




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

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

  lowerCAmelCase__				:  Optional[int]		       =     ConvNextConfig(
      depths=UpperCamelCase	,      hidden_sizes=UpperCamelCase	,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']      )
  lowerCAmelCase__				:  Any		       =     UperNetConfig(
      backbone_config=UpperCamelCase	,      auxiliary_in_channels=UpperCamelCase	,      num_labels=UpperCamelCase	,      idalabel=UpperCamelCase	,      labelaid=UpperCamelCase	,      )

  return config



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Union[str, Any]		       =     dct.pop(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     val



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Dict:
  lowerCAmelCase__				:  Optional[int]		       =     {
      '''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__				:  Optional[Any]		       =     model_name_to_url[model_name]
  lowerCAmelCase__				:  Dict		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      map_location='''cpu'''      )['''state_dict''']

  lowerCAmelCase__				:  Union[str, Any]		       =     get_upernet_config(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     UperNetForSemanticSegmentation(UpperCamelCase      )
  model.eval()

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

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

  model.load_state_dict(UpperCamelCase      )

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

  lowerCAmelCase__				:  Optional[int]		       =     SegformerImageProcessor()
  lowerCAmelCase__				:  Optional[int]		       =     processor(UpperCamelCase	,      return_tensors='''pt'''      ).pixel_values

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

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__				:  List[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":
    lowerCAmelCase__				:  str		       =     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":
    lowerCAmelCase__				:  Optional[int]		       =     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":
    lowerCAmelCase__				:  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":
    lowerCAmelCase__				:  List[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]	,      UpperCamelCase	,      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(UpperCamelCase      )
    print(F"""Saving processor to {pytorch_dump_folder_path}"""      )
    processor.save_pretrained(UpperCamelCase      )

  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)







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     comments[0] if len(UpperCamelCase      ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''      )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''      )
      issue.remove_from_labels('''stale'''      )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''      )
      issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
     main()

