


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 	_a     ( SCREAMING_SNAKE_CASE		: List[Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			Optional[Any]      				=      384
 if "tiny" in model_name:
  UpperCamelCase__    :			List[str]      				=      [3, 3, 9, 3]
  UpperCamelCase__    :			Tuple      				=      [96, 192, 384, 768]
 if "small" in model_name:
  UpperCamelCase__    :			str      				=      [3, 3, 27, 3]
  UpperCamelCase__    :			Dict      				=      [96, 192, 384, 768]
 if "base" in model_name:
  UpperCamelCase__    :			int      				=      [3, 3, 27, 3]
  UpperCamelCase__    :			Union[str, Any]      				=      [128, 256, 512, 1024]
  UpperCamelCase__    :			Optional[Any]      				=      512
 if "large" in model_name:
  UpperCamelCase__    :			List[Any]      				=      [3, 3, 27, 3]
  UpperCamelCase__    :			Tuple      				=      [192, 384, 768, 1536]
  UpperCamelCase__    :			Union[str, Any]      				=      768
 if "xlarge" in model_name:
  UpperCamelCase__    :			Optional[int]      				=      [3, 3, 27, 3]
  UpperCamelCase__    :			List[Any]      				=      [256, 512, 1024, 2048]
  UpperCamelCase__    :			List[str]      				=      1024

 # set label information
 UpperCamelCase__    :			str      				=      150
 UpperCamelCase__    :			str      				=      """huggingface/label-files"""
 UpperCamelCase__    :			Tuple      				=      """ade20k-id2label.json"""
 UpperCamelCase__    :			List[Any]      				=      json.load(open(hf_hub_download(UpperCAmelCase__			,       UpperCAmelCase__			,       repo_type='''dataset'''   )			,       '''r'''   )   )
 UpperCamelCase__    :			str      				=      {int(UpperCAmelCase__   ): v for k, v in idalabel.items()}
 UpperCamelCase__    :			List[Any]      				=      {v: k for k, v in idalabel.items()}

 UpperCamelCase__    :			Union[str, Any]      				=      ConvNextConfig(
     depths=UpperCAmelCase__			,       hidden_sizes=UpperCAmelCase__			,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   )
 UpperCamelCase__    :			Optional[int]      				=      UperNetConfig(
     backbone_config=UpperCAmelCase__			,       auxiliary_in_channels=UpperCAmelCase__			,       num_labels=UpperCAmelCase__			,       idalabel=UpperCAmelCase__			,       labelaid=UpperCAmelCase__			,       )

 return config

def 	_a     ( SCREAMING_SNAKE_CASE		: Optional[Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			Optional[Any]      				=      []

 # 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 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: List[Any]			,       SCREAMING_SNAKE_CASE		: Union[str, Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			List[Any]      				=      dct.pop(UpperCAmelCase__   )
 UpperCamelCase__    :			List[Any]      				=      val

def 	_a     ( SCREAMING_SNAKE_CASE		: Optional[int]			,       SCREAMING_SNAKE_CASE		: Dict			,       SCREAMING_SNAKE_CASE		: List[Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			Dict      				=      {
     """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""",
 }
 UpperCamelCase__    :			Optional[Any]      				=      model_name_to_url[model_name]
 UpperCamelCase__    :			Any      				=      torch.hub.load_state_dict_from_url(UpperCAmelCase__			,       map_location='''cpu'''   )["""state_dict"""]

 UpperCamelCase__    :			str      				=      get_upernet_config(UpperCAmelCase__   )
 UpperCamelCase__    :			int      				=      UperNetForSemanticSegmentation(UpperCAmelCase__   )
 model.eval()

 # replace "bn" => "batch_norm"
 for key in state_dict.copy().keys():
  UpperCamelCase__    :			Dict      				=      state_dict.pop(UpperCAmelCase__   )
  if "bn" in key:
   UpperCamelCase__    :			Optional[int]      				=      key.replace('''bn'''			,       '''batch_norm'''   )
  UpperCamelCase__    :			List[str]      				=      val

 # rename keys
 UpperCamelCase__    :			Optional[int]      				=      create_rename_keys(UpperCAmelCase__   )
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__   )

 model.load_state_dict(UpperCAmelCase__   )

 # verify on image
 UpperCamelCase__    :			List[Any]      				=      """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
 UpperCamelCase__    :			int      				=      Image.open(requests.get(UpperCAmelCase__			,       stream=UpperCAmelCase__   ).raw   ).convert('''RGB'''   )

 UpperCamelCase__    :			Tuple      				=      SegformerImageProcessor()
 UpperCamelCase__    :			Tuple      				=      processor(UpperCAmelCase__			,       return_tensors='''pt'''   ).pixel_values

 with torch.no_grad():
  UpperCamelCase__    :			int      				=      model(UpperCAmelCase__   )

 if model_name == "upernet-convnext-tiny":
  UpperCamelCase__    :			Dict      				=      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":
  UpperCamelCase__    :			Optional[int]      				=      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":
  UpperCamelCase__    :			List[str]      				=      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":
  UpperCamelCase__    :			Optional[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":
  UpperCamelCase__    :			Tuple      				=      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]			,       UpperCAmelCase__			,       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(UpperCAmelCase__   )
  print(F"Saving processor to {pytorch_dump_folder_path}"   )
  processor.save_pretrained(UpperCAmelCase__   )

 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__":
       __UpperCamelCase				:				Optional[Any]  				= 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."
       )

       __UpperCamelCase				:				List[str]  				= parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      bool:
							return numa ^ numa < 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()





