



"""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    : Any							=							logging.get_logger(__name__)

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]    )  ->       Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   DPTConfig()

     if "large" in checkpoint_url:
          _UpperCAmelCase      :     List[str]        =   1_024
          _UpperCAmelCase      :     Tuple        =   4_096
          _UpperCAmelCase      :     Tuple        =   24
          _UpperCAmelCase      :     Dict        =   16
          _UpperCAmelCase      :     Optional[int]        =   [5, 11, 17, 23]
          _UpperCAmelCase      :     Dict        =   [256, 512, 1_024, 1_024]
          _UpperCAmelCase      :     Any        =   (1, 384, 384)

     if "ade" in checkpoint_url:
          _UpperCAmelCase      :     int        =   True

          _UpperCAmelCase      :     int        =   150
          _UpperCAmelCase      :     List[Any]        =   "huggingface/label-files"
          _UpperCAmelCase      :     Dict        =   "ade20k-id2label.json"
          _UpperCAmelCase      :     List[str]        =   json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  repo_type="dataset"    )    )						,  "r"    )    )
          _UpperCAmelCase      :     List[Any]        =   {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
          _UpperCAmelCase      :     Any        =   idalabel
          _UpperCAmelCase      :     Any        =   {v: k for k, v in idalabel.items()}
          _UpperCAmelCase      :     Dict        =   [1, 150, 480, 480]

     return config, expected_shape

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   ["pretrained.model.head.weight", "pretrained.model.head.bias"]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       int:





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

     return name

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       Tuple:





     '''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      :     List[str]        =   state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'    )
          _UpperCAmelCase      :     Dict        =   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      :     Union[str, Any]        =   in_proj_weight[: config.hidden_size, :]
          _UpperCAmelCase      :     Optional[Any]        =   in_proj_bias[: config.hidden_size]
          _UpperCAmelCase      :     Any        =   in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _UpperCAmelCase      :     List[str]        =   in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _UpperCAmelCase      :     Union[str, Any]        =   in_proj_weight[
              -config.hidden_size :, :
          ]
          _UpperCAmelCase      :     Any        =   in_proj_bias[-config.hidden_size :]

def 						__snake_case						(				)  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase      :     List[Any]        =   Image.open(requests.get(SCREAMING_SNAKE_CASE__						,  stream=SCREAMING_SNAKE_CASE__    ).raw    )
     return im

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       int:





     '''simple docstring'''

     _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   get_dpt_config(SCREAMING_SNAKE_CASE__    )
     # load original state_dict from URL
     _UpperCAmelCase      :     List[str]        =   torch.hub.load_state_dict_from_url(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      :     List[str]        =   state_dict.pop(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   val
     # read in qkv matrices
     read_in_q_k_v(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     # load HuggingFace model
     _UpperCAmelCase      :     Optional[int]        =   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      :     Tuple        =   480 if "ade" in checkpoint_url else 384
     _UpperCAmelCase      :     Any        =   DPTImageProcessor(size=SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Optional[int]        =   prepare_img()
     _UpperCAmelCase      :     Optional[Any]        =   image_processor(SCREAMING_SNAKE_CASE__						,  return_tensors="pt"    )

     # forward pass
     _UpperCAmelCase      :     List[str]        =   model(**SCREAMING_SNAKE_CASE__    ).logits if "ade" in checkpoint_url else model(**SCREAMING_SNAKE_CASE__    ).predicted_depth

     # Assert logits
     _UpperCAmelCase      :     Union[str, Any]        =   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:
          _UpperCAmelCase      :     Optional[Any]        =   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(SCREAMING_SNAKE_CASE__    )
     assert (
         torch.allclose(outputs[0, 0, :3, :3]						,  SCREAMING_SNAKE_CASE__						,  atol=1E-4    )
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3]						,  SCREAMING_SNAKE_CASE__    )
     )

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


if __name__ == "__main__":
 _lowerCAmelCase    : List[Any]							=							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    : Tuple							=							parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowerCAmelCase    : List[str]							=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : int							=							{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase    : List[Any]							=							{
    "google/electra-small-generator": 5_12,
    "google/electra-base-generator": 5_12,
    "google/electra-large-generator": 5_12,
    "google/electra-small-discriminator": 5_12,
    "google/electra-base-discriminator": 5_12,
    "google/electra-large-discriminator": 5_12,
}

_lowerCAmelCase    : Optional[Any]							=							{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Any								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			ElectraTokenizer



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

           _UpperCAmelCase      :     List[str]        =   json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
           if (
               normalizer_state.get("lowercase"					,     A						) != do_lower_case
               or normalizer_state.get("strip_accents"					,     A						) != strip_accents
               or normalizer_state.get("handle_chinese_chars"					,     A						) != tokenize_chinese_chars
           ):
                _UpperCAmelCase      :     Union[str, Any]        =   getattr(A					,     normalizer_state.pop("type"						)						)
                _UpperCAmelCase      :     Dict        =   do_lower_case
                _UpperCAmelCase      :     Optional[int]        =   strip_accents
                _UpperCAmelCase      :     Any        =   tokenize_chinese_chars
                _UpperCAmelCase      :     Optional[Any]        =   normalizer_class(**A						)

           _UpperCAmelCase      :     int        =   do_lower_case



      def 						snake_case_ (					self					:     Tuple					,     A					:     str					,     A					:     int=None						):
           _UpperCAmelCase      :     Dict        =   [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 						snake_case_ (					self					:     Any					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Any        =   [self.sep_token_id]
           _UpperCAmelCase      :     Dict        =   [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 						snake_case_ (					self					:     Any					,     A					:     str					,     A					:     Optional[str] = None						):
           _UpperCAmelCase      :     List[Any]        =   self._tokenizer.model.save(A					,     name=A						)
           return tuple(A						)
