





'''simple docstring'''






import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__							:  Optional[Any]    =logging.get_logger(__name__)

A__							:  Any    ={
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

A__							:  Union[str, Any]    ={
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				EfficientNetConfig()
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""hidden_dim"""]
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""width_coef"""]
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""depth_coef"""]
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""image_size"""]
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""dropout_rate"""]
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""dw_padding"""]

      _lowerCAmelCase					=				"""huggingface/label-files"""
      _lowerCAmelCase					=				"""imagenet-1k-id2label.json"""
      _lowerCAmelCase					=				10_00
      _lowerCAmelCase					=				json.load(open(hf_hub_download(A__				,      A__				,      repo_type="""dataset"""    )				,      """r"""    )    )
      _lowerCAmelCase					=				{int(A__    ): v for k, v in idalabel.items()}

      _lowerCAmelCase					=				idalabel
      _lowerCAmelCase					=				{v: k for k, v in idalabel.items()}
      return config
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(A__				,      stream=A__    ).raw    )
      return im
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""image_size"""]
      _lowerCAmelCase					=				EfficientNetImageProcessor(
          size={"""height""": size, """width""": size}				,      image_mean=[0.485, 0.456, 0.406]				,      image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]				,      do_center_crop=A__				,      )
      return preprocessor
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[v.split("""_"""    )[0].split("""block"""    )[1] for v in original_param_names if v.startswith("""block"""    )]
      _lowerCAmelCase					=				sorted(set(A__    )    )
      _lowerCAmelCase					=				len(A__    )
      _lowerCAmelCase					=				{b: str(A__    ) for b, i in zip(A__				,      range(A__    )    )}

      _lowerCAmelCase					=				[]
      rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")    )
      rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")    )
      rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")    )
      rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")    )
      rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")    )

      for b in block_names:
            _lowerCAmelCase					=				block_name_mapping[b]
            rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")    )
            rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")    )
            rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")    )
            rename_keys.append(
                (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")    )
            rename_keys.append(
                (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")    )
            rename_keys.append(
                (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")    )
            rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")    )
            rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")    )
            rename_keys.append(
                (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")    )
            rename_keys.append(
                (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")    )

            rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")    )
            rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")    )
            rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")    )
            rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")    )
            rename_keys.append(
                (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")    )
            rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")    )
            rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")    )
            rename_keys.append(
                (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")    )
            rename_keys.append(
                (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")    )

      rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""")    )
      rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""")    )
      rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""")    )
      rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""")    )
      rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""")    )

      _lowerCAmelCase					=				{}
      for item in rename_keys:
            if item[0] in original_param_names:
                  _lowerCAmelCase					=				"""efficientnet.""" + item[1]

      _lowerCAmelCase					=				"""classifier.weight"""
      _lowerCAmelCase					=				"""classifier.bias"""
      return key_mapping
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key, value in tf_params.items():
            if "normalization" in key:
                  continue

            _lowerCAmelCase					=				key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  _lowerCAmelCase					=				torch.from_numpy(A__    ).permute(3				,      2				,      0				,      1    )
            elif "depthwise_kernel" in key:
                  _lowerCAmelCase					=				torch.from_numpy(A__    ).permute(2				,      3				,      0				,      1    )
            elif "kernel" in key:
                  _lowerCAmelCase					=				torch.from_numpy(np.transpose(A__    )    )
            else:
                  _lowerCAmelCase					=				torch.from_numpy(A__    )

            # Replace HF parameters with original TF model parameters
            assert hf_params[hf_key].shape == new_hf_value.shape
            hf_params[hf_key].copy_(A__    )
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				model_classes[model_name](
          include_top=A__				,      weights="""imagenet"""				,      input_tensor=A__				,      input_shape=A__				,      pooling=A__				,      classes=10_00				,      classifier_activation="""softmax"""				,      )

      _lowerCAmelCase					=				original_model.trainable_variables
      _lowerCAmelCase					=				original_model.non_trainable_variables
      _lowerCAmelCase					=				{param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            _lowerCAmelCase					=				param.numpy()
      _lowerCAmelCase					=				list(tf_params.keys()    )

      # Load HuggingFace model
      _lowerCAmelCase					=				get_efficientnet_config(A__    )
      _lowerCAmelCase					=				EfficientNetForImageClassification(A__    ).eval()
      _lowerCAmelCase					=				hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print("""Converting parameters..."""    )
      _lowerCAmelCase					=				rename_keys(A__    )
      replace_params(A__				,      A__				,      A__    )

