



'''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()
lowercase_		       =  logging.get_logger(__name__)




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  DPTConfig(embedding_type='''hybrid''')

     if "large" in checkpoint_url:
          _a      =  1_024
          _a      =  4_096
          _a      =  24
          _a      =  16
          _a      =  [5, 11, 17, 23]
          _a      =  [256, 512, 1_024, 1_024]
          _a      =  (1, 384, 384)

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

     if "ade" in checkpoint_url:
          _a      =  True
          _a      =  768
          _a      =  [1, 1, 1, 0.5]
          _a      =  150
          _a      =  16
          _a      =  '''huggingface/label-files'''
          _a      =  '''ade20k-id2label.json'''
          _a      =  json.load(open(cached_download(hf_hub_url(__A  ,	__A  ,	repo_type='''dataset'''))  ,	'''r'''))
          _a      =  {int(__A): v for k, v in idalabel.items()}
          _a      =  idalabel
          _a      =  {v: k for k, v in idalabel.items()}
          _a      =  [1, 150, 480, 480]

     return config, expected_shape




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__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
     ):
          _a      =  name.replace('''pretrained.model'''  ,	'''dpt.encoder''')
     if "pretrained.model" in name:
          _a      =  name.replace('''pretrained.model'''  ,	'''dpt.embeddings''')
     if "patch_embed" in name:
          _a      =  name.replace('''patch_embed'''  ,	'''''')
     if "pos_embed" in name:
          _a      =  name.replace('''pos_embed'''  ,	'''position_embeddings''')
     if "attn.proj" in name:
          _a      =  name.replace('''attn.proj'''  ,	'''attention.output.dense''')
     if "proj" in name and "project" not in name:
          _a      =  name.replace('''proj'''  ,	'''projection''')
     if "blocks" in name:
          _a      =  name.replace('''blocks'''  ,	'''layer''')
     if "mlp.fc1" in name:
          _a      =  name.replace('''mlp.fc1'''  ,	'''intermediate.dense''')
     if "mlp.fc2" in name:
          _a      =  name.replace('''mlp.fc2'''  ,	'''output.dense''')
     if "norm1" in name and "backbone" not in name:
          _a      =  name.replace('''norm1'''  ,	'''layernorm_before''')
     if "norm2" in name and "backbone" not in name:
          _a      =  name.replace('''norm2'''  ,	'''layernorm_after''')
     if "scratch.output_conv" in name:
          _a      =  name.replace('''scratch.output_conv'''  ,	'''head''')
     if "scratch" in name:
          _a      =  name.replace('''scratch'''  ,	'''neck''')
     if "layer1_rn" in name:
          _a      =  name.replace('''layer1_rn'''  ,	'''convs.0''')
     if "layer2_rn" in name:
          _a      =  name.replace('''layer2_rn'''  ,	'''convs.1''')
     if "layer3_rn" in name:
          _a      =  name.replace('''layer3_rn'''  ,	'''convs.2''')
     if "layer4_rn" in name:
          _a      =  name.replace('''layer4_rn'''  ,	'''convs.3''')
     if "refinenet" in name:
          _a      =  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
          _a      =  name.replace(F'''refinenet{layer_idx}'''  ,	F'''fusion_stage.layers.{abs(layer_idx-4)}''')
     if "out_conv" in name:
          _a      =  name.replace('''out_conv'''  ,	'''projection''')
     if "resConfUnit1" in name:
          _a      =  name.replace('''resConfUnit1'''  ,	'''residual_layer1''')
     if "resConfUnit2" in name:
          _a      =  name.replace('''resConfUnit2'''  ,	'''residual_layer2''')
     if "conv1" in name:
          _a      =  name.replace('''conv1'''  ,	'''convolution1''')
     if "conv2" in name:
          _a      =  name.replace('''conv2'''  ,	'''convolution2''')
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          _a      =  name.replace('''pretrained.act_postprocess1.0.project.0'''  ,	'''neck.reassemble_stage.readout_projects.0.0''')
     if "pretrained.act_postprocess2.0.project.0" in name:
          _a      =  name.replace('''pretrained.act_postprocess2.0.project.0'''  ,	'''neck.reassemble_stage.readout_projects.1.0''')
     if "pretrained.act_postprocess3.0.project.0" in name:
          _a      =  name.replace('''pretrained.act_postprocess3.0.project.0'''  ,	'''neck.reassemble_stage.readout_projects.2.0''')
     if "pretrained.act_postprocess4.0.project.0" in name:
          _a      =  name.replace('''pretrained.act_postprocess4.0.project.0'''  ,	'''neck.reassemble_stage.readout_projects.3.0''')

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          _a      =  name.replace('''pretrained.act_postprocess1.3'''  ,	'''neck.reassemble_stage.layers.0.projection''')
     if "pretrained.act_postprocess1.4" in name:
          _a      =  name.replace('''pretrained.act_postprocess1.4'''  ,	'''neck.reassemble_stage.layers.0.resize''')
     if "pretrained.act_postprocess2.3" in name:
          _a      =  name.replace('''pretrained.act_postprocess2.3'''  ,	'''neck.reassemble_stage.layers.1.projection''')
     if "pretrained.act_postprocess2.4" in name:
          _a      =  name.replace('''pretrained.act_postprocess2.4'''  ,	'''neck.reassemble_stage.layers.1.resize''')
     if "pretrained.act_postprocess3.3" in name:
          _a      =  name.replace('''pretrained.act_postprocess3.3'''  ,	'''neck.reassemble_stage.layers.2.projection''')
     if "pretrained.act_postprocess4.3" in name:
          _a      =  name.replace('''pretrained.act_postprocess4.3'''  ,	'''neck.reassemble_stage.layers.3.projection''')
     if "pretrained.act_postprocess4.4" in name:
          _a      =  name.replace('''pretrained.act_postprocess4.4'''  ,	'''neck.reassemble_stage.layers.3.resize''')
     if "pretrained" in name:
          _a      =  name.replace('''pretrained'''  ,	'''dpt''')
     if "bn" in name:
          _a      =  name.replace('''bn'''  ,	'''batch_norm''')
     if "head" in name:
          _a      =  name.replace('''head'''  ,	'''head.head''')
     if "encoder.norm" in name:
          _a      =  name.replace('''encoder.norm'''  ,	'''layernorm''')
     if "auxlayer" in name:
          _a      =  name.replace('''auxlayer'''  ,	'''auxiliary_head.head''')
     if "backbone" in name:
          _a      =  name.replace('''backbone'''  ,	'''backbone.bit.encoder''')

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

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




def lowerCAmelCase  (__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)
          _a      =  state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''')
          _a      =  state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''')
          # next, add query, keys and values (in that order) to the state dict
          _a      =  in_proj_weight[: config.hidden_size, :]
          _a      =  in_proj_bias[: config.hidden_size]
          _a      =  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a      =  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _a      =  in_proj_weight[
              -config.hidden_size :, :
          ]
          _a      =  in_proj_bias[-config.hidden_size :]




def lowerCAmelCase  ():



     """simple docstring"""



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




@torch.no_grad()
def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



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

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

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

     _a      =  prepare_img()
     _a      =  image_processor(__A  ,	return_tensors='''pt''')

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

     if show_prediction:
          _a      =  (
              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__":
  lowercase_		       =  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",
  )

  lowercase_		       =  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'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



