


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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     lowercase_  							=			3_8_4
     lowercase_  							=			7
     if "tiny" in model_name:
          lowercase_  							=			9_6
          lowercase_  							=			(2, 2, 6, 2)
          lowercase_  							=			(3, 6, 1_2, 2_4)
     elif "small" in model_name:
          lowercase_  							=			9_6
          lowercase_  							=			(2, 2, 1_8, 2)
          lowercase_  							=			(3, 6, 1_2, 2_4)
     elif "base" in model_name:
          lowercase_  							=			1_2_8
          lowercase_  							=			(2, 2, 1_8, 2)
          lowercase_  							=			(4, 8, 1_6, 3_2)
          lowercase_  							=			1_2
          lowercase_  							=			5_1_2
     elif "large" in model_name:
          lowercase_  							=			1_9_2
          lowercase_  							=			(2, 2, 1_8, 2)
          lowercase_  							=			(6, 1_2, 2_4, 4_8)
          lowercase_  							=			1_2
          lowercase_  							=			7_6_8

     # set label information
     lowercase_  							=			1_5_0
     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
     lowercase_  							=			dct.pop(snake_case_  )
     lowercase_  							=			val


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
     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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
     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__":
       a					  =     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					  =     parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
							a					  =     {
							    'linear': PIL.Image.Resampling.BILINEAR,
							    'bilinear': PIL.Image.Resampling.BILINEAR,
							    'bicubic': PIL.Image.Resampling.BICUBIC,
							    'lanczos': PIL.Image.Resampling.LANCZOS,
							    'nearest': PIL.Image.Resampling.NEAREST,
							}
else:
							a					  =     {
							    'linear': PIL.Image.LINEAR,
							    'bilinear': PIL.Image.BILINEAR,
							    'bicubic': PIL.Image.BICUBIC,
							    'lanczos': PIL.Image.LANCZOS,
							    'nearest': PIL.Image.NEAREST,
							}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			(images / 2 + 0.5).clamp(0						,							1  )
					lowercase_  							=			images.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()
					lowercase_  							=			numpy_to_pil(UpperCAmelCase__  )
					return images



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if images.ndim == 3:
										lowercase_  							=			images[None, ...]
					lowercase_  							=			(images * 2_5_5).round().astype("""uint8"""  )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										lowercase_  							=			[Image.fromarray(image.squeeze()						,							mode="""L"""  ) for image in images]
					else:
										lowercase_  							=			[Image.fromarray(UpperCAmelCase__  ) for image in images]

					return pil_images


