





'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__lowerCamelCase						: Any       =   logging.get_logger(__name__)

__lowerCamelCase						: int       =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Optional[int]       =   {
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase						: List[str]       =   {
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


__lowerCamelCase						: int       =   {
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				PRETRAINED_INIT_CONFIGURATION
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				DistilBertTokenizer


  def __init__(				self    :					List[Any]							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					Optional[int]="[UNK]"							,	UpperCamelCase__    :					Tuple="[SEP]"							,	UpperCamelCase__    :					List[str]="[PAD]"							,	UpperCamelCase__    :					Optional[int]="[CLS]"							,	UpperCamelCase__    :					Tuple="[MASK]"							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					List[str]=None							,	**UpperCamelCase__    :					Optional[Any]							,	)     ->						Dict:
    super().__init__(
        UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	do_lower_case=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	tokenize_chinese_chars=UpperCamelCase__							,	strip_accents=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
    if (
        normalizer_state.get('''lowercase'''							,	UpperCamelCase__  ) != do_lower_case
        or normalizer_state.get('''strip_accents'''							,	UpperCamelCase__  ) != strip_accents
        or normalizer_state.get('''handle_chinese_chars'''							,	UpperCamelCase__  ) != tokenize_chinese_chars
    ):
      _UpperCamelCase       			=getattr(UpperCamelCase__							,	normalizer_state.pop('''type'''  )  )
      _UpperCamelCase       			=do_lower_case
      _UpperCamelCase       			=strip_accents
      _UpperCamelCase       			=tokenize_chinese_chars
      _UpperCamelCase       			=normalizer_class(**UpperCamelCase__  )

    _UpperCamelCase       			=do_lower_case


  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Dict=None  )     ->						Optional[Any]:
    _UpperCamelCase       			=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[self.cls_token_id]
    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    _UpperCamelCase       			=self._tokenizer.model.save(UpperCamelCase__							,	name=UpperCamelCase__  )
    return tuple(UpperCamelCase__  )













'''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						: Optional[Any]		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _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(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''   )   )  ,      '''r'''   )   )
    _UpperCamelCase       			={int(__SCREAMING_SNAKE_CASE   ): 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   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """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   ():


  """simple docstring"""

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


@torch.no_grad()
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
      __lowerCamelCase						: List[str]		=   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',
      )

      __lowerCamelCase						: Dict		=   parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )






