





'''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    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     384
      if "tiny" in model_name:
            _snake_case		:	Optional[int]					     =     [3, 3, 9, 3]
            _snake_case		:	Any					     =     [96, 192, 384, 768]
      if "small" in model_name:
            _snake_case		:	Tuple					     =     [3, 3, 27, 3]
            _snake_case		:	Optional[int]					     =     [96, 192, 384, 768]
      if "base" in model_name:
            _snake_case		:	str					     =     [3, 3, 27, 3]
            _snake_case		:	Any					     =     [128, 256, 512, 1_024]
            _snake_case		:	Optional[Any]					     =     512
      if "large" in model_name:
            _snake_case		:	Optional[int]					     =     [3, 3, 27, 3]
            _snake_case		:	List[str]					     =     [192, 384, 768, 1_536]
            _snake_case		:	Tuple					     =     768
      if "xlarge" in model_name:
            _snake_case		:	Optional[int]					     =     [3, 3, 27, 3]
            _snake_case		:	Union[str, Any]					     =     [256, 512, 1_024, 2_048]
            _snake_case		:	Optional[int]					     =     1_024

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

      _snake_case		:	List[str]					     =     ConvNextConfig(
          depths=lowerCAmelCase_			,      hidden_sizes=lowerCAmelCase_			,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
      _snake_case		:	str					     =     UperNetConfig(
          backbone_config=lowerCAmelCase_			,      auxiliary_in_channels=lowerCAmelCase_			,      num_labels=lowerCAmelCase_			,      idalabel=lowerCAmelCase_			,      labelaid=lowerCAmelCase_			,      )

      return config




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Any					     =     dct.pop(lowerCAmelCase_	)
      _snake_case		:	int					     =     val




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _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''',
      }
      _snake_case		:	Optional[Any]					     =     model_name_to_url[model_name]
      _snake_case		:	List[Any]					     =     torch.hub.load_state_dict_from_url(lowerCAmelCase_			,      map_location='''cpu'''	)['''state_dict''']

      _snake_case		:	Dict					     =     get_upernet_config(lowerCAmelCase_	)
      _snake_case		:	List[str]					     =     UperNetForSemanticSegmentation(lowerCAmelCase_	)
      model.eval()

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

      # rename keys
      _snake_case		:	Optional[int]					     =     create_rename_keys(lowerCAmelCase_	)
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      model.load_state_dict(lowerCAmelCase_	)

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

      _snake_case		:	Any					     =     SegformerImageProcessor()
      _snake_case		:	int					     =     processor(lowerCAmelCase_			,      return_tensors='''pt'''	).pixel_values

      with torch.no_grad():
            _snake_case		:	Tuple					     =     model(lowerCAmelCase_	)

      if model_name == "upernet-convnext-tiny":
            _snake_case		:	List[Any]					     =     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":
            _snake_case		:	Dict					     =     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":
            _snake_case		:	str					     =     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":
            _snake_case		:	Union[str, Any]					     =     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":
            _snake_case		:	List[Any]					     =     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]			,      lowerCAmelCase_			,      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(lowerCAmelCase_	)
            print(f'''Saving processor to {pytorch_dump_folder_path}'''	)
            processor.save_pretrained(lowerCAmelCase_	)

      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__":
   UpperCAmelCase					:		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.'
   )

   UpperCAmelCase					:		int		=							parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

import os

import numpy
import onnx




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     a.name
      _snake_case		:	List[Any]					     =     b.name

      _snake_case		:	Tuple					     =     ''''''
      _snake_case		:	Tuple					     =     ''''''

      _snake_case		:	Optional[Any]					     =     a == b

      _snake_case		:	List[Any]					     =     name_a
      _snake_case		:	str					     =     name_b

      return res




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for i, input_name in enumerate(node_proto.input	):
            if input_name == name:
                  node_proto.input.insert(lowerCAmelCase_			,      lowerCAmelCase_	)
                  node_proto.input.pop(i + 1	)

      if node_proto.op_type == "If":
            _graph_replace_input_with(node_proto.attribute[0].g			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            _graph_replace_input_with(node_proto.attribute[1].g			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      if node_proto.op_type == "Loop":
            _graph_replace_input_with(node_proto.attribute[0].g			,      lowerCAmelCase_			,      lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for n in graph_proto.node:
            _node_replace_input_with(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     list(model.graph.initializer	)
      _snake_case		:	List[str]					     =     list(model_without_ext.graph.initializer	)
      for i, ref_i in ind_to_replace:
            assert inits_with_data[i].name == inits[i].name
            assert inits_with_data[ref_i].name == inits[ref_i].name
            assert i > ref_i

            _snake_case		:	List[Any]					     =     inits[i].name
            _snake_case		:	List[str]					     =     inits[ref_i].name

            model_without_ext.graph.initializer.remove(inits[i]	)

            # for n in model.graph.node:
            _graph_replace_input_with(model_without_ext.graph			,      lowerCAmelCase_			,      lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     os.path.dirname(lowerCAmelCase_	)
      _snake_case		:	str					     =     os.path.basename(lowerCAmelCase_	)

      _snake_case		:	Tuple					     =     onnx.load(os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)	)

      _snake_case		:	Union[str, Any]					     =     list(model.graph.initializer	)

      _snake_case		:	Union[str, Any]					     =     set()
      _snake_case		:	Any					     =     {}
      _snake_case		:	str					     =     []

      _snake_case		:	Union[str, Any]					     =     0

      for i in range(len(lowerCAmelCase_	)	):
            if i in dup_set:
                  continue

            for j in range(i + 1			,      len(lowerCAmelCase_	)	):
                  if j in dup_set:
                        continue
                  if _is_equal_tensor_proto(inits[i]			,      inits[j]	):
                        dup_set.add(lowerCAmelCase_	)
                        dup_set.add(lowerCAmelCase_	)

                        _snake_case		:	List[Any]					     =     inits[j].data_type
                        _snake_case		:	Dict					     =     numpy.prod(inits[j].dims	)
                        if dtype == 1:
                              mem_size *= 4
                        elif dtype == 6:
                              mem_size *= 4
                        elif dtype == 7 or dtype == 11:
                              mem_size *= 8
                        else:
                              print('''unexpected data type: '''			,      lowerCAmelCase_	)
                        total_reduced_size += mem_size

                        _snake_case		:	Union[str, Any]					     =     inits[i].name
                        _snake_case		:	Any					     =     inits[j].name

                        if name_i in dup_map:
                              dup_map[name_i].append(lowerCAmelCase_	)
                        else:
                              _snake_case		:	Union[str, Any]					     =     [name_j]
                        ind_to_replace.append((j, i)	)

      print('''total reduced size: '''			,      total_reduced_size / 1_024 / 1_024 / 1_024			,      '''GB'''	)

      _snake_case		:	List[str]					     =     sorted(lowerCAmelCase_	)
      _remove_dup_initializers_from_model(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      _snake_case		:	List[str]					     =     '''optimized_''' + model_file_name
      _snake_case		:	List[Any]					     =     os.path.join(lowerCAmelCase_			,      lowerCAmelCase_	)
      onnx.save(lowerCAmelCase_			,      lowerCAmelCase_	)

      return new_model


