


"""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 				_snake_case      (   __snake_case							:      Optional[int]   ):




   """simple docstring"""







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

   # set label information
   _lowerCamelCase	:					int   =		150
   _lowerCamelCase	:					Any   =		"""huggingface/label-files"""
   _lowerCamelCase	:					str   =		"""ade20k-id2label.json"""
   _lowerCamelCase	:					Tuple   =		json.load(open(hf_hub_download(__snake_case						, __snake_case						, repo_type="""dataset"""   )						, """r"""   )   )
   _lowerCamelCase	:					Any   =		{int(__snake_case   ): v for k, v in idalabel.items()}
   _lowerCamelCase	:					Any   =		{v: k for k, v in idalabel.items()}

   _lowerCamelCase	:					int   =		SwinConfig(
       embed_dim=__snake_case						, depths=__snake_case						, num_heads=__snake_case						, window_size=__snake_case						, out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						, )
   _lowerCamelCase	:					Optional[int]   =		UperNetConfig(
       backbone_config=__snake_case						, auxiliary_in_channels=__snake_case						, num_labels=__snake_case						, idalabel=__snake_case						, labelaid=__snake_case						, )

   return config

def 				_snake_case      (   __snake_case							:      Any   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		[]

   # 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 				_snake_case      (   __snake_case							:      str						, __snake_case							:      Any						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					List[Any]   =		dct.pop(__snake_case   )
   _lowerCamelCase	:					int   =		val

def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
   for i in range(len(backbone_config.depths   )   ):
      _lowerCamelCase	:					Union[str, Any]   =		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	:					Optional[Any]   =		state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'   )
         _lowerCamelCase	:					str   =		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	:					Union[str, Any]   =		in_proj_weight[:dim, :]
         _lowerCamelCase	:					Union[str, Any]   =		in_proj_bias[: dim]
         _lowerCamelCase	:					List[Any]   =		in_proj_weight[
             dim : dim * 2, :
         ]
         _lowerCamelCase	:					Union[str, Any]   =		in_proj_bias[
             dim : dim * 2
         ]
         _lowerCamelCase	:					int   =		in_proj_weight[
             -dim :, :
         ]
         _lowerCamelCase	:					Tuple   =		in_proj_bias[-dim :]
         # fmt: on

def 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase   ,    _lowerCamelCase	:					int   =		x.shape
   _lowerCamelCase	:					Union[str, Any]   =		x.reshape(__snake_case						, 4						, in_channel // 4   )
   _lowerCamelCase	:					List[str]   =		x[:, [0, 2, 1, 3], :].transpose(1						, 2   ).reshape(__snake_case						, __snake_case   )
   return x

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase   ,    _lowerCamelCase	:					str   =		x.shape
   _lowerCamelCase	:					Optional[Any]   =		x.reshape(__snake_case						, in_channel // 4						, 4   )
   _lowerCamelCase	:					int   =		x[:, :, [0, 2, 1, 3]].transpose(1						, 2   ).reshape(__snake_case						, __snake_case   )

   return x

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		x.shape[0]
   _lowerCamelCase	:					int   =		x.reshape(4						, in_channel // 4   )
   _lowerCamelCase	:					int   =		x[[0, 2, 1, 3], :].transpose(0						, 1   ).reshape(__snake_case   )
   return x

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		x.shape[0]
   _lowerCamelCase	:					int   =		x.reshape(in_channel // 4						, 4   )
   _lowerCamelCase	:					Dict   =		x[:, [0, 2, 1, 3]].transpose(0						, 1   ).reshape(__snake_case   )
   return x

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      str						, __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		{
       """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	:					Optional[int]   =		model_name_to_url[model_name]
   _lowerCamelCase	:					Dict   =		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   )

   _lowerCamelCase	:					str   =		get_upernet_config(__snake_case   )
   _lowerCamelCase	:					List[str]   =		UperNetForSemanticSegmentation(__snake_case   )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      _lowerCamelCase	:					Union[str, Any]   =		state_dict.pop(__snake_case   )
      if "bn" in key:
         _lowerCamelCase	:					List[Any]   =		key.replace("""bn"""						, """batch_norm"""   )
      _lowerCamelCase	:					List[Any]   =		val

   # rename keys
   _lowerCamelCase	:					Tuple   =		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:
            _lowerCamelCase	:					Optional[int]   =		reverse_correct_unfold_reduction_order(__snake_case   )
         if "norm" in key:
            _lowerCamelCase	:					Optional[int]   =		reverse_correct_unfold_norm_order(__snake_case   )

   model.load_state_dict(__snake_case   )

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

   _lowerCamelCase	:					Optional[int]   =		SegformerImageProcessor()
   _lowerCamelCase	:					Any   =		processor(__snake_case						, return_tensors="""pt"""   ).pixel_values

   with torch.no_grad():
      _lowerCamelCase	:					List[str]   =		model(__snake_case   )
      _lowerCamelCase	:					Dict   =		outputs.logits

   print(logits.shape   )
   print("""First values of logits:"""						, logits[0, 0, :3, :3]   )
   # assert values
   if model_name == "upernet-swin-tiny":
      _lowerCamelCase	:					Tuple   =		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	:					List[Any]   =		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	:					Union[str, 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":
      _lowerCamelCase	:					Optional[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]						, __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__":
 UpperCAmelCase				=						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				=						parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

