


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		UNetaDModel
			lowerCAmelCase       :		ScoreSdeVeScheduler




			def __init__(						self     :   Any		,     lowerCamelCase__     :   UNetaDModel		,     lowerCamelCase__     :   ScoreSdeVeScheduler       )      ->int:



					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=lowerCamelCase__		,     scheduler=lowerCamelCase__       )






			@torch.no_grad()
			def __call__(						self     :   Optional[Any]		,     lowerCamelCase__     :   int = 1		,     lowerCamelCase__     :   int = 20_00		,     lowerCamelCase__     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     lowerCamelCase__     :   Optional[str] = "pil"		,     lowerCamelCase__     :   bool = True		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->Union[ImagePipelineOutput, Tuple]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					self.unet.config.sample_size
					_UpperCAmelCase						:				Dict	    =					(batch_size, 3, img_size, img_size)

					_UpperCAmelCase						:				Dict	    =					self.unet

					_UpperCAmelCase						:				Optional[int]	    =					randn_tensor(lowerCamelCase__		,     generator=lowerCamelCase__       ) * self.scheduler.init_noise_sigma
					_UpperCAmelCase						:				Dict	    =					sample.to(self.device       )

					self.scheduler.set_timesteps(lowerCamelCase__       )
					self.scheduler.set_sigmas(lowerCamelCase__       )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
							_UpperCAmelCase						:				Optional[Any]	    =					self.scheduler.sigmas[i] * torch.ones(shape[0]		,     device=self.device       )

							# correction step
							for _ in range(self.scheduler.config.correct_steps       ):
									_UpperCAmelCase						:				int	    =					self.unet(lowerCamelCase__		,     lowerCamelCase__       ).sample
									_UpperCAmelCase						:				Dict	    =					self.scheduler.step_correct(lowerCamelCase__		,     lowerCamelCase__		,     generator=lowerCamelCase__       ).prev_sample

							# prediction step
							_UpperCAmelCase						:				List[Any]	    =					model(lowerCamelCase__		,     lowerCamelCase__       ).sample
							_UpperCAmelCase						:				Optional[Any]	    =					self.scheduler.step_pred(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     generator=lowerCamelCase__       )

							_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[int]	    =					output.prev_sample, output.prev_sample_mean

					_UpperCAmelCase						:				Optional[int]	    =					sample_mean.clamp(0		,     1       )
					_UpperCAmelCase						:				Optional[Any]	    =					sample.cpu().permute(0		,     2		,     3		,     1       ).numpy()
					if output_type == "pil":
							_UpperCAmelCase						:				int	    =					self.numpy_to_pil(lowerCamelCase__       )

					if not return_dict:
							return (sample,)

					return ImagePipelineOutput(images=lowerCamelCase__       )







'''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()
lowerCamelCase__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					DPTConfig()

		if "large" in checkpoint_url:
				_UpperCAmelCase						:				List[str]	    =					1_024
				_UpperCAmelCase						:				Optional[int]	    =					4_096
				_UpperCAmelCase						:				Union[str, Any]	    =					24
				_UpperCAmelCase						:				List[Any]	    =					16
				_UpperCAmelCase						:				List[Any]	    =					[5, 11, 17, 23]
				_UpperCAmelCase						:				int	    =					[256, 512, 1_024, 1_024]
				_UpperCAmelCase						:				Optional[Any]	    =					(1, 384, 384)

		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				Optional[int]	    =					True

				_UpperCAmelCase						:				List[Any]	    =					150
				_UpperCAmelCase						:				Optional[Any]	    =					"huggingface/label-files"
				_UpperCAmelCase						:				Optional[int]	    =					"ade20k-id2label.json"
				_UpperCAmelCase						:				Tuple	    =					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase					,       __lowerCAmelCase					,       repo_type="dataset"      )      )					,       "r"      )      )
				_UpperCAmelCase						:				str	    =					{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					idalabel
				_UpperCAmelCase						:				Union[str, Any]	    =					{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					[1, 150, 480, 480]

		return config, expected_shape






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
		for k in ignore_keys:
				state_dict.pop(__lowerCAmelCase					,       __lowerCAmelCase      )






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

		return name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for i in range(config.num_hidden_layers      ):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				_UpperCAmelCase						:				int	    =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
				_UpperCAmelCase						:				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
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[: config.hidden_size, :]
				_UpperCAmelCase						:				Dict	    =					in_proj_bias[: config.hidden_size]
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCAmelCase						:				int	    =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCAmelCase						:				Union[str, Any]	    =					in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCAmelCase						:				str	    =					in_proj_bias[-config.hidden_size :]






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Any	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )
		return im






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

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

		# Check outputs on an image
		_UpperCAmelCase						:				Any	    =					480 if "ade" in checkpoint_url else 384
		_UpperCAmelCase						:				List[str]	    =					DPTImageProcessor(size=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					prepare_img()
		_UpperCAmelCase						:				Dict	    =					image_processor(__lowerCAmelCase					,       return_tensors="pt"      )

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

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

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


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)



