





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_       ):
      _a							:  List[str]				= 384
      if "tiny" in model_name:
            _a							:  Optional[Any]				= [3, 3, 9, 3]
            _a							:  Dict				= [96, 192, 384, 768]
      if "small" in model_name:
            _a							:  Tuple				= [3, 3, 27, 3]
            _a							:  str				= [96, 192, 384, 768]
      if "base" in model_name:
            _a							:  Tuple				= [3, 3, 27, 3]
            _a							:  Dict				= [128, 256, 512, 1024]
            _a							:  str				= 512
      if "large" in model_name:
            _a							:  str				= [3, 3, 27, 3]
            _a							:  List[Any]				= [192, 384, 768, 1536]
            _a							:  List[Any]				= 768
      if "xlarge" in model_name:
            _a							:  Optional[Any]				= [3, 3, 27, 3]
            _a							:  Dict				= [256, 512, 1024, 2048]
            _a							:  List[Any]				= 1024

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

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

      return config




def 						lowerCamelCase_	(		UpperCamelCase_       ):
      _a							:  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_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_       ):
      _a							:  Dict				= dct.pop(__SCREAMING_SNAKE_CASE       )
      _a							:  int				= val




def 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_       ):
      _a							:  List[str]				= {
          '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',
      }
      _a							:  Optional[int]				= model_name_to_url[model_name]
      _a							:  Optional[Any]				= torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE			,					map_location='''cpu'''       )['state_dict']

      _a							:  str				= get_upernet_config(__SCREAMING_SNAKE_CASE       )
      _a							:  Optional[int]				= UperNetForSemanticSegmentation(__SCREAMING_SNAKE_CASE       )
      model.eval()

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

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

      model.load_state_dict(__SCREAMING_SNAKE_CASE       )

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

      _a							:  Tuple				= SegformerImageProcessor()
      _a							:  Dict				= processor(__SCREAMING_SNAKE_CASE			,					return_tensors='''pt'''       ).pixel_values

      with torch.no_grad():
            _a							:  Union[str, Any]				= model(__SCREAMING_SNAKE_CASE       )

      if model_name == "upernet-convnext-tiny":
            _a							:  Optional[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":
            _a							:  Optional[Any]				= 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":
            _a							:  Any				= 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":
            _a							:  List[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":
            _a							:  Optional[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]			,					__SCREAMING_SNAKE_CASE			,					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(__SCREAMING_SNAKE_CASE       )
            print(f"""Saving processor to {pytorch_dump_folder_path}"""       )
            processor.save_pretrained(__SCREAMING_SNAKE_CASE       )

      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					:     Any	      =	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					:     List[str]	      =	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_torch_available,
)


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

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