






"""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()
_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	DPTConfig()

      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 "ade" in checkpoint_url:
            UpperCAmelCase          =	True

            UpperCAmelCase          =	150
            UpperCAmelCase          =	"""huggingface/label-files"""
            UpperCAmelCase          =	"""ade20k-id2label.json"""
            UpperCAmelCase          =	json.load(open(cached_download(hf_hub_url(_snake_case				,	_snake_case				,	repo_type="""dataset"""							)							)				,	"""r"""							)							)
            UpperCAmelCase          =	{int(_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			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_snake_case				,	_snake_case							)

def _a			(		_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"""				,	"""patch_embeddings"""							)
      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:
            UpperCAmelCase          =	name.replace("""norm1"""				,	"""layernorm_before"""							)
      if "norm2" 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"""							)

      return name

def _a			(		_snake_case				,	_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(_snake_case				,	stream=_snake_case							).raw							)
      return im

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







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	get_dpt_config(_snake_case							)
      # load original state_dict from URL
      UpperCAmelCase          =	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():
            UpperCAmelCase          =	state_dict.pop(_snake_case							)
            UpperCAmelCase          =	val
      # read in qkv matrices
      read_in_q_k_v(_snake_case				,	_snake_case							)

      # load HuggingFace model
      UpperCAmelCase          =	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
      UpperCAmelCase          =	480 if """ade""" in checkpoint_url else 384
      UpperCAmelCase          =	DPTImageProcessor(size=_snake_case							)

      UpperCAmelCase          =	prepare_img()
      UpperCAmelCase          =	image_processor(_snake_case				,	return_tensors="""pt"""							)

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

      # Assert logits
      UpperCAmelCase          =	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:
            UpperCAmelCase          =	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__":
  _UpperCamelCase					      =  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.""",
  )

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











"""simple docstring"""







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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _UpperCamelCase					      =  None


_UpperCamelCase					      =  logging.get_logger(__name__)


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_UpperCamelCase					      =  {
    """google/pegasus-xsum""": 512,
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PegasusTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A="<pad>"     ,A="</s>"     ,A="<unk>"     ,A="<mask_2>"     ,A="<mask_1>"     ,A=None     ,A=103     ,**A     ,):
          UpperCAmelCase          =	offset

          if additional_special_tokens is not None:
                if not isinstance(A     ,A ):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A )}, but is'''
                          F''' {type(A )}''' )

                UpperCAmelCase          =	(
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F'''<unk_{i}>''' for i in range(len(A )     ,self.offset - 1 )
                ]

                if len(set(A ) ) != len(A ):
                      raise ValueError(
                          """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
                UpperCAmelCase          =	additional_special_tokens_extended
          else:
                UpperCAmelCase          =	[mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F'''<unk_{i}>''' for i in range(2     ,self.offset )]

          super().__init__(
              A     ,tokenizer_file=A     ,pad_token=A     ,eos_token=A     ,unk_token=A     ,mask_token=A     ,mask_token_sent=A     ,offset=A     ,additional_special_tokens=A     ,**A     ,)
          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	False if not self.vocab_file else True






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
                raise ValueError(
                    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                    F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )

          return [1 if x in all_special_ids else 0 for x in seq]






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
          if already_has_special_tokens:
                return self._special_token_mask(A )
          elif token_ids_a is None:
                return self._special_token_mask(A ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

          return (out_vocab_file,)