      # Initialize preprocessor and preprocess input image
      _lowerCAmelCase					=				convert_image_processor(A__    )
      _lowerCAmelCase					=				preprocessor(images=prepare_img()				,      return_tensors="""pt"""    )

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            _lowerCAmelCase					=				hf_model(**A__    )
      _lowerCAmelCase					=				outputs.logits.detach().numpy()

      # Original model inference
      _lowerCAmelCase					=				False
      _lowerCAmelCase					=				CONFIG_MAP[model_name]["""image_size"""]
      _lowerCAmelCase					=				prepare_img().resize((image_size, image_size)				,      resample=PIL.Image.NEAREST    )
      _lowerCAmelCase					=				image.img_to_array(A__    )
      _lowerCAmelCase					=				np.expand_dims(A__				,      axis=0    )
      _lowerCAmelCase					=				original_model.predict(A__    )

      # Check whether original and HF model outputs match  -> np.allclose
      assert np.allclose(A__				,      A__				,      atol=1e-3    ), "The predicted logits are not the same."
      print("""Model outputs match!"""    )

      if save_model:
            # Create folder to save model
            if not os.path.isdir(A__    ):
                  os.mkdir(A__    )
            # Save converted model and image processor
            hf_model.save_pretrained(A__    )
            preprocessor.save_pretrained(A__    )

      if push_to_hub:
            # Push model and image processor to hub
            print(f"Pushing converted {model_name} to the hub..."    )
            _lowerCAmelCase					=				f"efficientnet-{model_name}"
            preprocessor.push_to_hub(A__    )
            hf_model.push_to_hub(A__    )


if __name__ == "__main__":
      A__							:  int    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default='''b0''',
          type=str,
          help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''hf_model''',
          type=str,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
      parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

      A__							:  Dict    =parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










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__						: int					)     ->    List[str]:
   lowerCamelCase_						:   int		= 384
   if "tiny" in model_name:
      lowerCamelCase_						:   str		= [3, 3, 9, 3]
      lowerCamelCase_						:   List[str]		= [96, 192, 384, 768]
   if "small" in model_name:
      lowerCamelCase_						:   Any		= [3, 3, 27, 3]
      lowerCamelCase_						:   List[Any]		= [96, 192, 384, 768]
   if "base" in model_name:
      lowerCamelCase_						:   Optional[Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   int		= [128, 256, 512, 1024]
      lowerCamelCase_						:   int		= 512
   if "large" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Dict		= [192, 384, 768, 1536]
      lowerCamelCase_						:   Union[str, Any]		= 768
   if "xlarge" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Optional[int]		= [256, 512, 1024, 2048]
      lowerCamelCase_						:   List[str]		= 1024

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

   lowerCamelCase_						:   Dict		= ConvNextConfig(
       depths=A__						,						hidden_sizes=A__						,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   lowerCamelCase_						:   str		= UperNetConfig(
       backbone_config=A__						,						auxiliary_in_channels=A__						,						num_labels=A__						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[Any]:
   lowerCamelCase_						:   Tuple		= []

   # 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__						: Optional[Any]						,						A__						: List[Any]						,						A__						: int					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= dct.pop(A__					)
   lowerCamelCase_						:   int		= val


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: str						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   int		= {
       """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_						:   List[str]		= model_name_to_url[model_name]
   lowerCamelCase_						:   Dict		= torch.hub.load_state_dict_from_url(A__						,						map_location="""cpu"""					)["""state_dict"""]

   lowerCamelCase_						:   List[Any]		= get_upernet_config(A__					)
   lowerCamelCase_						:   Any		= UperNetForSemanticSegmentation(A__					)
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   Tuple		= state_dict.pop(A__					)
      if "bn" in key:
         lowerCamelCase_						:   Dict		= key.replace("""bn"""						,						"""batch_norm"""					)
      lowerCamelCase_						:   Tuple		= val

   # rename keys
   lowerCamelCase_						:   List[Any]		= create_rename_keys(A__					)
   for src, dest in rename_keys:
      rename_key(A__						,						A__						,						A__					)

   model.load_state_dict(A__					)

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

   lowerCamelCase_						:   List[str]		= SegformerImageProcessor()
   lowerCamelCase_						:   Union[str, Any]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   with torch.no_grad():
      lowerCamelCase_						:   List[str]		= model(A__					)

   if model_name == "upernet-convnext-tiny":
      lowerCamelCase_						:   Dict		= 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_						:   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":
      lowerCamelCase_						:   str		= 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_						:   List[str]		= 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_						:   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]						,						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__				:	Dict					       =					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__				:	List[Any]					       =					parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





