




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

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

      _A							:  Any    =		SwinConfig(
          embed_dim=snake_case_,depths=snake_case_,num_heads=snake_case_,window_size=snake_case_,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""],)
      _A							:  Any    =		UperNetConfig(
          backbone_config=snake_case_,auxiliary_in_channels=snake_case_,num_labels=snake_case_,idalabel=snake_case_,labelaid=snake_case_,)

      return config







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]

      # 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        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  str    =		dct.pop(snake_case_  )
      _A							:  Union[str, Any]    =		val







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







def        lowerCAmelCase_			(  snake_case_  ):
      _A    ,     _A							:  Optional[int]    =		x.shape
      _A							:  Optional[Any]    =		x.reshape(snake_case_,4,in_channel // 4  )
      _A							:  Tuple    =		x[:, [0, 2, 1, 3], :].transpose(1,2  ).reshape(snake_case_,snake_case_  )
      return x







def        lowerCAmelCase_			(  snake_case_  ):
      _A    ,     _A							:  Optional[Any]    =		x.shape
      _A							:  str    =		x.reshape(snake_case_,in_channel // 4,4  )
      _A							:  Any    =		x[:, :, [0, 2, 1, 3]].transpose(1,2  ).reshape(snake_case_,snake_case_  )

      return x







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Any    =		x.shape[0]
      _A							:  List[Any]    =		x.reshape(4,in_channel // 4  )
      _A							:  Dict    =		x[[0, 2, 1, 3], :].transpose(0,1  ).reshape(snake_case_  )
      return x







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







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		{
          """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""",
      }
      _A							:  Optional[int]    =		model_name_to_url[model_name]
      _A							:  int    =		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  )

      _A							:  Tuple    =		get_upernet_config(snake_case_  )
      _A							:  Optional[Any]    =		UperNetForSemanticSegmentation(snake_case_  )
      model.eval()

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

      # rename keys
      _A							:  Tuple    =		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:
                        _A							:  Union[str, Any]    =		reverse_correct_unfold_reduction_order(snake_case_  )
                  if "norm" in key:
                        _A							:  str    =		reverse_correct_unfold_norm_order(snake_case_  )

      model.load_state_dict(snake_case_  )

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

      _A							:  int    =		SegformerImageProcessor()
      _A							:  List[Any]    =		processor(snake_case_,return_tensors="""pt"""  ).pixel_values

      with torch.no_grad():
            _A							:  Any    =		model(snake_case_  )
            _A							:  str    =		outputs.logits

      print(logits.shape  )
      print("""First values of logits:""",logits[0, 0, :3, :3]  )
      # assert values
      if model_name == "upernet-swin-tiny":
            _A							:  List[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":
            _A							:  Optional[Any]    =		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":
            _A							:  Union[str, 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":
            _A							:  List[Any]    =		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],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)







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Union[str, Any]    =		""""""
      for i in table:
            res += inp[i - 1]
      return res







def        lowerCAmelCase_			(  snake_case_  ):
      return data[1:] + data[0]







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Dict    =		""""""
      for i in range(len(snake_case_  )  ):
            if a[i] == b[i]:
                  res += "0"
            else:
                  res += "1"
      return res







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  int    =		int("""0b""" + data[0] + data[-1],2  )
      _A							:  Any    =		int("""0b""" + data[1:3],2  )
      return bin(s[row][col]  )[2:]







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A							:  List[str]    =		message[:4]
      _A							:  List[Any]    =		message[4:]
      _A							:  Union[str, Any]    =		apply_table(snake_case_,snake_case_  )
      _A							:  List[Any]    =		xor(snake_case_,snake_case_  )
      _A							:  Optional[Any]    =		apply_sbox(snake_case_,temp[:4]  )  # noqa: E741
      _A							:  List[Any]    =		apply_sbox(snake_case_,temp[4:]  )
      _A							:  int    =		"""0""" * (2 - len(snake_case_  )) + l  # noqa: E741
      _A							:  Union[str, Any]    =		"""0""" * (2 - len(snake_case_  )) + r
      _A							:  List[Any]    =		apply_table(l + r,snake_case_  )
      _A							:  Any    =		xor(snake_case_,snake_case_  )
      return temp + right


if __name__ == "__main__":
  _snake_case            = input("Enter 10 bit key: ")
  _snake_case            = input("Enter 8 bit message: ")

  _snake_case            = [6, 3, 7, 4, 8, 5, 10, 9]
  _snake_case            = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
  _snake_case            = [2, 4, 3, 1]
  _snake_case            = [2, 6, 3, 1, 4, 8, 5, 7]
  _snake_case            = [4, 1, 3, 5, 7, 2, 8, 6]
  _snake_case            = [4, 1, 2, 3, 2, 3, 4, 1]
  _snake_case            = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
  _snake_case            = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

  # key generation
  _snake_case            = apply_table(key, paa_table)
  _snake_case            = temp[:5]
  _snake_case            = temp[5:]
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)

  # encryption
  _snake_case            = apply_table(message, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Cipher text is:", CT)

  # decryption
  _snake_case            = apply_table(CT, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Plain text after decypting is:", PT)

