




'''simple docstring'''





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				384
     if "tiny" in model_name:
          _snake_case							=				[3, 3, 9, 3]
          _snake_case							=				[96, 192, 384, 768]
     if "small" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[96, 192, 384, 768]
     if "base" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[128, 256, 512, 1_024]
          _snake_case							=				512
     if "large" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[192, 384, 768, 1_536]
          _snake_case							=				768
     if "xlarge" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[256, 512, 1_024, 2_048]
          _snake_case							=				1_024

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

     _snake_case							=				ConvNextConfig(
         depths=_UpperCamelCase ,						hidden_sizes=_UpperCamelCase ,						out_features=["stage1", "stage2", "stage3", "stage4"]   )
     _snake_case							=				UperNetConfig(
         backbone_config=_UpperCamelCase ,						auxiliary_in_channels=_UpperCamelCase ,						num_labels=_UpperCamelCase ,						idalabel=_UpperCamelCase ,						labelaid=_UpperCamelCase ,						)

     return config





def      __a     (       _UpperCamelCase:   Dict   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				[]

     # fmt: off
     # stem
     rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")   )
     rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")   )
     rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")   )
     rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.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}.{j}.gamma""", F"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.norm.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.norm.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")   )
               rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")   )
          if i > 0:
               rename_keys.append((F"""backbone.downsample_layers.{i}.0.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")   )
               rename_keys.append((F"""backbone.downsample_layers.{i}.0.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")   )
               rename_keys.append((F"""backbone.downsample_layers.{i}.1.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")   )
               rename_keys.append((F"""backbone.downsample_layers.{i}.1.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.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     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Dict   )						->			Union[str, Any]:

     """simple docstring"""




     _snake_case							=				dct.pop(_UpperCamelCase   )
     _snake_case							=				val





def      __a     (       _UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Dict   )						->			Dict:

     """simple docstring"""




     _snake_case							=				{
         "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
         "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
         "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
         "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
         "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
     }
     _snake_case							=				model_name_to_url[model_name]
     _snake_case							=				torch.hub.load_state_dict_from_url(_UpperCamelCase ,						map_location="cpu"   )["state_dict"]

     _snake_case							=				get_upernet_config(_UpperCamelCase   )
     _snake_case							=				UperNetForSemanticSegmentation(_UpperCamelCase   )
     model.eval()

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

     # rename keys
     _snake_case							=				create_rename_keys(_UpperCamelCase   )
     for src, dest in rename_keys:
          rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )

     model.load_state_dict(_UpperCamelCase   )

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

     _snake_case							=				SegformerImageProcessor()
     _snake_case							=				processor(_UpperCamelCase ,						return_tensors="pt"   ).pixel_values

     with torch.no_grad():
          _snake_case							=				model(_UpperCamelCase   )

     if model_name == "upernet-convnext-tiny":
          _snake_case							=				torch.tensor(
              [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]   )
     elif model_name == "upernet-convnext-small":
          _snake_case							=				torch.tensor(
              [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]   )
     elif model_name == "upernet-convnext-base":
          _snake_case							=				torch.tensor(
              [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]   )
     elif model_name == "upernet-convnext-large":
          _snake_case							=				torch.tensor(
              [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]   )
     elif model_name == "upernet-convnext-xlarge":
          _snake_case							=				torch.tensor(
              [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]   )
     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_						:      List[str]       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''upernet-convnext-tiny''',
       type=str,
       choices=[F'upernet-convnext-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
       help='''Name of the ConvNext 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_						:      Optional[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

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase_						:      int       =						TypeVar('''T''')
UpperCamelCase_						:      Dict       =						TypeVar('''U''')

class        _a    (						Generic[T, U]							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				key
          _snake_case							=				val
          _snake_case							=				None
          _snake_case							=				None






     def __repr__(			self   )							->      str:
          return (
              f"""Node: key: {self.key}, val: {self.val}, """
              f"""has next: {bool(self.next   )}, has prev: {bool(self.prev   )}"""
          )







class        _a    (						Generic[T, U]							):






     def __init__(			self   )							->      None:
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							, _snake_case							=				self.rear, self.head






     def __repr__(			self   )							->      str:
          _snake_case							=				["DoubleLinkedList"]
          _snake_case							=				self.head
          while node.next is not None:
               rep.append(str(_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          _snake_case							=				node
          _snake_case							=				previous
          _snake_case							=				node
          _snake_case							=				self.rear






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      DoubleLinkedListNode[T, U] | None:

          if node.prev is None or node.next is None:
               return None

          _snake_case							=				node.next
          _snake_case							=				node.prev
          _snake_case							=				None
          _snake_case							=				None
          return node







class        _a    (						Generic[T, U]							):
     SCREAMING_SNAKE_CASE_							:   dict[Callable[[T], U], LRUCache[T, U]]       =						{}






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				DoubleLinkedList()
          _snake_case							=				capacity
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				{}






     def __repr__(			self   )							->      str:
          return (
              f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              f"""capacity={self.capacity}, current size={self.num_keys})"""
          )






     def __contains__(			self ,_SCREAMING_SNAKE_CASE   )							->      bool:
          return key in self.cache






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      U | None:
          # Note: pythonic interface would throw KeyError rather than return None

          if key in self.cache:
               self.hits += 1
               _snake_case							=				self.cache[key]
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_SCREAMING_SNAKE_CASE   )
               return node.val
          self.miss += 1
          return None






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      None:

          if key not in self.cache:
               if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case							=				self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(_SCREAMING_SNAKE_CASE   ) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
               _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.list.add(self.cache[key]   )
               self.num_keys += 1

          else:
               # bump node to the end of the list, update value
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node is not None  # node guaranteed to be in list
               _snake_case							=				value
               self.list.add(_SCREAMING_SNAKE_CASE   )






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE = 128   )							->      Callable[[Callable[[T], U]], Callable[..., U]]:

          def cache_decorator_inner(_SCREAMING_SNAKE_CASE   ) -> Callable[..., U]:
               def cache_decorator_wrapper(*_SCREAMING_SNAKE_CASE   ) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                         _snake_case							=				LRUCache(_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				cls.decorator_function_to_instance_map[func].get(args[0]   )
                    if result is None:
                         _snake_case							=				func(*_SCREAMING_SNAKE_CASE   )
                         cls.decorator_function_to_instance_map[func].put(args[0] ,_SCREAMING_SNAKE_CASE   )
                    return result

               def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

               setattr(_SCREAMING_SNAKE_CASE ,"cache_info" ,_SCREAMING_SNAKE_CASE   )  # noqa: B010

               return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


