




'''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__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[str]	      =    384
      if "tiny" in model_name:
            snake_case__       :   Optional[Any]	      =    [3, 3, 9, 3]
            snake_case__       :   Dict	      =    [96, 192, 384, 768]
      if "small" in model_name:
            snake_case__       :   Tuple	      =    [3, 3, 27, 3]
            snake_case__       :   str	      =    [96, 192, 384, 768]
      if "base" in model_name:
            snake_case__       :   Tuple	      =    [3, 3, 27, 3]
            snake_case__       :   Dict	      =    [128, 256, 512, 1024]
            snake_case__       :   str	      =    512
      if "large" in model_name:
            snake_case__       :   str	      =    [3, 3, 27, 3]
            snake_case__       :   List[Any]	      =    [192, 384, 768, 1536]
            snake_case__       :   List[Any]	      =    768
      if "xlarge" in model_name:
            snake_case__       :   Optional[Any]	      =    [3, 3, 27, 3]
            snake_case__       :   Dict	      =    [256, 512, 1024, 2048]
            snake_case__       :   List[Any]	      =    1024

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

      snake_case__       :   Dict	      =    ConvNextConfig(
          depths=__SCREAMING_SNAKE_CASE				,      hidden_sizes=__SCREAMING_SNAKE_CASE				,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
      snake_case__       :   str	      =    UperNetConfig(
          backbone_config=__SCREAMING_SNAKE_CASE				,      auxiliary_in_channels=__SCREAMING_SNAKE_CASE				,      num_labels=__SCREAMING_SNAKE_CASE				,      idalabel=__SCREAMING_SNAKE_CASE				,      labelaid=__SCREAMING_SNAKE_CASE				,      )

      return config






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[str]:
      snake_case__       :   List[Any]	      =    []

      # 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__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      snake_case__       :   Dict	      =    dct.pop(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   int	      =    val






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   List[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',
      }
      snake_case__       :   Optional[int]	      =    model_name_to_url[model_name]
      snake_case__       :   Optional[Any]	      =    torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,      map_location='cpu'		)['state_dict']

      snake_case__       :   str	      =    get_upernet_config(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[int]	      =    UperNetForSemanticSegmentation(__SCREAMING_SNAKE_CASE		)
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            snake_case__       :   Tuple	      =    state_dict.pop(__SCREAMING_SNAKE_CASE		)
            if "bn" in key:
                  snake_case__       :   List[str]	      =    key.replace('bn'				,      'batch_norm'		)
            snake_case__       :   Optional[Any]	      =    val

      # rename keys
      snake_case__       :   List[str]	      =    create_rename_keys(__SCREAMING_SNAKE_CASE		)
      for src, dest in rename_keys:
            rename_key(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      model.load_state_dict(__SCREAMING_SNAKE_CASE		)

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

      snake_case__       :   Tuple	      =    SegformerImageProcessor()
      snake_case__       :   Dict	      =    processor(__SCREAMING_SNAKE_CASE				,      return_tensors='pt'		).pixel_values

      with torch.no_grad():
            snake_case__       :   Union[str, Any]	      =    model(__SCREAMING_SNAKE_CASE		)

      if model_name == "upernet-convnext-tiny":
            snake_case__       :   Optional[Any]	      =    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__       :   Optional[Any]	      =    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__       :   Any	      =    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__       :   List[Any]	      =    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__       :   Optional[Any]	      =    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]				,      __SCREAMING_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(__SCREAMING_SNAKE_CASE		)
            print(f"Saving processor to {pytorch_dump_folder_path}"		)
            processor.save_pretrained(__SCREAMING_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__":
      A_			      =		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."
      )

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






'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__      ,							snake_case__       :   Optional[int]	      =    [], []
      while len(__SCREAMING_SNAKE_CASE		) > 1:
            snake_case__      ,							snake_case__       :   Tuple	      =    min(__SCREAMING_SNAKE_CASE		), max(__SCREAMING_SNAKE_CASE		)
            start.append(__SCREAMING_SNAKE_CASE		)
            end.append(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
      end.reverse()
      return start + collection + end


if __name__ == "__main__":
      A_			      =		input("Enter numbers separated by a comma:\n").strip()
      A_			      =		[int(item) for item in user_input.split(",")]
      print(*merge_sort(unsorted), sep=",")
