






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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)





def        _snake_case   (_snake_case				:       Optional[Any])					->	List[Any]:
   _lowercase              =DPTConfig()

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

   if "ade" in checkpoint_url:
      _lowercase              =True

      _lowercase              =150
      _lowercase              ='huggingface/label-files'
      _lowercase              ='ade20k-id2label.json'
      _lowercase              =json.load(open(cached_download(hf_hub_url(_snake_case      ,    _snake_case      ,    repo_type='dataset'))      ,    'r'))
      _lowercase              ={int(_snake_case): v for k, v in idalabel.items()}
      _lowercase              =idalabel
      _lowercase              ={v: k for k, v in idalabel.items()}
      _lowercase              =[1, 150, 480, 480]

   return config, expected_shape





def        _snake_case   (_snake_case				:       int)					->	Union[str, Any]:
   _lowercase              =['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(_snake_case      ,    _snake_case)





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

   return name





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





def        _snake_case   ()					->	Tuple:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)
   return im





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

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

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

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

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

   # Assert logits
   _lowercase              =torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]])
   if "ade" in checkpoint_url:
      _lowercase              =torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]])
   assert outputs.shape == torch.Size(_snake_case)
   assert (
       torch.allclose(outputs[0, 0, :3, :3]      ,    _snake_case      ,    atol=1E-4)
       if "ade" in checkpoint_url
       else torch.allclose(outputs[0, :3, :3]      ,    _snake_case)
   )

   Path(_snake_case).mkdir(exist_ok=_snake_case)
   print(f'''Saving model to {pytorch_dump_folder_path}''')
   model.save_pretrained(_snake_case)
   print(f'''Saving image processor to {pytorch_dump_folder_path}''')
   image_processor.save_pretrained(_snake_case)

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


if __name__ == "__main__":
    _SCREAMING_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=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.",
    )

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










from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.batch_decode(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.decode(*snake_case,      **snake_case)





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


