )
						if "proj" in name and "project" not in name:
												_SCREAMING_SNAKE_CASE				= name.replace("proj"	,			"projection"  )
						if "blocks" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("blocks"	,			"layer"  )
						if "mlp.fc1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("mlp.fc1"	,			"intermediate.dense"  )
						if "mlp.fc2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("mlp.fc2"	,			"output.dense"  )
						if "norm1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("norm1"	,			"layernorm_before"  )
						if "norm2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("norm2"	,			"layernorm_after"  )
						if "scratch.output_conv" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("scratch.output_conv"	,			"head"  )
						if "scratch" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("scratch"	,			"neck"  )
						if "layer1_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer1_rn"	,			"convs.0"  )
						if "layer2_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer2_rn"	,			"convs.1"  )
						if "layer3_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer3_rn"	,			"convs.2"  )
						if "layer4_rn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("layer4_rn"	,			"convs.3"  )
						if "refinenet" in name:
												_SCREAMING_SNAKE_CASE				= 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
												_SCREAMING_SNAKE_CASE				= name.replace(F'refinenet{layer_idx}'	,			F'fusion_stage.layers.{abs(layer_idx-4  )}'  )
						if "out_conv" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("out_conv"	,			"projection"  )
						if "resConfUnit1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("resConfUnit1"	,			"residual_layer1"  )
						if "resConfUnit2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("resConfUnit2"	,			"residual_layer2"  )
						if "conv1" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("conv1"	,			"convolution1"  )
						if "conv2" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("conv2"	,			"convolution2"  )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.0.project.0"	,			"neck.reassemble_stage.readout_projects.0.0"  )
						if "pretrained.act_postprocess2.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.0.project.0"	,			"neck.reassemble_stage.readout_projects.1.0"  )
						if "pretrained.act_postprocess3.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess3.0.project.0"	,			"neck.reassemble_stage.readout_projects.2.0"  )
						if "pretrained.act_postprocess4.0.project.0" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.0.project.0"	,			"neck.reassemble_stage.readout_projects.3.0"  )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.3"	,			"neck.reassemble_stage.layers.0.projection"  )
						if "pretrained.act_postprocess1.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess1.4"	,			"neck.reassemble_stage.layers.0.resize"  )
						if "pretrained.act_postprocess2.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.3"	,			"neck.reassemble_stage.layers.1.projection"  )
						if "pretrained.act_postprocess2.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess2.4"	,			"neck.reassemble_stage.layers.1.resize"  )
						if "pretrained.act_postprocess3.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess3.3"	,			"neck.reassemble_stage.layers.2.projection"  )
						if "pretrained.act_postprocess4.3" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.3"	,			"neck.reassemble_stage.layers.3.projection"  )
						if "pretrained.act_postprocess4.4" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained.act_postprocess4.4"	,			"neck.reassemble_stage.layers.3.resize"  )
						if "pretrained" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("pretrained"	,			"dpt"  )
						if "bn" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("bn"	,			"batch_norm"  )
						if "head" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("head"	,			"head.head"  )
						if "encoder.norm" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("encoder.norm"	,			"layernorm"  )
						if "auxlayer" in name:
												_SCREAMING_SNAKE_CASE				= name.replace("auxlayer"	,			"auxiliary_head.head"  )

						return name



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



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "http://images.cocodataset.org/val2017/000000039769.jpg"
						_SCREAMING_SNAKE_CASE				= Image.open(requests.get(_SCREAMING_SNAKE_CASE	,			stream=_SCREAMING_SNAKE_CASE  ).raw  )
						return im



@torch.no_grad()
def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= get_dpt_config(_SCREAMING_SNAKE_CASE  )
						# load original state_dict from URL
						_SCREAMING_SNAKE_CASE				= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,			map_location="cpu"  )
						# remove certain keys
						remove_ignore_keys_(_SCREAMING_SNAKE_CASE  )
						# rename keys
						for key in state_dict.copy().keys():
												_SCREAMING_SNAKE_CASE				= state_dict.pop(_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= val
						# read in qkv matrices
						read_in_q_k_v(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						# load HuggingFace model
						_SCREAMING_SNAKE_CASE				= DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE  ) if "ade" in checkpoint_url else DPTForDepthEstimation(_SCREAMING_SNAKE_CASE  )
						model.load_state_dict(_SCREAMING_SNAKE_CASE  )
						model.eval()

						# Check outputs on an image
						_SCREAMING_SNAKE_CASE				= 480 if "ade" in checkpoint_url else 384
						_SCREAMING_SNAKE_CASE				= DPTImageProcessor(size=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= prepare_img()
						_SCREAMING_SNAKE_CASE				= image_processor(_SCREAMING_SNAKE_CASE	,			return_tensors="pt"  )

						# forward pass
						_SCREAMING_SNAKE_CASE				= model(**_SCREAMING_SNAKE_CASE  ).logits if "ade" in checkpoint_url else model(**_SCREAMING_SNAKE_CASE  ).predicted_depth

						# Assert logits
						_SCREAMING_SNAKE_CASE				= torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]  )
						if "ade" in checkpoint_url:
												_SCREAMING_SNAKE_CASE				= torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]  )
						assert outputs.shape == torch.Size(_SCREAMING_SNAKE_CASE  )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]	,			_SCREAMING_SNAKE_CASE	,			atol=1e-4  )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]	,			_SCREAMING_SNAKE_CASE  )
						)

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

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


if __name__ == "__main__":
						_snake_case				:   List[Any]     =				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.""",
						)

						_snake_case				:   Union[str, Any]     =				parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


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


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								warnings.warn(
								    "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
								    " use DPTImageProcessor instead."					,						UpperCamelCase					,						)
								super().__init__(*UpperCamelCase					,						**UpperCamelCase      )