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					:	Tuple       =  logging.get_logger(__name__)




def       _snake_case	(   lowerCAmelCase							:   List[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    DPTConfig(embedding_type="hybrid"	)

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

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

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

		return config, expected_shape




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    ["pretrained.model.head.weight", "pretrained.model.head.bias"]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase   , lowerCAmelCase	)




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

		if ".." in name:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    name.replace(".."   , "."	)

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




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   int	):
		"""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)
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
				SCREAMING_SNAKE_CASE_  :		str				      =    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_  :		List[str]				      =    in_proj_weight[: config.hidden_size, :]
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    in_proj_bias[: config.hidden_size]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_  :		Dict				      =    in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    in_proj_bias[-config.hidden_size :]




def       _snake_case	(   ):
		"""simple docstring"""







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




@torch.no_grad()
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    get_dpt_config(lowerCAmelCase	)
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		SCREAMING_SNAKE_CASE_  :		Any				      =    torch.load(lowerCAmelCase   , map_location="cpu"	)
		# remove certain keys
		remove_ignore_keys_(lowerCAmelCase	)
		# rename keys
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    state_dict.pop(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    val
		# read in qkv matrices
		read_in_q_k_v(lowerCAmelCase   , lowerCAmelCase	)

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    DPTForSemanticSegmentation(lowerCAmelCase	) if "ade" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase	)
		model.load_state_dict(lowerCAmelCase	)
		model.eval()

		# Check outputs on an image
		SCREAMING_SNAKE_CASE_  :		int				      =    4_8_0 if "ade" in checkpoint_url else 3_8_4
		SCREAMING_SNAKE_CASE_  :		str				      =    DPTImageProcessor(size=lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		int				      =    prepare_img()
		SCREAMING_SNAKE_CASE_  :		Dict				      =    image_processor(lowerCAmelCase   , return_tensors="pt"	)

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

		if show_prediction:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
				    torch.nn.functional.interpolate(
				        outputs.unsqueeze(1	)   , size=(image.size[1], image.size[0])   , mode="bicubic"   , align_corners=lowerCAmelCase   , )
				    .squeeze()
				    .cpu()
				    .numpy()
				)

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

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


if __name__ == "__main__":
		__lowerCamelCase					:	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=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       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		while b:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    b, a % b
		return a




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase   , a % b	)




def       _snake_case	(   ):
		"""simple docstring"""







		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3   , 5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5   , 3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1   , 3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3   , 6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6   , 3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3   , 5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5   , 3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1   , 3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3   , 6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6   , 3	)}'	)


if __name__ == "__main__":
		main()
