


"""simple docstring"""


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  :     Any    					=    logging.get_logger(__name__)


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    DPTConfig(embedding_type="hybrid"   )

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

			if "nyu" or "midas" in checkpoint_url:
						__lowerCAmelCase       					=    768
						__lowerCAmelCase       					=    [1, 1, 1, 0.5]
						__lowerCAmelCase       					=    [256, 512, 768, 768]
						__lowerCAmelCase       					=    150
						__lowerCAmelCase       					=    16
						__lowerCAmelCase       					=    (1, 384, 384)
						__lowerCAmelCase       					=    False
						__lowerCAmelCase       					=    "project"

			if "ade" in checkpoint_url:
						__lowerCAmelCase       					=    True
						__lowerCAmelCase       					=    768
						__lowerCAmelCase       					=    [1, 1, 1, 0.5]
						__lowerCAmelCase       					=    150
						__lowerCAmelCase       					=    16
						__lowerCAmelCase       					=    "huggingface/label-files"
						__lowerCAmelCase       					=    "ade20k-id2label.json"
						__lowerCAmelCase       					=    json.load(open(cached_download(hf_hub_url(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )   )					,				"r"   )   )
						__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
						__lowerCAmelCase       					=    idalabel
						__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}
						__lowerCAmelCase       					=    [1, 150, 480, 480]

			return config, expected_shape


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''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"					,				""   )
			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 and "backbone" not in name:
						__lowerCAmelCase       					=    name.replace("norm1"					,				"layernorm_before"   )
			if "norm2" in name and "backbone" not 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"   )
			if "backbone" in name:
						__lowerCAmelCase       					=    name.replace("backbone"					,				"backbone.bit.encoder"   )

			if ".." in name:
						__lowerCAmelCase       					=    name.replace(".."					,				"."   )

			if "stem.conv" in name:
						__lowerCAmelCase       					=    name.replace("stem.conv"					,				"bit.embedder.convolution"   )
			if "blocks" in name:
						__lowerCAmelCase       					=    name.replace("blocks"					,				"layers"   )
			if "convolution" in name and "backbone" in name:
						__lowerCAmelCase       					=    name.replace("convolution"					,				"conv"   )
			if "layer" in name and "backbone" in name:
						__lowerCAmelCase       					=    name.replace("layer"					,				"layers"   )
			if "backbone.bit.encoder.bit" in name:
						__lowerCAmelCase       					=    name.replace("backbone.bit.encoder.bit"					,				"backbone.bit"   )
			if "embedder.conv" in name:
						__lowerCAmelCase       					=    name.replace("embedder.conv"					,				"embedder.convolution"   )
			if "backbone.bit.encoder.stem.norm" in name:
						__lowerCAmelCase       					=    name.replace("backbone.bit.encoder.stem.norm"					,				"backbone.bit.embedder.norm"   )
			return name


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''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 _lowerCamelCase			(	):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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

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

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

			__lowerCAmelCase       					=    prepare_img()
			__lowerCAmelCase       					=    image_processor(_UpperCamelCase					,				return_tensors="pt"   )

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

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

						Image.fromarray((prediction / prediction.max()) * 255   ).show()

			if pytorch_dump_folder_path is not None:
						Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
						print(f"Saving model to {pytorch_dump_folder_path}"   )
						model.save_pretrained(_UpperCamelCase   )
						print(f"Saving image processor to {pytorch_dump_folder_path}"   )
						image_processor.save_pretrained(_UpperCamelCase   )

			if push_to_hub:
						model.push_to_hub("ybelkada/dpt-hybrid-midas"   )
						image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"   )


if __name__ == "__main__":
	A  :     Dict    					=    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",
	)

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








"""simple docstring"""


import string


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    ""
			for i in sequence:
						__lowerCAmelCase       					=    ord(_UpperCamelCase   )
						if 65 <= extract <= 90:
									output += chr(155 - extract   )
						elif 97 <= extract <= 122:
									output += chr(219 - extract   )
						else:
									output += i
			return output


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    string.ascii_letters
			__lowerCAmelCase       					=    string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
			return "".join(
			    letters_reversed[letters.index(_UpperCamelCase   )] if c in letters else c for c in sequence   )


def _lowerCamelCase			(	):

			'''simple docstring'''




			from timeit import timeit

			print("Running performance benchmarks..."   )
			__lowerCAmelCase       					=    "from string import printable ; from __main__ import atbash, atbash_slow"
			print(f"> atbash_slow(): {timeit('atbash_slow(printable)'					,				setup=_UpperCamelCase   )} seconds"   )
			print(f">      atbash(): {timeit('atbash(printable)'					,				setup=_UpperCamelCase   )} seconds"   )


if __name__ == "__main__":
	for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
		print(f'''{example} encrypted in atbash: {atbash(example)}''')
	benchmark()




