

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()
_lowerCAmelCase  :      Union[str, Any]			 =logging.get_logger(__name__)



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[int]									= DPTConfig(embedding_type="hybrid"      )

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

	if "nyu" or "midas" in checkpoint_url:
		UpperCAmelCase__:	Union[str, Any]									= 7_6_8
		UpperCAmelCase__:	Tuple									= [1, 1, 1, 0.5]
		UpperCAmelCase__:	Tuple									= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
		UpperCAmelCase__:	int									= 1_5_0
		UpperCAmelCase__:	Optional[Any]									= 1_6
		UpperCAmelCase__:	List[str]									= (1, 3_8_4, 3_8_4)
		UpperCAmelCase__:	int									= False
		UpperCAmelCase__:	List[Any]									= "project"

	if "ade" in checkpoint_url:
		UpperCAmelCase__:	str									= True
		UpperCAmelCase__:	Optional[Any]									= 7_6_8
		UpperCAmelCase__:	Tuple									= [1, 1, 1, 0.5]
		UpperCAmelCase__:	int									= 1_5_0
		UpperCAmelCase__:	Any									= 1_6
		UpperCAmelCase__:	int									= "huggingface/label-files"
		UpperCAmelCase__:	Optional[Any]									= "ade20k-id2label.json"
		UpperCAmelCase__:	Union[str, Any]									= json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,repo_type="dataset"      )      )	,"r"      )      )
		UpperCAmelCase__:	Optional[Any]									= {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
		UpperCAmelCase__:	Any									= idalabel
		UpperCAmelCase__:	int									= {v: k for k, v in idalabel.items()}
		UpperCAmelCase__:	Optional[int]									= [1, 1_5_0, 4_8_0, 4_8_0]

	return config, expected_shape



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= ["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      ):
	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		UpperCAmelCase__:	List[str]									= name.replace("pretrained.model"	,"dpt.encoder"      )
	if "pretrained.model" in name:
		UpperCAmelCase__:	int									= name.replace("pretrained.model"	,"dpt.embeddings"      )
	if "patch_embed" in name:
		UpperCAmelCase__:	List[str]									= name.replace("patch_embed"	,""      )
	if "pos_embed" in name:
		UpperCAmelCase__:	str									= name.replace("pos_embed"	,"position_embeddings"      )
	if "attn.proj" in name:
		UpperCAmelCase__:	Optional[Any]									= name.replace("attn.proj"	,"attention.output.dense"      )
	if "proj" in name and "project" not in name:
		UpperCAmelCase__:	str									= name.replace("proj"	,"projection"      )
	if "blocks" in name:
		UpperCAmelCase__:	Optional[Any]									= name.replace("blocks"	,"layer"      )
	if "mlp.fc1" in name:
		UpperCAmelCase__:	Dict									= name.replace("mlp.fc1"	,"intermediate.dense"      )
	if "mlp.fc2" in name:
		UpperCAmelCase__:	Dict									= name.replace("mlp.fc2"	,"output.dense"      )
	if "norm1" in name and "backbone" not in name:
		UpperCAmelCase__:	Tuple									= name.replace("norm1"	,"layernorm_before"      )
	if "norm2" in name and "backbone" not in name:
		UpperCAmelCase__:	Tuple									= name.replace("norm2"	,"layernorm_after"      )
	if "scratch.output_conv" in name:
		UpperCAmelCase__:	Tuple									= name.replace("scratch.output_conv"	,"head"      )
	if "scratch" in name:
		UpperCAmelCase__:	int									= name.replace("scratch"	,"neck"      )
	if "layer1_rn" in name:
		UpperCAmelCase__:	Union[str, Any]									= name.replace("layer1_rn"	,"convs.0"      )
	if "layer2_rn" in name:
		UpperCAmelCase__:	List[str]									= name.replace("layer2_rn"	,"convs.1"      )
	if "layer3_rn" in name:
		UpperCAmelCase__:	Dict									= name.replace("layer3_rn"	,"convs.2"      )
	if "layer4_rn" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("layer4_rn"	,"convs.3"      )
	if "refinenet" in name:
		UpperCAmelCase__:	Optional[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
		UpperCAmelCase__:	Tuple									= name.replace(f"refinenet{layer_idx}"	,f"fusion_stage.layers.{abs(layer_idx-4      )}"      )
	if "out_conv" in name:
		UpperCAmelCase__:	List[str]									= name.replace("out_conv"	,"projection"      )
	if "resConfUnit1" in name:
		UpperCAmelCase__:	int									= name.replace("resConfUnit1"	,"residual_layer1"      )
	if "resConfUnit2" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("resConfUnit2"	,"residual_layer2"      )
	if "conv1" in name:
		UpperCAmelCase__:	List[str]									= name.replace("conv1"	,"convolution1"      )
	if "conv2" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("conv2"	,"convolution2"      )
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		UpperCAmelCase__:	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:
		UpperCAmelCase__:	Dict									= name.replace("pretrained.act_postprocess2.0.project.0"	,"neck.reassemble_stage.readout_projects.1.0"      )
	if "pretrained.act_postprocess3.0.project.0" in name:
		UpperCAmelCase__:	Tuple									= name.replace("pretrained.act_postprocess3.0.project.0"	,"neck.reassemble_stage.readout_projects.2.0"      )
	if "pretrained.act_postprocess4.0.project.0" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("pretrained.act_postprocess4.0.project.0"	,"neck.reassemble_stage.readout_projects.3.0"      )

