




'''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 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  __a			=					384
  if "tiny" in model_name:
    __a			=					[3, 3, 9, 3]
    __a			=					[96, 192, 384, 768]
  if "small" in model_name:
    __a			=					[3, 3, 27, 3]
    __a			=					[96, 192, 384, 768]
  if "base" in model_name:
    __a			=					[3, 3, 27, 3]
    __a			=					[128, 256, 512, 1024]
    __a			=					512
  if "large" in model_name:
    __a			=					[3, 3, 27, 3]
    __a			=					[192, 384, 768, 1536]
    __a			=					768
  if "xlarge" in model_name:
    __a			=					[3, 3, 27, 3]
    __a			=					[256, 512, 1024, 2048]
    __a			=					1024

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

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

  return config


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  __a			=					[]

  # 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 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							List[str]				,					a_	:							List[Any]			):
  __a			=					dct.pop(a_			)
  __a			=					val


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]				,					a_	:							Optional[Any]				,					a_	:							int			):
  __a			=					{
      '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',
  }
  __a			=					model_name_to_url[model_name]
  __a			=					torch.hub.load_state_dict_from_url(a_				,					map_location='cpu'			)['state_dict']

  __a			=					get_upernet_config(a_			)
  __a			=					UperNetForSemanticSegmentation(a_			)
  model.eval()

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

  # rename keys
  __a			=					create_rename_keys(a_			)
  for src, dest in rename_keys:
    rename_key(a_				,					a_				,					a_			)

  model.load_state_dict(a_			)

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

  __a			=					SegformerImageProcessor()
  __a			=					processor(a_				,					return_tensors='pt'			).pixel_values

  with torch.no_grad():
    __a			=					model(a_			)

  if model_name == "upernet-convnext-tiny":
    __a			=					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":
    __a			=					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":
    __a			=					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":
    __a			=					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":
    __a			=					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__":
  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'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



