



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()
UpperCAmelCase__   =       logging.get_logger(__name__)





def 							A	(     _UpperCAmelCase				:   Any    )				-> int:







			'''simple docstring'''
			_UpperCAmelCase		       =   DPTConfig(embedding_type='hybrid'    )

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

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

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

			return config, expected_shape





def 							A	(     _UpperCAmelCase				:   str    )				-> Any:







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





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

			if ".." in name:
						_UpperCAmelCase		       =   name.replace('..'							,   '.'    )

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





def 							A	(     _UpperCAmelCase				:   str							,   _UpperCAmelCase				:   Dict    )				-> 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)
						_UpperCAmelCase		       =   state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"    )
						_UpperCAmelCase		       =   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		       =   in_proj_weight[: config.hidden_size, :]
						_UpperCAmelCase		       =   in_proj_bias[: config.hidden_size]
						_UpperCAmelCase		       =   in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						_UpperCAmelCase		       =   in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						_UpperCAmelCase		       =   in_proj_weight[
						    -config.hidden_size :, :
						]
						_UpperCAmelCase		       =   in_proj_bias[-config.hidden_size :]





def 							A	(     )				-> Optional[int]:







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





@torch.no_grad()
def 							A	(     _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   str    )				-> List[Any]:







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

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

			# Check outputs on an image
			_UpperCAmelCase		       =   480 if '''ade''' in checkpoint_url else 384
			_UpperCAmelCase		       =   DPTImageProcessor(size=_UpperCAmelCase    )

			_UpperCAmelCase		       =   prepare_img()
			_UpperCAmelCase		       =   image_processor(_UpperCAmelCase							,   return_tensors='pt'    )

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

			if show_prediction:
						_UpperCAmelCase		       =   (
						    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__":
							UpperCAmelCase__   =       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",
							)

							UpperCAmelCase__   =       parser.parse_args()
							convert_dpt_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
							)




from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




