
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()
lowercase__       =logging.get_logger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  List[str]						=  DPTConfig()

 if "large" in checkpoint_url:
  __a :  Optional[int]						=  1_0_2_4
  __a :  Optional[int]						=  4_0_9_6
  __a :  Optional[int]						=  2_4
  __a :  Tuple						=  1_6
  __a :  int						=  [5, 1_1, 1_7, 2_3]
  __a :  Optional[int]						=  [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
  __a :  Optional[int]						=  (1, 3_8_4, 3_8_4)

 if "ade" in checkpoint_url:
  __a :  List[Any]						=  True

  __a :  Dict						=  1_5_0
  __a :  Union[str, Any]						=  '''huggingface/label-files'''
  __a :  List[str]						=  '''ade20k-id2label.json'''
  __a :  Any						=  json.load(open(cached_download(hf_hub_url(lowerCAmelCase__  ,   lowerCAmelCase__  ,   repo_type='''dataset'''  )  )  ,   '''r'''  )  )
  __a :  Optional[int]						=  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
  __a :  Tuple						=  idalabel
  __a :  Optional[Any]						=  {v: k for k, v in idalabel.items()}
  __a :  str						=  [1, 1_5_0, 4_8_0, 4_8_0]

 return config, expected_shape




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 __a :  str						=  ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
 for k in ignore_keys:
  state_dict.pop(lowerCAmelCase__  ,   lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 if (
     "pretrained.model" in name
     and "cls_token" not in name
     and "pos_embed" not in name
     and "patch_embed" not in name
 ):
  __a :  Tuple						=  name.replace('''pretrained.model'''  ,   '''dpt.encoder'''  )
 if "pretrained.model" in name:
  __a :  Optional[Any]						=  name.replace('''pretrained.model'''  ,   '''dpt.embeddings'''  )
 if "patch_embed" in name:
  __a :  int						=  name.replace('''patch_embed'''  ,   '''patch_embeddings'''  )
 if "pos_embed" in name:
  __a :  Optional[Any]						=  name.replace('''pos_embed'''  ,   '''position_embeddings'''  )
 if "attn.proj" in name:
  __a :  str						=  name.replace('''attn.proj'''  ,   '''attention.output.dense'''  )
 if "proj" in name and "project" not in name:
  __a :  List[str]						=  name.replace('''proj'''  ,   '''projection'''  )
 if "blocks" in name:
  __a :  List[Any]						=  name.replace('''blocks'''  ,   '''layer'''  )
 if "mlp.fc1" in name:
  __a :  Tuple						=  name.replace('''mlp.fc1'''  ,   '''intermediate.dense'''  )
 if "mlp.fc2" in name:
  __a :  Any						=  name.replace('''mlp.fc2'''  ,   '''output.dense'''  )
 if "norm1" in name:
  __a :  Any						=  name.replace('''norm1'''  ,   '''layernorm_before'''  )
 if "norm2" in name:
  __a :  List[Any]						=  name.replace('''norm2'''  ,   '''layernorm_after'''  )
 if "scratch.output_conv" in name:
  __a :  Tuple						=  name.replace('''scratch.output_conv'''  ,   '''head'''  )
 if "scratch" in name:
  __a :  Dict						=  name.replace('''scratch'''  ,   '''neck'''  )
 if "layer1_rn" in name:
  __a :  Any						=  name.replace('''layer1_rn'''  ,   '''convs.0'''  )
 if "layer2_rn" in name:
  __a :  Tuple						=  name.replace('''layer2_rn'''  ,   '''convs.1'''  )
 if "layer3_rn" in name:
  __a :  str						=  name.replace('''layer3_rn'''  ,   '''convs.2'''  )
 if "layer4_rn" in name:
  __a :  Dict						=  name.replace('''layer4_rn'''  ,   '''convs.3'''  )
 if "refinenet" in name:
  __a :  str						=  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
  __a :  Tuple						=  name.replace(f"refinenet{layer_idx}"  ,   f"fusion_stage.layers.{abs(layer_idx-4  )}"  )
 if "out_conv" in name:
  __a :  Optional[Any]						=  name.replace('''out_conv'''  ,   '''projection'''  )
 if "resConfUnit1" in name:
  __a :  Optional[Any]						=  name.replace('''resConfUnit1'''  ,   '''residual_layer1'''  )
 if "resConfUnit2" in name:
  __a :  List[str]						=  name.replace('''resConfUnit2'''  ,   '''residual_layer2'''  )
 if "conv1" in name:
  __a :  Optional[int]						=  name.replace('''conv1'''  ,   '''convolution1'''  )
 if "conv2" in name:
  __a :  List[str]						=  name.replace('''conv2'''  ,   '''convolution2'''  )
 # readout blocks
 if "pretrained.act_postprocess1.0.project.0" in name:
  __a :  Any						=  name.replace('''pretrained.act_postprocess1.0.project.0'''  ,   '''neck.reassemble_stage.readout_projects.0.0'''  )
 if "pretrained.act_postprocess2.0.project.0" in name:
  __a :  str						=  name.replace('''pretrained.act_postprocess2.0.project.0'''  ,   '''neck.reassemble_stage.readout_projects.1.0'''  )
 if "pretrained.act_postprocess3.0.project.0" in name:
  __a :  Optional[Any]						=  name.replace('''pretrained.act_postprocess3.0.project.0'''  ,   '''neck.reassemble_stage.readout_projects.2.0'''  )
 if "pretrained.act_postprocess4.0.project.0" in name:
  __a :  Optional[Any]						=  name.replace('''pretrained.act_postprocess4.0.project.0'''  ,   '''neck.reassemble_stage.readout_projects.3.0'''  )
 # resize blocks
 if "pretrained.act_postprocess1.3" in name:
  __a :  Dict						=  name.replace('''pretrained.act_postprocess1.3'''  ,   '''neck.reassemble_stage.layers.0.projection'''  )
 if "pretrained.act_postprocess1.4" in name:
  __a :  Any						=  name.replace('''pretrained.act_postprocess1.4'''  ,   '''neck.reassemble_stage.layers.0.resize'''  )
 if "pretrained.act_postprocess2.3" in name:
  __a :  int						=  name.replace('''pretrained.act_postprocess2.3'''  ,   '''neck.reassemble_stage.layers.1.projection'''  )
 if "pretrained.act_postprocess2.4" in name:
  __a :  Dict						=  name.replace('''pretrained.act_postprocess2.4'''  ,   '''neck.reassemble_stage.layers.1.resize'''  )
 if "pretrained.act_postprocess3.3" in name:
  __a :  str						=  name.replace('''pretrained.act_postprocess3.3'''  ,   '''neck.reassemble_stage.layers.2.projection'''  )
 if "pretrained.act_postprocess4.3" in name:
  __a :  int						=  name.replace('''pretrained.act_postprocess4.3'''  ,   '''neck.reassemble_stage.layers.3.projection'''  )
 if "pretrained.act_postprocess4.4" in name:
  __a :  Optional[int]						=  name.replace('''pretrained.act_postprocess4.4'''  ,   '''neck.reassemble_stage.layers.3.resize'''  )
 if "pretrained" in name:
  __a :  Tuple						=  name.replace('''pretrained'''  ,   '''dpt'''  )
 if "bn" in name:
  __a :  List[str]						=  name.replace('''bn'''  ,   '''batch_norm'''  )
 if "head" in name:
  __a :  Any						=  name.replace('''head'''  ,   '''head.head'''  )
 if "encoder.norm" in name:
  __a :  List[str]						=  name.replace('''encoder.norm'''  ,   '''layernorm'''  )
 if "auxlayer" in name:
  __a :  Any						=  name.replace('''auxlayer'''  ,   '''auxiliary_head.head'''  )

