





import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				3_84
  _snake_case     		=				7
  if "tiny" in model_name:
    _snake_case     		=				96
    _snake_case     		=				(2, 2, 6, 2)
    _snake_case     		=				(3, 6, 12, 24)
  elif "small" in model_name:
    _snake_case     		=				96
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(3, 6, 12, 24)
  elif "base" in model_name:
    _snake_case     		=				1_28
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(4, 8, 16, 32)
    _snake_case     		=				12
    _snake_case     		=				5_12
  elif "large" in model_name:
    _snake_case     		=				1_92
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(6, 12, 24, 48)
    _snake_case     		=				12
    _snake_case     		=				7_68

  # set label information
  _snake_case     		=				1_50
  _snake_case     		=				"huggingface/label-files"
  _snake_case     		=				"ade20k-id2label.json"
  _snake_case     		=				json.load(open(hf_hub_download(lowerCAmelCase__					,	lowerCAmelCase__					,	repo_type="dataset"       )					,	"r"       )       )
  _snake_case     		=				{int(lowerCAmelCase__       ): v for k, v in idalabel.items()}
  _snake_case     		=				{v: k for k, v in idalabel.items()}

  _snake_case     		=				SwinConfig(
      embed_dim=lowerCAmelCase__					,	depths=lowerCAmelCase__					,	num_heads=lowerCAmelCase__					,	window_size=lowerCAmelCase__					,	out_features=["stage1", "stage2", "stage3", "stage4"]					,	)
  _snake_case     		=				UperNetConfig(
      backbone_config=lowerCAmelCase__					,	auxiliary_in_channels=lowerCAmelCase__					,	num_labels=lowerCAmelCase__					,	idalabel=lowerCAmelCase__					,	labelaid=lowerCAmelCase__					,	)

  return config




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]

  # fmt: off
  # stem
  rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")       )
  rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")       )
  rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")       )
  rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.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}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')       )

    if i < 3:
      rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')       )
      rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				dct.pop(lowerCAmelCase__       )
  _snake_case     		=				val




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
  for i in range(len(backbone_config.depths       )       ):
    _snake_case     		=				num_features[i]
    for j in range(backbone_config.depths[i]       ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      _snake_case     		=				state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''       )
      _snake_case     		=				state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''       )
      # next, add query, keys and values (in that order) to the state dict
      _snake_case     		=				in_proj_weight[:dim, :]
      _snake_case     		=				in_proj_bias[: dim]
      _snake_case     		=				in_proj_weight[
          dim : dim * 2, :
      ]
      _snake_case     		=				in_proj_bias[
          dim : dim * 2
      ]
      _snake_case     		=				in_proj_weight[
          -dim :, :
      ]
      _snake_case     		=				in_proj_bias[-dim :]
      # fmt: on




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				x.shape
  _snake_case     		=				x.reshape(lowerCAmelCase__					,	4					,	in_channel // 4       )
  _snake_case     		=				x[:, [0, 2, 1, 3], :].transpose(1					,	2       ).reshape(lowerCAmelCase__					,	lowerCAmelCase__       )
  return x




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				x.shape
  _snake_case     		=				x.reshape(lowerCAmelCase__					,	in_channel // 4					,	4       )
  _snake_case     		=				x[:, :, [0, 2, 1, 3]].transpose(1					,	2       ).reshape(lowerCAmelCase__					,	lowerCAmelCase__       )

  return x




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				x.shape[0]
  _snake_case     		=				x.reshape(4					,	in_channel // 4       )
  _snake_case     		=				x[[0, 2, 1, 3], :].transpose(0					,	1       ).reshape(lowerCAmelCase__       )
  return x




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				x.shape[0]
  _snake_case     		=				x.reshape(in_channel // 4					,	4       )
  _snake_case     		=				x[:, [0, 2, 1, 3]].transpose(0					,	1       ).reshape(lowerCAmelCase__       )
  return x




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				{
      "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
      "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
      "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
      "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
  }
  _snake_case     		=				model_name_to_url[model_name]
  _snake_case     		=				torch.hub.load_state_dict_from_url(lowerCAmelCase__					,	map_location="cpu"					,	file_name=lowerCAmelCase__       )[
      "state_dict"
  ]

  for name, param in state_dict.items():
    print(lowerCAmelCase__					,	param.shape       )

  _snake_case     		=				get_upernet_config(lowerCAmelCase__       )
  _snake_case     		=				UperNetForSemanticSegmentation(lowerCAmelCase__       )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    _snake_case     		=				state_dict.pop(lowerCAmelCase__       )
    if "bn" in key:
      _snake_case     		=				key.replace("bn"					,	"batch_norm"       )
    _snake_case     		=				val

  # rename keys
  _snake_case     		=				create_rename_keys(lowerCAmelCase__       )
  for src, dest in rename_keys:
    rename_key(lowerCAmelCase__					,	lowerCAmelCase__					,	lowerCAmelCase__       )
  read_in_q_k_v(lowerCAmelCase__					,	config.backbone_config       )

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        _snake_case     		=				reverse_correct_unfold_reduction_order(lowerCAmelCase__       )
      if "norm" in key:
        _snake_case     		=				reverse_correct_unfold_norm_order(lowerCAmelCase__       )

  model.load_state_dict(lowerCAmelCase__       )

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

  _snake_case     		=				SegformerImageProcessor()
  _snake_case     		=				processor(lowerCAmelCase__					,	return_tensors="pt"       ).pixel_values

  with torch.no_grad():
    _snake_case     		=				model(lowerCAmelCase__       )
    _snake_case     		=				outputs.logits

  print(logits.shape       )
  print("First values of logits:"					,	logits[0, 0, :3, :3]       )
  # assert values
  if model_name == "upernet-swin-tiny":
    _snake_case     		=				torch.tensor(
        [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]       )
  elif model_name == "upernet-swin-small":
    _snake_case     		=				torch.tensor(
        [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]       )
  elif model_name == "upernet-swin-base":
    _snake_case     		=				torch.tensor(
        [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]       )
  elif model_name == "upernet-swin-large":
    _snake_case     		=				torch.tensor(
        [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]       )
  print("Logits:"					,	outputs.logits[0, 0, :3, :3]       )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]					,	lowerCAmelCase__					,	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(lowerCAmelCase__       )
    print(f'''Saving processor to {pytorch_dump_folder_path}'''       )
    processor.save_pretrained(lowerCAmelCase__       )

  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__":
 __magic_name__   :						List[Any]    =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""upernet-swin-tiny""",
     type=str,
     choices=[F'upernet-swin-{size}' for size in ["""tiny""", """small""", """base""", """large"""]],
     help="""Name of the Swin + 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."""
 )

 __magic_name__   :						Dict    =			parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


