


"""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   snake_case						(				_a:     Optional[int]							)->		Optional[int]:
      '''simple docstring'''


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

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

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

      return config



def   snake_case						(				_a:     Optional[int]							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[]

      # 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   snake_case						(				_a:     int		,   _a:     Any		,   _a:     Any							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		dct.pop(_a							)
      lowerCamelCase__           =		val



def   snake_case						(				_a:     Union[str, Any]		,   _a:     Tuple		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		{
          '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__           =		model_name_to_url[model_name]
      lowerCamelCase__           =		torch.hub.load_state_dict_from_url(_a		,   map_location='cpu'							)['state_dict']

      lowerCamelCase__           =		get_upernet_config(_a							)
      lowerCamelCase__           =		UperNetForSemanticSegmentation(_a							)
      model.eval()

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

      # rename keys
      lowerCamelCase__           =		create_rename_keys(_a							)
      for src, dest in rename_keys:
            rename_key(_a		,   _a		,   _a							)

      model.load_state_dict(_a							)

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

      lowerCamelCase__           =		SegformerImageProcessor()
      lowerCamelCase__           =		processor(_a		,   return_tensors='pt'							).pixel_values

      with torch.no_grad():
            lowerCamelCase__           =		model(_a							)

      if model_name == "upernet-convnext-tiny":
            lowerCamelCase__           =		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__           =		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__           =		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__           =		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__           =		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__":
   _snake_case          =							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."
   )

   _snake_case          =							parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =							{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["BlenderbotTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "BlenderbotForCausalLM",
       "BlenderbotForConditionalGeneration",
       "BlenderbotModel",
       "BlenderbotPreTrainedModel",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "TFBlenderbotForConditionalGeneration",
       "TFBlenderbotModel",
       "TFBlenderbotPreTrainedModel",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "FlaxBlenderbotForConditionalGeneration",
       "FlaxBlenderbotModel",
       "FlaxBlenderbotPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_blenderbot import (
       BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlenderbotConfig,
       BlenderbotOnnxConfig,
   )
   from .tokenization_blenderbot import BlenderbotTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blenderbot import (
          BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlenderbotForCausalLM,
          BlenderbotForConditionalGeneration,
          BlenderbotModel,
          BlenderbotPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blenderbot import (
          TFBlenderbotForConditionalGeneration,
          TFBlenderbotModel,
          TFBlenderbotPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_blenderbot import (
          FlaxBlenderbotForConditionalGeneration,
          FlaxBlenderbotModel,
          FlaxBlenderbotPreTrainedModel,
      )

else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



