





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any     )      ->						Dict:
  _lowercase							= 384
  _lowercase							= 7
  if "tiny" in model_name:
    _lowercase							= 96
    _lowercase							= (2, 2, 6, 2)
    _lowercase							= (3, 6, 12, 24)
  elif "small" in model_name:
    _lowercase							= 96
    _lowercase							= (2, 2, 18, 2)
    _lowercase							= (3, 6, 12, 24)
  elif "base" in model_name:
    _lowercase							= 128
    _lowercase							= (2, 2, 18, 2)
    _lowercase							= (4, 8, 16, 32)
    _lowercase							= 12
    _lowercase							= 512
  elif "large" in model_name:
    _lowercase							= 192
    _lowercase							= (2, 2, 18, 2)
    _lowercase							= (6, 12, 24, 48)
    _lowercase							= 12
    _lowercase							= 768

  # set label information
  _lowercase							= 150
  _lowercase							= 'huggingface/label-files'
  _lowercase							= 'ade20k-id2label.json'
  _lowercase							= json.load(open(hf_hub_download(snake_case__			,							snake_case__			,							repo_type='dataset'     )			,							'r'     )     )
  _lowercase							= {int(snake_case__     ): v for k, v in idalabel.items()}
  _lowercase							= {v: k for k, v in idalabel.items()}

  _lowercase							= SwinConfig(
      embed_dim=snake_case__			,							depths=snake_case__			,							num_heads=snake_case__			,							window_size=snake_case__			,							out_features=['stage1', 'stage2', 'stage3', 'stage4']			,							)
  _lowercase							= UperNetConfig(
      backbone_config=snake_case__			,							auxiliary_in_channels=snake_case__			,							num_labels=snake_case__			,							idalabel=snake_case__			,							labelaid=snake_case__			,							)

  return config

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Optional[int]:
  _lowercase							= []

  # 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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Union[str, Any]			,							snake_case__  :Tuple     )      ->						Dict:
  _lowercase							= dct.pop(snake_case__     )
  _lowercase							= val

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						List[str]:
  _lowercase						,		_lowercase							= x.shape
  _lowercase							= x.reshape(snake_case__			,							4			,							in_channel // 4     )
  _lowercase							= x[:, [0, 2, 1, 3], :].transpose(1			,							2     ).reshape(snake_case__			,							snake_case__     )
  return x

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						int:
  _lowercase						,		_lowercase							= x.shape
  _lowercase							= x.reshape(snake_case__			,							in_channel // 4			,							4     )
  _lowercase							= x[:, :, [0, 2, 1, 3]].transpose(1			,							2     ).reshape(snake_case__			,							snake_case__     )

  return x

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						List[Any]:
  _lowercase							= x.shape[0]
  _lowercase							= x.reshape(4			,							in_channel // 4     )
  _lowercase							= x[[0, 2, 1, 3], :].transpose(0			,							1     ).reshape(snake_case__     )
  return x

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Tuple:
  _lowercase							= x.shape[0]
  _lowercase							= x.reshape(in_channel // 4			,							4     )
  _lowercase							= x[:, [0, 2, 1, 3]].transpose(0			,							1     ).reshape(snake_case__     )
  return x

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]			,							snake_case__  :Tuple			,							snake_case__  :List[Any]     )      ->						List[Any]:
  _lowercase							= {
      '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',
  }
  _lowercase							= model_name_to_url[model_name]
  _lowercase							= torch.hub.load_state_dict_from_url(snake_case__			,							map_location='cpu'			,							file_name=snake_case__     )[
      'state_dict'
  ]

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

  _lowercase							= get_upernet_config(snake_case__     )
  _lowercase							= UperNetForSemanticSegmentation(snake_case__     )
  model.eval()

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

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

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

  model.load_state_dict(snake_case__     )

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

  _lowercase							= SegformerImageProcessor()
  _lowercase							= processor(snake_case__			,							return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    _lowercase							= model(snake_case__     )
    _lowercase							= outputs.logits

  print(logits.shape     )
  print('First values of logits:'			,							logits[0, 0, :3, :3]     )
  # assert values
  if model_name == "upernet-swin-tiny":
    _lowercase							= 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":
    _lowercase							= 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":
    _lowercase							= 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":
    _lowercase							= 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]			,							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(snake_case__     )
    print(F"""Saving processor to {pytorch_dump_folder_path}"""     )
    processor.save_pretrained(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)






from itertools import count

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int = 50     )      ->						int:
  _lowercase							= [1] * min_block_length

  for n in count(snake_case__     ):
    fill_count_functions.append(1     )

    for block_length in range(snake_case__			,							n + 1     ):
      for block_start in range(n - block_length     ):
        fill_count_functions[n] += fill_count_functions[
            n - block_start - block_length - 1
        ]

      fill_count_functions[n] += 1

    if fill_count_functions[n] > 100_0000:
      break

  return n


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