





"""simple docstring"""





from __future__ import annotations
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:  # noqa: E741





  """simple docstring"""


  while r - l > 1:
    lowerCAmelCase__     :List[str]											=				(l + r) // 2
    if v[m] >= key:
      lowerCAmelCase__     :str											=				m
    else:
      lowerCAmelCase__     :Dict											=				m  # noqa: E741
  return r
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  if len(_SCREAMING_SNAKE_CASE     ) == 0:
    return 0

  lowerCAmelCase__     :int											=				[0] * len(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				1

  lowerCAmelCase__     :Any											=				v[0]

  for i in range(1	,  len(_SCREAMING_SNAKE_CASE     )     ):
    if v[i] < tail[0]:
      lowerCAmelCase__     :List[Any]											=				v[i]
    elif v[i] > tail[length - 1]:
      lowerCAmelCase__     :Optional[int]											=				v[i]
      length += 1
    else:
      lowerCAmelCase__     :Optional[int]											=				v[i]

  return length


if __name__ == "__main__":
  import doctest

  doctest.testmod()







"""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  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


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

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

  lowerCAmelCase__     :List[str]											=				ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE	,  hidden_sizes=_SCREAMING_SNAKE_CASE	,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
  lowerCAmelCase__     :Union[str, Any]											=				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 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]

  # 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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				{
      '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',
  }
  lowerCAmelCase__     :List[Any]											=				model_name_to_url[model_name]
  lowerCAmelCase__     :Optional[Any]											=				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['state_dict']

  lowerCAmelCase__     :List[Any]											=				get_upernet_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowerCAmelCase__     :Optional[int]											=				state_dict.pop(_SCREAMING_SNAKE_CASE     )
    if "bn" in key:
      lowerCAmelCase__     :Any											=				key.replace('bn'	,  'batch_norm'     )
    lowerCAmelCase__     :int											=				val

  # rename keys
  lowerCAmelCase__     :Optional[Any]											=				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
  lowerCAmelCase__     :str											=				'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  lowerCAmelCase__     :Optional[Any]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     ).convert('RGB'     )

  lowerCAmelCase__     :Tuple											=				SegformerImageProcessor()
  lowerCAmelCase__     :List[Any]											=				processor(_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				model(_SCREAMING_SNAKE_CASE     )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
  elif model_name == "upernet-convnext-small":
    lowerCAmelCase__     :Union[str, Any]											=				torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
  elif model_name == "upernet-convnext-base":
    lowerCAmelCase__     :Dict											=				torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
  elif model_name == "upernet-convnext-large":
    lowerCAmelCase__     :List[str]											=				torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
  elif model_name == "upernet-convnext-xlarge":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
  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)
