"""simple docstring"""




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

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

  _lowerCAmelCase		: List[str]						    =  SwinConfig(
      embed_dim=_lowerCamelCase    ,depths=_lowerCamelCase    ,num_heads=_lowerCamelCase    ,window_size=_lowerCamelCase    ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,)
  _lowerCAmelCase		: Dict						    =  UperNetConfig(
      backbone_config=_lowerCamelCase    ,auxiliary_in_channels=_lowerCamelCase    ,num_labels=_lowerCamelCase    ,idalabel=_lowerCamelCase    ,labelaid=_lowerCamelCase    ,)

  return config





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]			) ->   Optional[Any]:
  _lowerCAmelCase		: List[str]						    =  []

  # 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  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[int]			) ->   Optional[Any]:
  _lowerCAmelCase		: Optional[int]						    =  dct.pop(_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  val





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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   Optional[Any]:
  _lowerCAmelCase		: Union[str, Any]						    =  x.shape
  _lowerCAmelCase		: Any						    =  x.reshape(_lowerCamelCase    ,4    ,in_channel // 4			)
  _lowerCAmelCase		: Optional[Any]						    =  x[:, [0, 2, 1, 3], :].transpose(1    ,2			).reshape(_lowerCamelCase    ,_lowerCamelCase			)
  return x





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]			) ->   Dict:
  _lowerCAmelCase		: str						    =  x.shape
  _lowerCAmelCase		: List[str]						    =  x.reshape(_lowerCamelCase    ,in_channel // 4    ,4			)
  _lowerCAmelCase		: int						    =  x[:, :, [0, 2, 1, 3]].transpose(1    ,2			).reshape(_lowerCamelCase    ,_lowerCamelCase			)

  return x





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]			) ->   Dict:
  _lowerCAmelCase		: Tuple						    =  x.shape[0]
  _lowerCAmelCase		: Optional[Any]						    =  x.reshape(4    ,in_channel // 4			)
  _lowerCAmelCase		: Optional[int]						    =  x[[0, 2, 1, 3], :].transpose(0    ,1			).reshape(_lowerCamelCase			)
  return x





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   List[str]:
  _lowerCAmelCase		: Tuple						    =  x.shape[0]
  _lowerCAmelCase		: Any						    =  x.reshape(in_channel // 4    ,4			)
  _lowerCAmelCase		: Any						    =  x[:, [0, 2, 1, 3]].transpose(0    ,1			).reshape(_lowerCamelCase			)
  return x





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[Any]			) ->   List[Any]:
  _lowerCAmelCase		: Optional[Any]						    =  {
      """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		: Union[str, Any]						    =  model_name_to_url[model_name]
  _lowerCAmelCase		: Optional[Any]						    =  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			)

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

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    _lowerCAmelCase		: Optional[int]						    =  state_dict.pop(_lowerCamelCase			)
    if "bn" in key:
      _lowerCAmelCase		: List[Any]						    =  key.replace("""bn"""    ,"""batch_norm"""			)
    _lowerCAmelCase		: str						    =  val

  # rename keys
  _lowerCAmelCase		: int						    =  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:
        _lowerCAmelCase		: Any						    =  reverse_correct_unfold_reduction_order(_lowerCamelCase			)
      if "norm" in key:
        _lowerCAmelCase		: Any						    =  reverse_correct_unfold_norm_order(_lowerCamelCase			)

  model.load_state_dict(_lowerCamelCase			)

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

  _lowerCAmelCase		: Tuple						    =  SegformerImageProcessor()
  _lowerCAmelCase		: Optional[Any]						    =  processor(_lowerCamelCase    ,return_tensors="""pt"""			).pixel_values

  with torch.no_grad():
    _lowerCAmelCase		: Dict						    =  model(_lowerCamelCase			)
    _lowerCAmelCase		: List[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		: Union[str, Any]						    =  torch.tensor(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]			)
  elif model_name == "upernet-swin-small":
    _lowerCAmelCase		: Tuple						    =  torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]			)
  elif model_name == "upernet-swin-base":
    _lowerCAmelCase		: Optional[Any]						    =  torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]			)
  elif model_name == "upernet-swin-large":
    _lowerCAmelCase		: Tuple						    =  torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]			)
  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__":
     _a   :	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.'
     )

     _a   :	Optional[Any] 					=     parser.parse_args()
     convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




