


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

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

 __A  :		Union[str, Any]       =			SwinConfig(
     embed_dim=UpperCamelCase__,				depths=UpperCamelCase__,				num_heads=UpperCamelCase__,				window_size=UpperCamelCase__,				out_features=['stage1', 'stage2', 'stage3', 'stage4'],				)
 __A  :		Union[str, Any]       =			UperNetConfig(
     backbone_config=UpperCamelCase__,				auxiliary_in_channels=UpperCamelCase__,				num_labels=UpperCamelCase__,				idalabel=UpperCamelCase__,				labelaid=UpperCamelCase__,				)

 return config


def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A  :		Tuple       =			[]

 # 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 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					int			):
 __A  :		List[Any]       =			dct.pop(UpperCamelCase__			)
 __A  :		Dict       =			val


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


def 			_lowercase       (				UpperCamelCase__       :					Tuple			):
 __A     ,__A  :		Dict       =			x.shape
 __A  :		List[Any]       =			x.reshape(UpperCamelCase__,				4,				in_channel // 4			)
 __A  :		Optional[Any]       =			x[:, [0, 2, 1, 3], :].transpose(1,				2			).reshape(UpperCamelCase__,				UpperCamelCase__			)
 return x


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A     ,__A  :		str       =			x.shape
 __A  :		List[str]       =			x.reshape(UpperCamelCase__,				in_channel // 4,				4			)
 __A  :		Any       =			x[:, :, [0, 2, 1, 3]].transpose(1,				2			).reshape(UpperCamelCase__,				UpperCamelCase__			)

 return x


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
 __A  :		Union[str, Any]       =			x.shape[0]
 __A  :		Optional[Any]       =			x.reshape(4,				in_channel // 4			)
 __A  :		Any       =			x[[0, 2, 1, 3], :].transpose(0,				1			).reshape(UpperCamelCase__			)
 return x


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		Any       =			x.shape[0]
 __A  :		int       =			x.reshape(in_channel // 4,				4			)
 __A  :		Optional[Any]       =			x[:, [0, 2, 1, 3]].transpose(0,				1			).reshape(UpperCamelCase__			)
 return x


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Optional[int]       =			{
     '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  :		List[str]       =			torch.hub.load_state_dict_from_url(UpperCamelCase__,				map_location='cpu',				file_name=UpperCamelCase__			)[
     'state_dict'
 ]

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

 __A  :		List[Any]       =			get_upernet_config(UpperCamelCase__			)
 __A  :		List[Any]       =			UperNetForSemanticSegmentation(UpperCamelCase__			)
 model.eval()

 # replace "bn" => "batch_norm"
 for key in state_dict.copy().keys():
  __A  :		Tuple       =			state_dict.pop(UpperCamelCase__			)
  if "bn" in key:
   __A  :		Optional[int]       =			key.replace('bn',				'batch_norm'			)
  __A  :		List[str]       =			val

 # rename keys
 __A  :		int       =			create_rename_keys(UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				config.backbone_config			)

 # fix downsample parameters
 for key, value in state_dict.items():
  if "downsample" in key:
   if "reduction" in key:
    __A  :		Dict       =			reverse_correct_unfold_reduction_order(UpperCamelCase__			)
   if "norm" in key:
    __A  :		Any       =			reverse_correct_unfold_norm_order(UpperCamelCase__			)

 model.load_state_dict(UpperCamelCase__			)

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

 __A  :		str       =			SegformerImageProcessor()
 __A  :		List[str]       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

 with torch.no_grad():
  __A  :		Optional[int]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

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

 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__":
 UpperCAmelCase_ :     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.'
 )

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










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			0.0

  # Start at index 1 and do index 0 at the end.
  for i in range(1      ,      self.order + 1						):
   result += (
       self.b_coeffs[i] * self.input_history[i - 1]
       - self.a_coeffs[i] * self.output_history[i - 1]
   )

  __A  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






