

'''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 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    384
     if "tiny" in model_name:
          __magic_name__						       =    [3, 3, 9, 3]
          __magic_name__						       =    [96, 192, 384, 768]
     if "small" in model_name:
          __magic_name__						       =    [3, 3, 27, 3]
          __magic_name__						       =    [96, 192, 384, 768]
     if "base" in model_name:
          __magic_name__						       =    [3, 3, 27, 3]
          __magic_name__						       =    [128, 256, 512, 1024]
          __magic_name__						       =    512
     if "large" in model_name:
          __magic_name__						       =    [3, 3, 27, 3]
          __magic_name__						       =    [192, 384, 768, 1536]
          __magic_name__						       =    768
     if "xlarge" in model_name:
          __magic_name__						       =    [3, 3, 27, 3]
          __magic_name__						       =    [256, 512, 1024, 2048]
          __magic_name__						       =    1024

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

     __magic_name__						       =    ConvNextConfig(
         depths=a		,						hidden_sizes=a		,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
     __magic_name__						       =    UperNetConfig(
         backbone_config=a		,						auxiliary_in_channels=a		,						num_labels=a		,						idalabel=a		,						labelaid=a		,						)

     return config




def 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    []

     # 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 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    dct.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a		,						a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    {
         '''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''',
     }
     __magic_name__						       =    model_name_to_url[model_name]
     __magic_name__						       =    torch.hub.load_state_dict_from_url(a		,						map_location='''cpu'''			)['''state_dict''']

     __magic_name__						       =    get_upernet_config(a			)
     __magic_name__						       =    UperNetForSemanticSegmentation(a			)
     model.eval()

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

     # rename keys
     __magic_name__						       =    create_rename_keys(a			)
     for src, dest in rename_keys:
          rename_key(a		,						a		,						a			)

     model.load_state_dict(a			)

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

     __magic_name__						       =    SegformerImageProcessor()
     __magic_name__						       =    processor(a		,						return_tensors='''pt'''			).pixel_values

     with torch.no_grad():
          __magic_name__						       =    model(a			)

     if model_name == "upernet-convnext-tiny":
          __magic_name__						       =    torch.tensor(
              [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]			)
     elif model_name == "upernet-convnext-small":
          __magic_name__						       =    torch.tensor(
              [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]			)
     elif model_name == "upernet-convnext-base":
          __magic_name__						       =    torch.tensor(
              [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]			)
     elif model_name == "upernet-convnext-large":
          __magic_name__						       =    torch.tensor(
              [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]			)
     elif model_name == "upernet-convnext-xlarge":
          __magic_name__						       =    torch.tensor(
              [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]			)
     print('''Logits:'''		,						outputs.logits[0, 0, :3, :3]			)
     assert torch.allclose(outputs.logits[0, 0, :3, :3]		,						a		,						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(a			)
          print(F'''Saving processor to {pytorch_dump_folder_path}'''			)
          processor.save_pretrained(a			)

     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__":
      _lowerCAmelCase         =						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."
      )

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









'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase         =						object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase         =						object()




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tuple((re.compile(x + '''$'''			) for x in qs)			)
     for i in range(len(a			) - len(a			) + 1			):
          __magic_name__						       =    [x.match(a			) for x, y in zip(a		,						ks[i:]			)]
          if matches and all(a			):
               return True
     return False




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     def replace(a		,						a			):
          for rule, replacement in rules:
               if _match(a		,						a			):
                    return replacement
          return val

     return replace




def 						UpperCamelCase	(			)				->     Union[str, Any]:






     '''simple docstring'''
     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P('''mp'''		,						a			)),
         (("transformer", "wte", "embedding"), P('''mp'''		,						a			)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a		,						'''mp'''			)),
         (("attention", "out_proj", "kernel"), P('''mp'''		,						a			)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(a		,						'''mp'''			)),
         (("mlp", "c_fc", "bias"), P('''mp'''			)),
         (("mlp", "c_proj", "kernel"), P('''mp'''		,						a			)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    _get_partition_rules()
     __magic_name__						       =    _replacement_rules(a			)
     __magic_name__						       =    {k: _unmatched for k in flatten_dict(a			)}
     __magic_name__						       =    {k: replace(a		,						a			) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(a			)			)






