

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_       	=	logging.get_logger(__name__)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	DPTConfig()

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

						if "ade" in checkpoint_url:
												snake_case_   =	True

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

						return config, expected_shape


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


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

						return name


def   __SCREAMING_SNAKE_CASE					(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)
												snake_case_   =	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
												snake_case_   =	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_   =	in_proj_weight[: config.hidden_size, :]
												snake_case_   =	in_proj_bias[: config.hidden_size]
												snake_case_   =	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case_   =	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												snake_case_   =	in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case_   =	in_proj_bias[-config.hidden_size :]


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case_   =	Image.open(requests.get(SCREAMING_SNAKE_CASE__  ,		stream=SCREAMING_SNAKE_CASE__       ).raw       )
						return im


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

						# load HuggingFace model
						snake_case_   =	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
						snake_case_   =	480 if '''ade''' in checkpoint_url else 384
						snake_case_   =	DPTImageProcessor(size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	prepare_img()
						snake_case_   =	image_processor(SCREAMING_SNAKE_CASE__  ,		return_tensors='''pt'''       )

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

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

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


if __name__ == "__main__":
					lowerCAmelCase_       	=	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_       	=	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.abc import Generator


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{}
						snake_case_   =	2
						while True:
												snake_case_   =	factor_map.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												if factor:
																		snake_case_   =	factor + prime
																		while x in factor_map:
																								x += factor
																		snake_case_   =	factor
												else:
																		snake_case_   =	prime
																		yield prime
												prime += 1


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 1E10       ):
						snake_case_   =	sieve()

						snake_case_   =	1
						while True:
												snake_case_   =	next(SCREAMING_SNAKE_CASE__       )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(SCREAMING_SNAKE_CASE__       )
												n += 2


if __name__ == "__main__":
					print(solution())