






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()
snake_case__      :	Optional[int]       	=      logging.get_logger(__name__)






def     _snake_case    (__lowercase):
   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(_lowerCamelCase    ,      _lowerCamelCase    ,      repo_type='dataset'))    ,      'r'))
      UpperCamelCase_									=     {int(_lowerCamelCase): 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     _snake_case    (__lowercase):
   UpperCamelCase_									=     ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase    ,      _lowerCamelCase)






def     _snake_case    (__lowercase):
   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     _snake_case    (__lowercase    ,      __lowercase):
   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     _snake_case    ():
   UpperCamelCase_									=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
   UpperCamelCase_									=     Image.open(requests.get(_lowerCamelCase    ,      stream=_lowerCamelCase).raw)
   return im






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dpt_config(_lowerCamelCase)
   # load original state_dict from URL
   UpperCamelCase_									=     torch.hub.load_state_dict_from_url(_lowerCamelCase    ,      map_location='cpu')
   # remove certain keys
   remove_ignore_keys_(_lowerCamelCase)
   # rename keys
   for key in state_dict.copy().keys():
      UpperCamelCase_									=     state_dict.pop(_lowerCamelCase)
      UpperCamelCase_									=     val
   # read in qkv matrices
   read_in_q_k_v(_lowerCamelCase    ,      _lowerCamelCase)

   # load HuggingFace model
   UpperCamelCase_									=     DPTForSemanticSegmentation(_lowerCamelCase) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase)
   model.load_state_dict(_lowerCamelCase)
   model.eval()

   # Check outputs on an image
   UpperCamelCase_									=     480 if """ade""" in checkpoint_url else 384
   UpperCamelCase_									=     DPTImageProcessor(size=_lowerCamelCase)

   UpperCamelCase_									=     prepare_img()
   UpperCamelCase_									=     image_processor(_lowerCamelCase    ,      return_tensors='pt')

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

   # Assert logits
   UpperCamelCase_									=     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_									=     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(_lowerCamelCase)
   assert (
       torch.allclose(outputs[0, 0, :3, :3]    ,      _lowerCamelCase    ,      atol=1e-4)
       if "ade" in checkpoint_url
       else torch.allclose(outputs[0, :3, :3]    ,      _lowerCamelCase)
   )

   Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
   print(f"""Saving model to {pytorch_dump_folder_path}""")
   model.save_pretrained(_lowerCamelCase)
   print(f"""Saving image processor to {pytorch_dump_folder_path}""")
   image_processor.save_pretrained(_lowerCamelCase)

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


if __name__ == "__main__":
 snake_case__      :	int       	=      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.""",
 )

 snake_case__      :	Tuple       	=      parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)














import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['vocab_file'] )
     with open(self.vocab_file		,		'w'		,		encoding='utf-8' ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

     UpperCamelCase_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





