



"""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()
A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple ):
						"""simple docstring"""






						snake_case   :			Any						    = DPTConfig()

						if "large" in checkpoint_url:
												snake_case   :			Tuple						    = 1_0_2_4
												snake_case   :			str						    = 4_0_9_6
												snake_case   :			List[str]						    = 2_4
												snake_case   :			str						    = 1_6
												snake_case   :			int						    = [5, 1_1, 1_7, 2_3]
												snake_case   :			Union[str, Any]						    = [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												snake_case   :			Tuple						    = (1, 3_8_4, 3_8_4)

						if "ade" in checkpoint_url:
												snake_case   :			List[Any]						    = True

												snake_case   :			Tuple						    = 1_5_0
												snake_case   :			Optional[Any]						    = "huggingface/label-files"
												snake_case   :			str						    = "ade20k-id2label.json"
												snake_case   :			Optional[Any]						    = json.load(open(cached_download(hf_hub_url(lowerCamelCase_     ,		lowerCamelCase_     ,		repo_type="dataset" ) )     ,		"r" ) )
												snake_case   :			List[Any]						    = {int(lowerCamelCase_ ): v for k, v in idalabel.items()}
												snake_case   :			Tuple						    = idalabel
												snake_case   :			List[Any]						    = {v: k for k, v in idalabel.items()}
												snake_case   :			Tuple						    = [1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
						for k in ignore_keys:
												state_dict.pop(lowerCamelCase_     ,		lowerCamelCase_ )

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

						return name

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Tuple ):
						"""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)
												snake_case   :			int						    = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
												snake_case   :			Tuple						    = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
												# next, add query, keys and values (in that order) to the state dict
												snake_case   :			List[Any]						    = in_proj_weight[: config.hidden_size, :]
												snake_case   :			List[str]						    = in_proj_bias[: config.hidden_size]
												snake_case   :			Optional[int]						    = in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case   :			Union[str, Any]						    = in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												snake_case   :			Optional[int]						    = in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case   :			Any						    = in_proj_bias[-config.hidden_size :]

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						snake_case   :			str						    = Image.open(requests.get(lowerCamelCase_     ,		stream=lowerCamelCase_ ).raw )
						return im

@torch.no_grad()
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






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

						# load HuggingFace model
						snake_case   :			List[str]						    = DPTForSemanticSegmentation(lowerCamelCase_ ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_ )
						model.load_state_dict(lowerCamelCase_ )
						model.eval()

						# Check outputs on an image
						snake_case   :			Any						    = 4_8_0 if "ade" in checkpoint_url else 3_8_4
						snake_case   :			str						    = DPTImageProcessor(size=lowerCamelCase_ )

						snake_case   :			Union[str, Any]						    = prepare_img()
						snake_case   :			Dict						    = image_processor(lowerCamelCase_     ,		return_tensors="pt" )

						# forward pass
						snake_case   :			Dict						    = model(**lowerCamelCase_ ).logits if "ade" in checkpoint_url else model(**lowerCamelCase_ ).predicted_depth

						# Assert logits
						snake_case   :			Optional[int]						    = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
						if "ade" in checkpoint_url:
												snake_case   :			int						    = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
						assert outputs.shape == torch.Size(lowerCamelCase_ )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]     ,		lowerCamelCase_     ,		atol=1e-4 )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]     ,		lowerCamelCase_ )
						)

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

						if push_to_hub:
												print("Pushing model to 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__":
			A		=						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.',
			)

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








"""simple docstring"""







import requests

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[str]						    = {"Content-Type": "application/json"}
						snake_case   :			int						    = requests.post(lowerCamelCase_     ,		json={"text": message_body}     ,		headers=lowerCamelCase_ )
						if response.status_code != 2_0_0:
												snake_case   :			str						    = (
												    "Request to slack returned an error "
												    f'''{response.status_code}, the response is:\n{response.text}'''
												)
												raise ValueError(lowerCamelCase_ )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')