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ):
 for i in range(config.num_hidden_layers  ):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __a :  Dict						=  state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"  )
  __a :  Optional[int]						=  state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"  )
  # next, add query, keys and values (in that order) to the state dict
  __a :  Union[str, Any]						=  in_proj_weight[: config.hidden_size, :]
  __a :  Tuple						=  in_proj_bias[: config.hidden_size]
  __a :  int						=  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a :  Any						=  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __a :  List[str]						=  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a :  Any						=  in_proj_bias[-config.hidden_size :]




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  Optional[int]						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Tuple  ):
 __a				,       __a :  int						=  get_dpt_config(lowerCAmelCase__  )
 # load original state_dict from URL
 __a :  Union[str, 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():
  __a :  Union[str, Any]						=  state_dict.pop(lowerCAmelCase__  )
  __a :  Optional[Any]						=  val
 # read in qkv matrices
 read_in_q_k_v(lowerCAmelCase__  ,   lowerCAmelCase__  )

 # load HuggingFace model
 __a :  Tuple						=  DPTForSemanticSegmentation(lowerCAmelCase__  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase__  )
 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

 # Check outputs on an image
 __a :  Optional[int]						=  4_8_0 if '''ade''' in checkpoint_url else 3_8_4
 __a :  List[str]						=  DPTImageProcessor(size=lowerCAmelCase__  )

 __a :  str						=  prepare_img()
 __a :  List[str]						=  image_processor(lowerCAmelCase__  ,   return_tensors='''pt'''  )

 # forward pass
 __a :  Tuple						=  model(**lowerCAmelCase__  ).logits if '''ade''' in checkpoint_url else model(**lowerCAmelCase__  ).predicted_depth

 # Assert logits
 __a :  List[Any]						=  torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]  )
 if "ade" in checkpoint_url:
  __a :  int						=  torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]  )
 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__":
      lowercase__       =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.',
      )

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


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ):
 __a :  str						=  len(lowerCAmelCase__  )
 __a :  Optional[int]						=  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a :  str						=  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a :  Tuple						=  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
