




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    (							lowercase			: Dict  ):


      '''simple docstring'''



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

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

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

      return config

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					[]

      # 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    (							lowercase			: Tuple			,							lowercase			: Union[str, Any]			,							lowercase			: Tuple  ):


      '''simple docstring'''



      lowerCamelCase_	  =					dct.pop(lowercase  )
      lowerCamelCase_	  =					val

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Dict  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



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

      return x

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Union[str, Any]			,							lowercase			: Dict  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{
          '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_	  =					model_name_to_url[model_name]
      lowerCamelCase_	  =					torch.hub.load_state_dict_from_url(lowercase			,							map_location='cpu'			,							file_name=lowercase  )[
          'state_dict'
      ]

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

      lowerCamelCase_	  =					get_upernet_config(lowercase  )
      lowerCamelCase_	  =					UperNetForSemanticSegmentation(lowercase  )
      model.eval()

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

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

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

      model.load_state_dict(lowercase  )

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

      lowerCamelCase_	  =					SegformerImageProcessor()
      lowerCamelCase_	  =					processor(lowercase			,							return_tensors='pt'  ).pixel_values

      with torch.no_grad():
            lowerCamelCase_	  =					model(lowercase  )
            lowerCamelCase_	  =					outputs.logits

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

      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__":
 lowerCamelCase       :	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."
 )

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









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)		,							0						)
                         color_img.itemset((y, x, 1)		,							0						)
                         color_img.itemset((y, x, 2)		,							255						)
       return color_img, corner_list


if __name__ == "__main__":
 lowerCamelCase       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



