


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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				384
  lowerCamelCase :					List[str]    =				7
  if "tiny" in model_name:
    lowerCamelCase :					Dict    =				96
    lowerCamelCase :					Tuple    =				(2, 2, 6, 2)
    lowerCamelCase :					Optional[Any]    =				(3, 6, 12, 24)
  elif "small" in model_name:
    lowerCamelCase :					List[Any]    =				96
    lowerCamelCase :					Any    =				(2, 2, 18, 2)
    lowerCamelCase :					Optional[Any]    =				(3, 6, 12, 24)
  elif "base" in model_name:
    lowerCamelCase :					Optional[int]    =				128
    lowerCamelCase :					Union[str, Any]    =				(2, 2, 18, 2)
    lowerCamelCase :					Union[str, Any]    =				(4, 8, 16, 32)
    lowerCamelCase :					Tuple    =				12
    lowerCamelCase :					Any    =				512
  elif "large" in model_name:
    lowerCamelCase :					int    =				192
    lowerCamelCase :					List[str]    =				(2, 2, 18, 2)
    lowerCamelCase :					int    =				(6, 12, 24, 48)
    lowerCamelCase :					int    =				12
    lowerCamelCase :					Optional[Any]    =				768

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

  lowerCamelCase :					Union[str, Any]    =				SwinConfig(
      embed_dim=SCREAMING_SNAKE_CASE_      ,						depths=SCREAMING_SNAKE_CASE_      ,						num_heads=SCREAMING_SNAKE_CASE_      ,						window_size=SCREAMING_SNAKE_CASE_      ,						out_features=["stage1", "stage2", "stage3", "stage4"]      ,						)
  lowerCamelCase :					Dict    =				UperNetConfig(
      backbone_config=SCREAMING_SNAKE_CASE_      ,						auxiliary_in_channels=SCREAMING_SNAKE_CASE_      ,						num_labels=SCREAMING_SNAKE_CASE_      ,						idalabel=SCREAMING_SNAKE_CASE_      ,						labelaid=SCREAMING_SNAKE_CASE_      ,						)

  return config






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				[]

  # 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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				val






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
  for i in range(len(backbone_config.depths	)	):
    lowerCamelCase :					int    =				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)
      lowerCamelCase :					Optional[Any]    =				state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""	)
      lowerCamelCase :					str    =				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
      lowerCamelCase :					List[str]    =				in_proj_weight[:dim, :]
      lowerCamelCase :					int    =				in_proj_bias[: dim]
      lowerCamelCase :					Union[str, Any]    =				in_proj_weight[
          dim : dim * 2, :
      ]
      lowerCamelCase :					List[Any]    =				in_proj_bias[
          dim : dim * 2
      ]
      lowerCamelCase :					int    =				in_proj_weight[
          -dim :, :
      ]
      lowerCamelCase :					str    =				in_proj_bias[-dim :]
      # fmt: on






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase    ,	lowerCamelCase :					int    =				x.shape
  lowerCamelCase :					Optional[int]    =				x.reshape(SCREAMING_SNAKE_CASE_      ,						4      ,						in_channel // 4	)
  lowerCamelCase :					Tuple    =				x[:, [0, 2, 1, 3], :].transpose(1      ,						2	).reshape(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  return x






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase    ,	lowerCamelCase :					int    =				x.shape
  lowerCamelCase :					int    =				x.reshape(SCREAMING_SNAKE_CASE_      ,						in_channel // 4      ,						4	)
  lowerCamelCase :					str    =				x[:, :, [0, 2, 1, 3]].transpose(1      ,						2	).reshape(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)

  return x






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				x.shape[0]
  lowerCamelCase :					Union[str, Any]    =				x.reshape(4      ,						in_channel // 4	)
  lowerCamelCase :					Optional[Any]    =				x[[0, 2, 1, 3], :].transpose(0      ,						1	).reshape(SCREAMING_SNAKE_CASE_	)
  return x






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				x.shape[0]
  lowerCamelCase :					Union[str, Any]    =				x.reshape(in_channel // 4      ,						4	)
  lowerCamelCase :					List[Any]    =				x[:, [0, 2, 1, 3]].transpose(0      ,						1	).reshape(SCREAMING_SNAKE_CASE_	)
  return x






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				{
      "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",
  }
  lowerCamelCase :					List[str]    =				model_name_to_url[model_name]
  lowerCamelCase :					Any    =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"      ,						file_name=SCREAMING_SNAKE_CASE_	)[
      "state_dict"
  ]

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

  lowerCamelCase :					List[Any]    =				get_upernet_config(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
  model.eval()

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

  # rename keys
  lowerCamelCase :					Optional[int]    =				create_rename_keys(SCREAMING_SNAKE_CASE_	)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,						config.backbone_config	)

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        lowerCamelCase :					Optional[Any]    =				reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE_	)
      if "norm" in key:
        lowerCamelCase :					Any    =				reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE_	)

  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					Optional[int]    =				SegformerImageProcessor()
  lowerCamelCase :					Optional[Any]    =				processor(SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	).pixel_values

  with torch.no_grad():
    lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					str    =				outputs.logits

  print(logits.shape	)
  print("First values of logits:"      ,						logits[0, 0, :3, :3]	)
  # assert values
  if model_name == "upernet-swin-tiny":
    lowerCamelCase :					str    =				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":
    lowerCamelCase :					Union[str, Any]    =				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":
    lowerCamelCase :					int    =				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":
    lowerCamelCase :					int    =				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]      ,						SCREAMING_SNAKE_CASE_      ,						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(SCREAMING_SNAKE_CASE_	)
    print(f"""Saving processor to {pytorch_dump_folder_path}"""	)
    processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

  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__":
   _snake_case             =     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.'''
   )

   _snake_case             =     parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


if __name__ == "__main__":
   print(f'''{solution() = }''')



