"""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   (		A      ):






     '''simple docstring'''



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

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

     lowercase__				 =							SwinConfig(
         embed_dim=A    ,				depths=A    ,				num_heads=A    ,				window_size=A    ,				out_features=["stage1", "stage2", "stage3", "stage4"]    ,				)
     lowercase__				 =							UperNetConfig(
         backbone_config=A    ,				auxiliary_in_channels=A    ,				num_labels=A    ,				idalabel=A    ,				labelaid=A    ,				)

     return config







def 					__a   (		A      ):






     '''simple docstring'''



     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 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							dct.pop(A      )
     lowercase__				 =							val







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     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 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		A      ):






     '''simple docstring'''



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

     return x







def 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     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(A    ,				map_location="cpu"    ,				file_name=A      )[
         "state_dict"
     ]

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

     lowercase__				 =							get_upernet_config(A      )
     lowercase__				 =							UperNetForSemanticSegmentation(A      )
     model.eval()

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

     # rename keys
     lowercase__				 =							create_rename_keys(A      )
     for src, dest in rename_keys:
          rename_key(A    ,				A    ,				A      )
     read_in_q_k_v(A    ,				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(A      )
               if "norm" in key:
                    lowercase__				 =							reverse_correct_unfold_norm_order(A      )

     model.load_state_dict(A      )

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

     lowercase__				 =							SegformerImageProcessor()
     lowercase__				 =							processor(A    ,				return_tensors="pt"      ).pixel_values

     with torch.no_grad():
          lowercase__				 =							model(A      )
          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]    ,				A    ,				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(A      )
          print(f'''Saving processor to {pytorch_dump_folder_path}'''      )
          processor.save_pretrained(A      )

     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_:					int     =					parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




