
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()
_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase			)       ->      Dict:
     UpperCamelCase						    =					DPTConfig(embedding_type='hybrid'			)

     if "large" in checkpoint_url:
          UpperCamelCase						    =					1024
          UpperCamelCase						    =					4096
          UpperCamelCase						    =					24
          UpperCamelCase						    =					16
          UpperCamelCase						    =					[5, 11, 17, 23]
          UpperCamelCase						    =					[256, 512, 1024, 1024]
          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(_lowercase						,     _lowercase						,     repo_type='dataset'			)			)						,     'r'			)			)
          UpperCamelCase						    =					{int(_lowercase			): 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  __lowerCamelCase					( _lowercase			)       ->      str:
     UpperCamelCase						    =					['pretrained.model.head.weight', 'pretrained.model.head.bias']
     for k in ignore_keys:
          state_dict.pop(_lowercase						,     _lowercase			)

def  __lowerCamelCase					( _lowercase			)       ->      str:
     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  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     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  __lowerCamelCase					( )       ->      Optional[Any]:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)
     return im

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					get_dpt_config(_lowercase			)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location='cpu'			)
     # remove certain keys
     remove_ignore_keys_(_lowercase			)
     # rename keys
     for key in state_dict.copy().keys():
          UpperCamelCase						    =					state_dict.pop(_lowercase			)
          UpperCamelCase						    =					val
     # read in qkv matrices
     read_in_q_k_v(_lowercase						,     _lowercase			)

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

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

     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					image_processor(_lowercase						,     return_tensors='pt'			)

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

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

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

     if pytorch_dump_folder_path is not None:
          Path(_lowercase			).mkdir(exist_ok=_lowercase			)
          print(F'Saving model to {pytorch_dump_folder_path}'			)
          model.save_pretrained(_lowercase			)
          print(F'Saving image processor to {pytorch_dump_folder_path}'			)
          image_processor.save_pretrained(_lowercase			)

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


if __name__ == "__main__":
 _snake_case      = 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''',
 )

 _snake_case      = 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 functools import lru_cache

@lru_cache
def  __lowerCamelCase					( _lowercase			)       ->      int:
     if num < 0:
          raise ValueError('Number should not be negative.'			)

     return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


