




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      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  int		       =     384
  if "tiny" in model_name:
    lowerCAmelCase__				:  Union[str, Any]		       =     [3, 3, 9, 3]
    lowerCAmelCase__				:  int		       =     [96, 192, 384, 768]
  if "small" in model_name:
    lowerCAmelCase__				:  Tuple		       =     [3, 3, 27, 3]
    lowerCAmelCase__				:  str		       =     [96, 192, 384, 768]
  if "base" in model_name:
    lowerCAmelCase__				:  str		       =     [3, 3, 27, 3]
    lowerCAmelCase__				:  int		       =     [128, 256, 512, 1024]
    lowerCAmelCase__				:  str		       =     512
  if "large" in model_name:
    lowerCAmelCase__				:  Union[str, Any]		       =     [3, 3, 27, 3]
    lowerCAmelCase__				:  Any		       =     [192, 384, 768, 1536]
    lowerCAmelCase__				:  Optional[int]		       =     768
  if "xlarge" in model_name:
    lowerCAmelCase__				:  int		       =     [3, 3, 27, 3]
    lowerCAmelCase__				:  Optional[int]		       =     [256, 512, 1024, 2048]
    lowerCAmelCase__				:  List[Any]		       =     1024

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

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

  return config



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  Dict		       =     []

  # 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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Union[str, Any]		       =     dct.pop(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     val



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Dict:
  lowerCAmelCase__				:  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''',
  }
  lowerCAmelCase__				:  Optional[Any]		       =     model_name_to_url[model_name]
  lowerCAmelCase__				:  Dict		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      map_location='''cpu'''      )['''state_dict''']

  lowerCAmelCase__				:  Union[str, Any]		       =     get_upernet_config(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     UperNetForSemanticSegmentation(UpperCamelCase      )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowerCAmelCase__				:  List[Any]		       =     state_dict.pop(UpperCamelCase      )
    if "bn" in key:
      lowerCAmelCase__				:  Tuple		       =     key.replace('''bn'''	,      '''batch_norm'''      )
    lowerCAmelCase__				:  List[str]		       =     val

  # rename keys
  lowerCAmelCase__				:  List[str]		       =     create_rename_keys(UpperCamelCase      )
  for src, dest in rename_keys:
    rename_key(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )

  model.load_state_dict(UpperCamelCase      )

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

  lowerCAmelCase__				:  Optional[int]		       =     SegformerImageProcessor()
  lowerCAmelCase__				:  Optional[int]		       =     processor(UpperCamelCase	,      return_tensors='''pt'''      ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__				:  Optional[Any]		       =     model(UpperCamelCase      )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__				:  List[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":
    lowerCAmelCase__				:  str		       =     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":
    lowerCAmelCase__				:  Optional[int]		       =     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":
    lowerCAmelCase__				:  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":
    lowerCAmelCase__				:  List[Any]		       =     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__":
     lowerCAmelCase_					    = 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_					    = parser.parse_args()
     convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












lowerCAmelCase_					    = 6_55_21



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  List[Any]		       =     0
  for plain_chr in plain_text:
    lowerCAmelCase__				:  Union[str, Any]		       =     (a + ord(UpperCamelCase      )) % MOD_ADLER
    lowerCAmelCase__				:  Optional[Any]		       =     (b + a) % MOD_ADLER
  return (b << 16) | a

