


'''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 	_A				(lowerCAmelCase__  :Any		)   ->  Union[str, Any]:




     '''simple docstring'''



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

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

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

     return config






def 	_A				(lowerCAmelCase__  :int		)   ->  Dict:




     '''simple docstring'''



     _a			      =							[]

     # 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 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Any				,						lowerCAmelCase__  :Dict		)   ->  Any:




     '''simple docstring'''



     _a			      =							dct.pop(lowerCAmelCase__		)
     _a			      =							val






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :str		)   ->  Optional[Any]:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  Dict:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :List[str]		)   ->  Optional[Any]:




     '''simple docstring'''



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

     return x






def 	_A				(lowerCAmelCase__  :int		)   ->  int:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  Optional[Any]:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							{
         '''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			      =							model_name_to_url[model_name]
     _a			      =							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		)

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

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

     # rename keys
     _a			      =							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:
                    _a			      =							reverse_correct_unfold_reduction_order(lowerCAmelCase__		)
               if "norm" in key:
                    _a			      =							reverse_correct_unfold_norm_order(lowerCAmelCase__		)

     model.load_state_dict(lowerCAmelCase__		)

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

     _a			      =							SegformerImageProcessor()
     _a			      =							processor(lowerCAmelCase__				,						return_tensors='pt'		).pixel_values

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

     print(logits.shape		)
     print('First values of logits:'				,						logits[0, 0, :3, :3]		)
     # assert values
     if model_name == "upernet-swin-tiny":
          _a			      =							torch.tensor(
              [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]		)
     elif model_name == "upernet-swin-small":
          _a			      =							torch.tensor(
              [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]		)
     elif model_name == "upernet-swin-base":
          _a			      =							torch.tensor(
              [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]		)
     elif model_name == "upernet-swin-large":
          _a			      =							torch.tensor(
              [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]		)
     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_					:    str          =    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_					:    str          =    parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''






from typing import Any






def 	_A				(lowerCAmelCase__  :list		)   ->  list[Any]:




     '''simple docstring'''



     if not input_list:
          return []
     _a			      =							[input_list.count(lowerCAmelCase__		) for value in input_list]
     _a			      =							max(lowerCAmelCase__		)  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(lowerCAmelCase__		) if value == y}		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

