





"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]			):
 lowerCAmelCase    				= 384
 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    				= 128
  lowerCAmelCase    				= (2, 2, 18, 2)
  lowerCAmelCase    				= (4, 8, 16, 32)
  lowerCAmelCase    				= 12
  lowerCAmelCase    				= 512
 elif "large" in model_name:
  lowerCAmelCase    				= 192
  lowerCAmelCase    				= (2, 2, 18, 2)
  lowerCAmelCase    				= (6, 12, 24, 48)
  lowerCAmelCase    				= 12
  lowerCAmelCase    				= 768

 # set label information
 lowerCAmelCase    				= 150
 lowerCAmelCase    				= 'huggingface/label-files'
 lowerCAmelCase    				= 'ade20k-id2label.json'
 lowerCAmelCase    				= json.load(open(hf_hub_download(_UpperCAmelCase  ,     _UpperCAmelCase  ,     repo_type='dataset'			)  ,     'r'			)			)
 lowerCAmelCase    				= {int(_UpperCAmelCase			): v for k, v in idalabel.items()}
 lowerCAmelCase    				= {v: k for k, v in idalabel.items()}

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

 return config

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 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			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    int			):
 lowerCAmelCase    				= dct.pop(_UpperCAmelCase			)
 lowerCAmelCase    				= val

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Union[str, Any]			):
 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			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase				,lowerCAmelCase    				= x.shape
 lowerCAmelCase    				= x.reshape(_UpperCAmelCase  ,     4  ,     in_channel // 4			)
 lowerCAmelCase    				= x[:, [0, 2, 1, 3], :].transpose(1  ,     2			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase				,lowerCAmelCase    				= x.shape
 lowerCAmelCase    				= x.reshape(_UpperCAmelCase  ,     in_channel // 4  ,     4			)
 lowerCAmelCase    				= x[:, :, [0, 2, 1, 3]].transpose(1  ,     2			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			)

 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]			):
 lowerCAmelCase    				= x.shape[0]
 lowerCAmelCase    				= x.reshape(4  ,     in_channel // 4			)
 lowerCAmelCase    				= x[[0, 2, 1, 3], :].transpose(0  ,     1			).reshape(_UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= x.shape[0]
 lowerCAmelCase    				= x.reshape(in_channel // 4  ,     4			)
 lowerCAmelCase    				= x[:, [0, 2, 1, 3]].transpose(0  ,     1			).reshape(_UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    int			):
 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(_UpperCAmelCase  ,     map_location='cpu'  ,     file_name=_UpperCAmelCase			)[
     'state_dict'
 ]

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

 lowerCAmelCase    				= get_upernet_config(_UpperCAmelCase			)
 lowerCAmelCase    				= UperNetForSemanticSegmentation(_UpperCAmelCase			)
 model.eval()

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

 # rename keys
 lowerCAmelCase    				= 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:
    lowerCAmelCase    				= reverse_correct_unfold_reduction_order(_UpperCAmelCase			)
   if "norm" in key:
    lowerCAmelCase    				= reverse_correct_unfold_norm_order(_UpperCAmelCase			)

 model.load_state_dict(_UpperCAmelCase			)

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

 lowerCAmelCase    				= SegformerImageProcessor()
 lowerCAmelCase    				= processor(_UpperCAmelCase  ,     return_tensors='pt'			).pixel_values

 with torch.no_grad():
  lowerCAmelCase    				= model(_UpperCAmelCase			)
  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]  ,     _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     :						Tuple						       =  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     :						int						       =  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     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




