




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    :		str			   =       logging.get_logger(__name__)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]					):


						'''simple docstring'''



						lowerCamelCase_							=   DPTConfig()

						if "large" in checkpoint_url:
												lowerCamelCase_							=   10_24
												lowerCamelCase_							=   40_96
												lowerCamelCase_							=   24
												lowerCamelCase_							=   16
												lowerCamelCase_							=   [5, 11, 17, 23]
												lowerCamelCase_							=   [2_56, 5_12, 10_24, 10_24]
												lowerCamelCase_							=   (1, 3_84, 3_84)

						if "ade" in checkpoint_url:
												lowerCamelCase_							=   True

												lowerCamelCase_							=   1_50
												lowerCamelCase_							=   'huggingface/label-files'
												lowerCamelCase_							=   'ade20k-id2label.json'
												lowerCamelCase_							=   json.load(open(cached_download(hf_hub_url(lowercase     ,							lowercase     ,							repo_type='dataset'					)					)     ,							'r'					)					)
												lowerCamelCase_							=   {int(lowercase					): v for k, v in idalabel.items()}
												lowerCamelCase_							=   idalabel
												lowerCamelCase_							=   {v: k for k, v in idalabel.items()}
												lowerCamelCase_							=   [1, 1_50, 4_80, 4_80]

						return config, expected_shape

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple					):


						'''simple docstring'''



						lowerCamelCase_							=   ['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(lowercase     ,							lowercase					)

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

						return name

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	List[Any]					):


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

def       _SCREAMING_SNAKE_CASE      (							):


						'''simple docstring'''



						lowerCamelCase_							=   'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCamelCase_							=   Image.open(requests.get(lowercase     ,							stream=lowercase					).raw					)
						return im

@torch.no_grad()
def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[Any]     ,							lowercase     :	str     ,							lowercase     :	Optional[Any]					):


						'''simple docstring'''



						lowerCamelCase_					,    lowerCamelCase_							=   get_dpt_config(lowercase					)
						# load original state_dict from URL
						lowerCamelCase_							=   torch.hub.load_state_dict_from_url(lowercase     ,							map_location='cpu'					)
						# remove certain keys
						remove_ignore_keys_(lowercase					)
						# rename keys
						for key in state_dict.copy().keys():
												lowerCamelCase_							=   state_dict.pop(lowercase					)
												lowerCamelCase_							=   val
						# read in qkv matrices
						read_in_q_k_v(lowercase     ,							lowercase					)

						# load HuggingFace model
						lowerCamelCase_							=   DPTForSemanticSegmentation(lowercase					) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase					)
						model.load_state_dict(lowercase					)
						model.eval()

						# Check outputs on an image
						lowerCamelCase_							=   4_80 if 'ade' in checkpoint_url else 3_84
						lowerCamelCase_							=   DPTImageProcessor(size=lowercase					)

						lowerCamelCase_							=   prepare_img()
						lowerCamelCase_							=   image_processor(lowercase     ,							return_tensors='pt'					)

						# forward pass
						lowerCamelCase_							=   model(**lowercase					).logits if 'ade' in checkpoint_url else model(**lowercase					).predicted_depth

						# Assert logits
						lowerCamelCase_							=   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:
												lowerCamelCase_							=   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(lowercase					)
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]     ,							lowercase     ,							atol=1e-4					)
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]     ,							lowercase					)
						)

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

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


if __name__ == "__main__":
	lowerCamelCase    :		int			   =       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.",
	)

	lowerCamelCase    :		Optional[int]			   =       parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''keras_nlp''']

 def __init__(    self :  Optional[int]						,							*A_ :  Any						,							**A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       requires_backends(self						,							['keras_nlp']  )



