





"""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 					__lowerCamelCase       (							a_					:		List[str]							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :str		    =  3_84
      if "tiny" in model_name:
            __SCREAMING_SNAKE_CASE       :int		    =  [3, 3, 9, 3]
            __SCREAMING_SNAKE_CASE       :Tuple		    =  [96, 1_92, 3_84, 7_68]
      if "small" in model_name:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  [3, 3, 27, 3]
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  [96, 1_92, 3_84, 7_68]
      if "base" in model_name:
            __SCREAMING_SNAKE_CASE       :int		    =  [3, 3, 27, 3]
            __SCREAMING_SNAKE_CASE       :Any		    =  [1_28, 2_56, 5_12, 10_24]
            __SCREAMING_SNAKE_CASE       :Dict		    =  5_12
      if "large" in model_name:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [3, 3, 27, 3]
            __SCREAMING_SNAKE_CASE       :Dict		    =  [1_92, 3_84, 7_68, 15_36]
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  7_68
      if "xlarge" in model_name:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  [3, 3, 27, 3]
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  [2_56, 5_12, 10_24, 20_48]
            __SCREAMING_SNAKE_CASE       :int		    =  10_24

      # set label information
      __SCREAMING_SNAKE_CASE       :Tuple		    =  1_50
      __SCREAMING_SNAKE_CASE       :Tuple		    =  '''huggingface/label-files'''
      __SCREAMING_SNAKE_CASE       :Any		    =  '''ade20k-id2label.json'''
      __SCREAMING_SNAKE_CASE       :int		    =  json.load(open(hf_hub_download(a_			,       a_			,       repo_type='''dataset'''							)			,       '''r'''							)							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {int(a_							): v for k, v in idalabel.items()}
      __SCREAMING_SNAKE_CASE       :List[str]		    =  {v: k for k, v in idalabel.items()}

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

      return config



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				Optional[Any]:
      __SCREAMING_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 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Any			,       a_					:		List[Any]							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dct.pop(a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  val



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		List[str]			,       a_					:		int							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  {
          '''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''',
      }
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_name_to_url[model_name]
      __SCREAMING_SNAKE_CASE       :Tuple		    =  torch.hub.load_state_dict_from_url(a_			,       map_location='''cpu'''							)['''state_dict''']

      __SCREAMING_SNAKE_CASE       :Any		    =  get_upernet_config(a_							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  UperNetForSemanticSegmentation(a_							)
      model.eval()

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

      # rename keys
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  create_rename_keys(a_							)
      for src, dest in rename_keys:
            rename_key(a_			,       a_			,       a_							)

      model.load_state_dict(a_							)

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

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  SegformerImageProcessor()
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  processor(a_			,       return_tensors='''pt'''							).pixel_values

      with torch.no_grad():
            __SCREAMING_SNAKE_CASE       :Any		    =  model(a_							)

      if model_name == "upernet-convnext-tiny":
            __SCREAMING_SNAKE_CASE       :str		    =  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":
            __SCREAMING_SNAKE_CASE       :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":
            __SCREAMING_SNAKE_CASE       :Tuple		    =  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":
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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":
            __SCREAMING_SNAKE_CASE       :Tuple		    =  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]			,       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__":
  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)






"""simple docstring"""
from itertools import product



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				list[int]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  sides_number
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  max_face_number * dice_number
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  [0] * (max_total + 1)

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  1
      __SCREAMING_SNAKE_CASE       :Tuple		    =  range(a_			,       max_face_number + 1							)
      for dice_numbers in product(a_			,       repeat=a_							):
            __SCREAMING_SNAKE_CASE       :Any		    =  sum(a_							)
            totals_frequencies[total] += 1

      return totals_frequencies



def 					__lowerCamelCase       (							)      ->				float:
      __SCREAMING_SNAKE_CASE       :Dict		    =  total_frequency_distribution(
          sides_number=4			,       dice_number=9							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  total_frequency_distribution(
          sides_number=6			,       dice_number=6							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  0
      __SCREAMING_SNAKE_CASE       :Any		    =  9
      __SCREAMING_SNAKE_CASE       :List[str]		    =  4 * 9
      __SCREAMING_SNAKE_CASE       :Dict		    =  6
      for peter_total in range(a_			,       max_peter_total + 1							):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total]							)

      __SCREAMING_SNAKE_CASE       :List[str]		    =  (4**9) * (6**6)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  peter_wins_count / total_games_number

      __SCREAMING_SNAKE_CASE       :str		    =  round(a_			,       ndigits=7							)

      return rounded_peter_win_probability


if __name__ == "__main__":
  print(f'{solution() = }')