

'''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  (					A__   ):






 """simple docstring"""



 __lowercase													=				DPTConfig(embedding_type='''hybrid'''   )

 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 "nyu" or "midas" in checkpoint_url:
  __lowercase													=				768
  __lowercase													=				[1, 1, 1, 0.5]
  __lowercase													=				[256, 512, 768, 768]
  __lowercase													=				150
  __lowercase													=				16
  __lowercase													=				(1, 384, 384)
  __lowercase													=				False
  __lowercase													=				'''project'''

 if "ade" in checkpoint_url:
  __lowercase													=				True
  __lowercase													=				768
  __lowercase													=				[1, 1, 1, 0.5]
  __lowercase													=				150
  __lowercase													=				16
  __lowercase													=				'''huggingface/label-files'''
  __lowercase													=				'''ade20k-id2label.json'''
  __lowercase													=				json.load(open(cached_download(hf_hub_url(A__  ,       A__  ,       repo_type='''dataset'''   )   )  ,       '''r'''   )   )
  __lowercase													=				{int(A__   ): 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 	_A  (					A__   ):






 """simple docstring"""



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


def 	_A  (					A__   ):






 """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
 ):
  __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'''  ,       ''''''   )
 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 and "backbone" not in name:
  __lowercase													=				name.replace('''norm1'''  ,       '''layernorm_before'''   )
 if "norm2" in name and "backbone" not 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'''   )
 if "backbone" in name:
  __lowercase													=				name.replace('''backbone'''  ,       '''backbone.bit.encoder'''   )

 if ".." in name:
  __lowercase													=				name.replace('''..'''  ,       '''.'''   )

 if "stem.conv" in name:
  __lowercase													=				name.replace('''stem.conv'''  ,       '''bit.embedder.convolution'''   )
 if "blocks" in name:
  __lowercase													=				name.replace('''blocks'''  ,       '''layers'''   )
 if "convolution" in name and "backbone" in name:
  __lowercase													=				name.replace('''convolution'''  ,       '''conv'''   )
 if "layer" in name and "backbone" in name:
  __lowercase													=				name.replace('''layer'''  ,       '''layers'''   )
 if "backbone.bit.encoder.bit" in name:
  __lowercase													=				name.replace('''backbone.bit.encoder.bit'''  ,       '''backbone.bit'''   )
 if "embedder.conv" in name:
  __lowercase													=				name.replace('''embedder.conv'''  ,       '''embedder.convolution'''   )
 if "backbone.bit.encoder.stem.norm" in name:
  __lowercase													=				name.replace('''backbone.bit.encoder.stem.norm'''  ,       '''backbone.bit.embedder.norm'''   )
 return name


def 	_A  (					A__  ,       A__   ):






 """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)
  __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 	_A  (					):






 """simple docstring"""



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


@torch.no_grad()
def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

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

 __lowercase													=				prepare_img()
 __lowercase													=				image_processor(A__  ,       return_tensors='''pt'''   )

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

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

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

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

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




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{'''vocab_file''': '''sentencepiece.model'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

lowerCAmelCase__      		=			{
    '''google/rembert''': 256,
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(   self     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]=False      ,lowercase__     :  Dict=True      ,lowercase__     :  List[str]=True      ,lowercase__     :  Dict="[CLS]"      ,lowercase__     :  Union[str, Any]="[SEP]"      ,lowercase__     :  List[str]="[UNK]"      ,lowercase__     :  int="[SEP]"      ,lowercase__     :  List[str]="[PAD]"      ,lowercase__     :  Optional[int]="[CLS]"      ,lowercase__     :  List[Any]="[MASK]"      ,**lowercase__     :  int      ,):
   super().__init__(
       do_lower_case=lowercase__      ,remove_space=lowercase__      ,keep_accents=lowercase__      ,bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,pad_token=lowercase__      ,cls_token=lowercase__      ,mask_token=lowercase__      ,**lowercase__      ,)

   __lowercase													=				do_lower_case
   __lowercase													=				remove_space
   __lowercase													=				keep_accents
   __lowercase													=				vocab_file

   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(lowercase__    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return len(self.sp_model    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				{self.convert_ids_to_tokens(lowercase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab






  def __getstate__(   self     :  List[Any]    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   return state






  def __setstate__(   self     :  str      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				d
   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(self.vocab_file    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]=False    ):
   __lowercase													=				self.sp_model.EncodeAsPieces(lowercase__    )
   return pieces






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]    ):
   return self.sp_model.PieceToId(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  str    ):
   return self.sp_model.IdToPiece(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				self.sp_model.decode_pieces(lowercase__    )
   return out_string






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         '''You should not supply a second sequence if the provided sequence of '''
         '''ids is already formatted with special tokens for the model.'''    )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is not None:
    return [1] + ([0] * len(lowercase__    )) + [1] + ([0] * len(lowercase__    )) + [1]
   return [1] + ([0] * len(lowercase__    )) + [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not os.path.isdir(lowercase__    ):
    logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase__    )    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

   if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase__    ):
    copyfile(self.vocab_file      ,lowercase__    )

   return (out_vocab_file,)