	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		UpperCAmelCase__:	int									= name.replace("pretrained.act_postprocess1.3"	,"neck.reassemble_stage.layers.0.projection"      )
	if "pretrained.act_postprocess1.4" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("pretrained.act_postprocess1.4"	,"neck.reassemble_stage.layers.0.resize"      )
	if "pretrained.act_postprocess2.3" in name:
		UpperCAmelCase__:	Tuple									= name.replace("pretrained.act_postprocess2.3"	,"neck.reassemble_stage.layers.1.projection"      )
	if "pretrained.act_postprocess2.4" in name:
		UpperCAmelCase__:	Any									= name.replace("pretrained.act_postprocess2.4"	,"neck.reassemble_stage.layers.1.resize"      )
	if "pretrained.act_postprocess3.3" in name:
		UpperCAmelCase__:	str									= name.replace("pretrained.act_postprocess3.3"	,"neck.reassemble_stage.layers.2.projection"      )
	if "pretrained.act_postprocess4.3" in name:
		UpperCAmelCase__:	Any									= name.replace("pretrained.act_postprocess4.3"	,"neck.reassemble_stage.layers.3.projection"      )
	if "pretrained.act_postprocess4.4" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("pretrained.act_postprocess4.4"	,"neck.reassemble_stage.layers.3.resize"      )
	if "pretrained" in name:
		UpperCAmelCase__:	Dict									= name.replace("pretrained"	,"dpt"      )
	if "bn" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("bn"	,"batch_norm"      )
	if "head" in name:
		UpperCAmelCase__:	Tuple									= name.replace("head"	,"head.head"      )
	if "encoder.norm" in name:
		UpperCAmelCase__:	Tuple									= name.replace("encoder.norm"	,"layernorm"      )
	if "auxlayer" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("auxlayer"	,"auxiliary_head.head"      )
	if "backbone" in name:
		UpperCAmelCase__:	Tuple									= name.replace("backbone"	,"backbone.bit.encoder"      )

	if ".." in name:
		UpperCAmelCase__:	Optional[int]									= name.replace(".."	,"."      )

	if "stem.conv" in name:
		UpperCAmelCase__:	int									= name.replace("stem.conv"	,"bit.embedder.convolution"      )
	if "blocks" in name:
		UpperCAmelCase__:	Tuple									= name.replace("blocks"	,"layers"      )
	if "convolution" in name and "backbone" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("convolution"	,"conv"      )
	if "layer" in name and "backbone" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("layer"	,"layers"      )
	if "backbone.bit.encoder.bit" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("backbone.bit.encoder.bit"	,"backbone.bit"      )
	if "embedder.conv" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("embedder.conv"	,"embedder.convolution"      )
	if "backbone.bit.encoder.stem.norm" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("backbone.bit.encoder.stem.norm"	,"backbone.bit.embedder.norm"      )
	return name



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



def 			_A	(      ):
	UpperCAmelCase__:	Optional[Any]									= "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase__:	Dict									= Image.open(requests.get(SCREAMING_SNAKE_CASE	,stream=SCREAMING_SNAKE_CASE      ).raw      )
	return im



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= get_dpt_config(SCREAMING_SNAKE_CASE      )
	# load original state_dict from URL
	# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
	UpperCAmelCase__:	Tuple									= torch.load(SCREAMING_SNAKE_CASE	,map_location="cpu"      )
	# remove certain keys
	remove_ignore_keys_(SCREAMING_SNAKE_CASE      )
	# rename keys
	for key in state_dict.copy().keys():
		UpperCAmelCase__:	Dict									= state_dict.pop(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Dict									= val
	# read in qkv matrices
	read_in_q_k_v(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	# load HuggingFace model
	UpperCAmelCase__:	int									= 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
	UpperCAmelCase__:	str									= 4_8_0 if "ade" in checkpoint_url else 3_8_4
	UpperCAmelCase__:	List[Any]									= DPTImageProcessor(size=SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	List[Any]									= prepare_img()
	UpperCAmelCase__:	Any									= image_processor(SCREAMING_SNAKE_CASE	,return_tensors="pt"      )

	# forward pass
	UpperCAmelCase__:	Optional[int]									= model(**SCREAMING_SNAKE_CASE      ).logits if "ade" in checkpoint_url else model(**SCREAMING_SNAKE_CASE      ).predicted_depth

	if show_prediction:
		UpperCAmelCase__:	Dict									= (
		    torch.nn.functional.interpolate(
		        outputs.unsqueeze(1      )	,size=(image.size[1], image.size[0])	,mode="bicubic"	,align_corners=SCREAMING_SNAKE_CASE	,)
		    .squeeze()
		    .cpu()
		    .numpy()
		)

		Image.fromarray((prediction / prediction.max()) * 2_5_5      ).show()

	if pytorch_dump_folder_path is not None:
		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:
		model.push_to_hub("ybelkada/dpt-hybrid-midas"      )
		image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =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=False,
					    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.""",
					)
					parser.add_argument(
					    """--show_prediction""",
					    action="""store_true""",
					)

					_lowerCAmelCase  :      List[str]			 =parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Any			 =[int(item) for item in user_input.split(""",""")]
					print(stooge_sort(unsorted))