



"""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     _a (	UpperCAmelCase__     )						->    List[Any]:
      __SCREAMING_SNAKE_CASE       		=   DPTConfig()

      if "large" in checkpoint_url:
            __SCREAMING_SNAKE_CASE       		=   10_24
            __SCREAMING_SNAKE_CASE       		=   40_96
            __SCREAMING_SNAKE_CASE       		=   24
            __SCREAMING_SNAKE_CASE       		=   16
            __SCREAMING_SNAKE_CASE       		=   [5, 11, 17, 23]
            __SCREAMING_SNAKE_CASE       		=   [2_56, 5_12, 10_24, 10_24]
            __SCREAMING_SNAKE_CASE       		=   (1, 3_84, 3_84)

      if "ade" in checkpoint_url:
            __SCREAMING_SNAKE_CASE       		=   True

            __SCREAMING_SNAKE_CASE       		=   1_50
            __SCREAMING_SNAKE_CASE       		=   '''huggingface/label-files'''
            __SCREAMING_SNAKE_CASE       		=   '''ade20k-id2label.json'''
            __SCREAMING_SNAKE_CASE       		=   json.load(open(cached_download(hf_hub_url(_snake_case     ,	_snake_case     ,	repo_type='''dataset'''     )     )     ,	'''r'''     )     )
            __SCREAMING_SNAKE_CASE       		=   {int(_snake_case     ): v for k, v in idalabel.items()}
            __SCREAMING_SNAKE_CASE       		=   idalabel
            __SCREAMING_SNAKE_CASE       		=   {v: k for k, v in idalabel.items()}
            __SCREAMING_SNAKE_CASE       		=   [1, 1_50, 4_80, 4_80]

      return config, expected_shape






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






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

      return name






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    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)
            __SCREAMING_SNAKE_CASE       		=   state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""     )
            __SCREAMING_SNAKE_CASE       		=   state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""     )
            # next, add query, keys and values (in that order) to the state dict
            __SCREAMING_SNAKE_CASE       		=   in_proj_weight[: config.hidden_size, :]
            __SCREAMING_SNAKE_CASE       		=   in_proj_bias[: config.hidden_size]
            __SCREAMING_SNAKE_CASE       		=   in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __SCREAMING_SNAKE_CASE       		=   in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            __SCREAMING_SNAKE_CASE       		=   in_proj_weight[
                -config.hidden_size :, :
            ]
            __SCREAMING_SNAKE_CASE       		=   in_proj_bias[-config.hidden_size :]






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






@torch.no_grad()
def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    str:
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   get_dpt_config(_snake_case     )
      # load original state_dict from URL
      __SCREAMING_SNAKE_CASE       		=   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():
            __SCREAMING_SNAKE_CASE       		=   state_dict.pop(_snake_case     )
            __SCREAMING_SNAKE_CASE       		=   val
      # read in qkv matrices
      read_in_q_k_v(_snake_case     ,	_snake_case     )

      # load HuggingFace model
      __SCREAMING_SNAKE_CASE       		=   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
      __SCREAMING_SNAKE_CASE       		=   4_80 if '''ade''' in checkpoint_url else 3_84
      __SCREAMING_SNAKE_CASE       		=   DPTImageProcessor(size=_snake_case     )

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

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

      # Assert logits
      __SCREAMING_SNAKE_CASE       		=   torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]     )
      if "ade" in checkpoint_url:
            __SCREAMING_SNAKE_CASE       		=   torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]     )
      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__":
       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)








import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






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


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


