




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						(						_lowerCAmelCase							:      Optional[Any]		):




 """simple docstring"""







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

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

 UpperCAmelCase__							       =  ConvNextConfig(
     depths=__UpperCAmelCase  ,							hidden_sizes=__UpperCAmelCase  ,							out_features=["stage1", "stage2", "stage3", "stage4"]		)
 UpperCAmelCase__							       =  UperNetConfig(
     backbone_config=__UpperCAmelCase  ,							auxiliary_in_channels=__UpperCAmelCase  ,							num_labels=__UpperCAmelCase  ,							idalabel=__UpperCAmelCase  ,							labelaid=__UpperCAmelCase  ,							)

 return config



def   lowerCAmelCase						(						_lowerCAmelCase							:      List[Any]		):




 """simple docstring"""







 UpperCAmelCase__							       =  []

 # 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						(						_lowerCAmelCase							:      Union[str, Any]  ,							_lowerCAmelCase							:      List[str]  ,							_lowerCAmelCase							:      List[str]		):




 """simple docstring"""







 UpperCAmelCase__							       =  dct.pop(__UpperCAmelCase		)
 UpperCAmelCase__							       =  val



def   lowerCAmelCase						(						_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      Any		):




 """simple docstring"""







 UpperCAmelCase__							       =  {
     "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",
 }
 UpperCAmelCase__							       =  model_name_to_url[model_name]
 UpperCAmelCase__							       =  torch.hub.load_state_dict_from_url(__UpperCAmelCase  ,							map_location="cpu"		)["state_dict"]

 UpperCAmelCase__							       =  get_upernet_config(__UpperCAmelCase		)
 UpperCAmelCase__							       =  UperNetForSemanticSegmentation(__UpperCAmelCase		)
 model.eval()

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

 # rename keys
 UpperCAmelCase__							       =  create_rename_keys(__UpperCAmelCase		)
 for src, dest in rename_keys:
  rename_key(__UpperCAmelCase  ,							__UpperCAmelCase  ,							__UpperCAmelCase		)

 model.load_state_dict(__UpperCAmelCase		)

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

 UpperCAmelCase__							       =  SegformerImageProcessor()
 UpperCAmelCase__							       =  processor(__UpperCAmelCase  ,							return_tensors="pt"		).pixel_values

 with torch.no_grad():
  UpperCAmelCase__							       =  model(__UpperCAmelCase		)

 if model_name == "upernet-convnext-tiny":
  UpperCAmelCase__							       =  torch.tensor(
      [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]		)
 elif model_name == "upernet-convnext-small":
  UpperCAmelCase__							       =  torch.tensor(
      [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]		)
 elif model_name == "upernet-convnext-base":
  UpperCAmelCase__							       =  torch.tensor(
      [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]		)
 elif model_name == "upernet-convnext-large":
  UpperCAmelCase__							       =  torch.tensor(
      [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]		)
 elif model_name == "upernet-convnext-xlarge":
  UpperCAmelCase__							       =  torch.tensor(
      [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]		)
 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  :      Optional[int]						=      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  :      Optional[Any]						=      parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)