



"""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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=384
   if "tiny" in model_name:
      SCREAMING_SNAKE_CASE_: Any     			=[3, 3, 9, 3]
      SCREAMING_SNAKE_CASE_: List[Any]     			=[96, 192, 384, 768]
   if "small" in model_name:
      SCREAMING_SNAKE_CASE_: str     			=[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_: Any     			=[96, 192, 384, 768]
   if "base" in model_name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[128, 256, 512, 1024]
      SCREAMING_SNAKE_CASE_: List[Any]     			=512
   if "large" in model_name:
      SCREAMING_SNAKE_CASE_: List[str]     			=[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[192, 384, 768, 1536]
      SCREAMING_SNAKE_CASE_: Tuple     			=768
   if "xlarge" in model_name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=[3, 3, 27, 3]
      SCREAMING_SNAKE_CASE_: int     			=[256, 512, 1024, 2048]
      SCREAMING_SNAKE_CASE_: int     			=1024

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

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=ConvNextConfig(
       depths=lowercase							,			hidden_sizes=lowercase							,			out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=UperNetConfig(
       backbone_config=lowercase							,			auxiliary_in_channels=lowercase							,			num_labels=lowercase							,			idalabel=lowercase							,			labelaid=lowercase							,			)

   return config





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=[]

   # 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      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=dct.pop(lowercase )
   SCREAMING_SNAKE_CASE_: int     			=val





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			={
       """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""",
   }
   SCREAMING_SNAKE_CASE_: Tuple     			=model_name_to_url[model_name]
   SCREAMING_SNAKE_CASE_: str     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )["""state_dict"""]

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_upernet_config(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=UperNetForSemanticSegmentation(lowercase )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Any     			=state_dict.pop(lowercase )
      if "bn" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.replace("""bn"""							,			"""batch_norm""" )
      SCREAMING_SNAKE_CASE_: Dict     			=val

   # rename keys
   SCREAMING_SNAKE_CASE_: Dict     			=create_rename_keys(lowercase )
   for src, dest in rename_keys:
      rename_key(lowercase							,			lowercase							,			lowercase )

   model.load_state_dict(lowercase )

   # verify on image
   SCREAMING_SNAKE_CASE_: Optional[Any]     			="""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
   SCREAMING_SNAKE_CASE_: str     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw ).convert("""RGB""" )

   SCREAMING_SNAKE_CASE_: List[str]     			=SegformerImageProcessor()
   SCREAMING_SNAKE_CASE_: List[str]     			=processor(lowercase							,			return_tensors="""pt""" ).pixel_values

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_: Tuple     			=model(lowercase )

   if model_name == "upernet-convnext-tiny":
      SCREAMING_SNAKE_CASE_: List[Any]     			=torch.tensor(
          [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
   elif model_name == "upernet-convnext-small":
      SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
          [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
   elif model_name == "upernet-convnext-base":
      SCREAMING_SNAKE_CASE_: str     			=torch.tensor(
          [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
   elif model_name == "upernet-convnext-large":
      SCREAMING_SNAKE_CASE_: str     			=torch.tensor(
          [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
   elif model_name == "upernet-convnext-xlarge":
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(
          [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
   print("""Logits:"""							,			outputs.logits[0, 0, :3, :3] )
   assert torch.allclose(outputs.logits[0, 0, :3, :3]							,			lowercase							,			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(lowercase )
      print(f'''Saving processor to {pytorch_dump_folder_path}''' )
      processor.save_pretrained(lowercase )

   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-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     =			parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




     return {"x": self.x[i], "y": self.y[i]}







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			={"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
   SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""csv"""							,			data_files=lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=datasets["""train"""].unique("""label""" )

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(
          examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase							,			padding="""max_length""" )
      if "label" in examples:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=[label_to_id[l] for l in examples["""label"""]]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   SCREAMING_SNAKE_CASE_: List[Any]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""sentence1""", """sentence2""", """label"""]							,			)

   def collate_fn(lowercase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



