


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	:		Optional[int]    =       logging.get_logger(__name__)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		DPTConfig()

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

		if "ade" in checkpoint_url:
				lowercase__					      =		True

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

		return config, expected_shape






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  Optional[Any]:
		"""simple docstring"""




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






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

		return name






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							List[str]					)       ->  Optional[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)
				lowercase__					      =		state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''					)
				lowercase__					      =		state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__					      =		in_proj_weight[: config.hidden_size, :]
				lowercase__					      =		in_proj_bias[: config.hidden_size]
				lowercase__					      =		in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__					      =		in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__					      =		in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__					      =		in_proj_bias[-config.hidden_size :]






def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




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






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Optional[Any]					)       ->  Any:
		"""simple docstring"""




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

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

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

		lowercase__					      =		prepare_img()
		lowercase__					      =		image_processor(__magic_name__   ,   return_tensors="""pt"""					)

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

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

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

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


if __name__ == "__main__":
	A	:		Optional[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.',
	)

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





from __future__ import annotations

from collections import deque



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Any				, _UpperCAmelCase   :						list[str]		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		[]
							self.adlist.append(
							    {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}		)

							for keyword in keywords:
									self.add_keyword(_UpperCAmelCase		)
							self.set_fail_transitions()

					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						str		)						->       int | None:




							"""simple docstring"""


							for state in self.adlist[current_state]["next_states"]:
									if char == self.adlist[state]["value"]:
											return state
							return None

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						str		)						->       None:




							"""simple docstring"""


							lowercase__					      =		0
							for character in keyword:
									lowercase__					      =		self.find_next_state(_UpperCAmelCase				, _UpperCAmelCase		)
									if next_state is None:
											self.adlist.append(
											    {
											        """value""": character,
											        """next_states""": [],
											        """fail_state""": 0,
											        """output""": [],
											    }		)
											self.adlist[current_state]["next_states"].append(len(self.adlist		) - 1		)
											lowercase__					      =		len(self.adlist		) - 1
									else:
											lowercase__					      =		next_state
							self.adlist[current_state]["output"].append(_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Any		)						->       None:




							"""simple docstring"""


							lowercase__					      =		deque()
							for node in self.adlist[0]["next_states"]:
									q.append(_UpperCAmelCase		)
									lowercase__					      =		0
							while q:
									lowercase__					      =		q.popleft()
									for child in self.adlist[r]["next_states"]:
											q.append(_UpperCAmelCase		)
											lowercase__					      =		self.adlist[r]["""fail_state"""]
											while (
											    self.find_next_state(_UpperCAmelCase				, self.adlist[child]["""value"""]		) is None
											    and state != 0
											):
													lowercase__					      =		self.adlist[state]["""fail_state"""]
											lowercase__					      =		self.find_next_state(
											    _UpperCAmelCase				, self.adlist[child]["""value"""]		)
											if self.adlist[child]["fail_state"] is None:
													lowercase__					      =		0
											lowercase__					      =		(
											    self.adlist[child]["""output"""]
											    + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
											)


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						str		)						->       dict[str, list[int]]:




							"""simple docstring"""


							lowercase__					      =		{}  # returns a dict with keywords and list of its occurrences
							lowercase__					      =		0
							for i in range(len(_UpperCAmelCase		)		):
									while (
									    self.find_next_state(_UpperCAmelCase				, string[i]		) is None
									    and current_state != 0
									):
											lowercase__					      =		self.adlist[current_state]["""fail_state"""]
									lowercase__					      =		self.find_next_state(_UpperCAmelCase				, string[i]		)
									if next_state is None:
											lowercase__					      =		0
									else:
											lowercase__					      =		next_state
											for key in self.adlist[current_state]["output"]:
													if key not in result:
															lowercase__					      =		[]
													result[key].append(i - len(_UpperCAmelCase		) + 1		)
							return result


if __name__ == "__main__":
	import doctest

	doctest.testmod()

