import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation

def 		UpperCamelCase	(      lowercase_   )		->	Any:


  '''simple docstring'''






  lowercase__  :   Dict    		=      3_84
  if "tiny" in model_name:
    lowercase__  :   Optional[int]    		=      [3, 3, 9, 3]
    lowercase__  :   int    		=      [96, 1_92, 3_84, 7_68]
  if "small" in model_name:
    lowercase__  :   Any    		=      [3, 3, 27, 3]
    lowercase__  :   Any    		=      [96, 1_92, 3_84, 7_68]
  if "base" in model_name:
    lowercase__  :   List[str]    		=      [3, 3, 27, 3]
    lowercase__  :   Optional[Any]    		=      [1_28, 2_56, 5_12, 10_24]
    lowercase__  :   Any    		=      5_12
  if "large" in model_name:
    lowercase__  :   int    		=      [3, 3, 27, 3]
    lowercase__  :   Any    		=      [1_92, 3_84, 7_68, 15_36]
    lowercase__  :   int    		=      7_68
  if "xlarge" in model_name:
    lowercase__  :   int    		=      [3, 3, 27, 3]
    lowercase__  :   Union[str, Any]    		=      [2_56, 5_12, 10_24, 20_48]
    lowercase__  :   Any    		=      10_24

  # set label information
  lowercase__  :   Optional[Any]    		=      1_50
  lowercase__  :   str    		=      '''huggingface/label-files'''
  lowercase__  :   List[str]    		=      '''ade20k-id2label.json'''
  lowercase__  :   Any    		=      json.load(open(hf_hub_download(a_ , a_ , repo_type="""dataset"""   ) , """r"""   )   )
  lowercase__  :   Optional[Any]    		=      {int(a_   ): v for k, v in idalabel.items()}
  lowercase__  :   Union[str, Any]    		=      {v: k for k, v in idalabel.items()}

  lowercase__  :   List[Any]    		=      ConvNextConfig(
      depths=a_ , hidden_sizes=a_ , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
  lowercase__  :   List[Any]    		=      UperNetConfig(
      backbone_config=a_ , auxiliary_in_channels=a_ , num_labels=a_ , idalabel=a_ , labelaid=a_ , )

  return config

def 		UpperCamelCase	(      lowercase_   )		->	List[Any]:


  '''simple docstring'''






  lowercase__  :   str    		=      []

  # fmt: off
  # stem
  rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")   )
  rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")   )
  rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")   )
  rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")   )
  # stages
  for i in range(len(config.backbone_config.depths   )   ):
    for j in range(config.backbone_config.depths[i]   ):
      rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')   )
      rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')   )
    if i > 0:
      rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')   )
      rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')   )
      rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')   )
      rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')   )

    rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')   )
    rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')   )

  # decode head
  rename_keys.extend(
      [
          ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
          ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
          ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
          ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
      ]   )
  # fmt: on

  return rename_keys

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Optional[int]:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      dct.pop(a_   )
  lowercase__  :   List[str]    		=      val

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      {
      '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
      '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
      '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
      '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
      '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
  }
  lowercase__  :   List[Any]    		=      model_name_to_url[model_name]
  lowercase__  :   Any    		=      torch.hub.load_state_dict_from_url(a_ , map_location="""cpu"""   )['''state_dict''']

  lowercase__  :   Tuple    		=      get_upernet_config(a_   )
  lowercase__  :   Optional[int]    		=      UperNetForSemanticSegmentation(a_   )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowercase__  :   List[Any]    		=      state_dict.pop(a_   )
    if "bn" in key:
      lowercase__  :   List[Any]    		=      key.replace("""bn""" , """batch_norm"""   )
    lowercase__  :   Optional[int]    		=      val

  # rename keys
  lowercase__  :   str    		=      create_rename_keys(a_   )
  for src, dest in rename_keys:
    rename_key(a_ , a_ , a_   )

  model.load_state_dict(a_   )

  # verify on image
  lowercase__  :   Tuple    		=      '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
  lowercase__  :   Tuple    		=      Image.open(requests.get(a_ , stream=a_   ).raw   ).convert("""RGB"""   )

  lowercase__  :   List[str]    		=      SegformerImageProcessor()
  lowercase__  :   Any    		=      processor(a_ , return_tensors="""pt"""   ).pixel_values

  with torch.no_grad():
    lowercase__  :   Any    		=      model(a_   )

  if model_name == "upernet-convnext-tiny":
    lowercase__  :   Optional[Any]    		=      torch.tensor(
        [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]   )
  elif model_name == "upernet-convnext-small":
    lowercase__  :   Any    		=      torch.tensor(
        [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]   )
  elif model_name == "upernet-convnext-base":
    lowercase__  :   Any    		=      torch.tensor(
        [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]   )
  elif model_name == "upernet-convnext-large":
    lowercase__  :   List[Any]    		=      torch.tensor(
        [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]   )
  elif model_name == "upernet-convnext-xlarge":
    lowercase__  :   Dict    		=      torch.tensor(
        [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]   )
  print("""Logits:""" , outputs.logits[0, 0, :3, :3]   )
  assert torch.allclose(outputs.logits[0, 0, :3, :3] , a_ , atol=1E-4   )
  print("""Looks ok!"""   )

  if pytorch_dump_folder_path is not None:
    print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
    model.save_pretrained(a_   )
    print(F'Saving processor to {pytorch_dump_folder_path}'   )
    processor.save_pretrained(a_   )

  if push_to_hub:
    print(F'Pushing model and processor for {model_name} to hub'   )
    model.push_to_hub(F'openmmlab/{model_name}'   )
    processor.push_to_hub(F'openmmlab/{model_name}'   )


if __name__ == "__main__":
     lowerCamelCase__    :		List[str]          =   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""upernet-convnext-tiny""",
         type=str,
         choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
         help="""Name of the ConvNext UperNet model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )
     parser.add_argument(
         """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
     )

     lowerCamelCase__    :		Tuple          =   parser.parse_args()
     convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






