

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class  A						(							unittest.TestCase ):







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       str,lowercase_ :       Optional[int]=3,lowercase_ :       Optional[Any]=3_2,lowercase_ :       str=3,lowercase_ :       List[str]=1_0,lowercase_ :       List[Any]=[1_0, 2_0, 3_0, 4_0],lowercase_ :       Dict=[1, 1, 2, 1],lowercase_ :       List[str]=True,lowercase_ :       Tuple=True,lowercase_ :       Optional[Any]="relu",lowercase_ :       Tuple=3,lowercase_ :       Any=None,)->    Tuple:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   embeddings_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_act
        A__          =   num_labels
        A__          =   scope
        A__          =   len(lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A__          =   self.get_config()

        return config, pixel_values


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return RegNetConfig(
            num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,image_size=self.image_size,)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   FlaxRegNetModel(config=lowercase_		)
        A__          =   model(lowercase_		)

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),)


    def 						snake_case__     (      self :       Any,lowercase_ :       int,lowercase_ :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   FlaxRegNetForImageClassification(config=lowercase_		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       Tuple		)->    str:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_flax
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Tuple		)->    None:





        '''simple docstring'''







        A__          =   FlaxRegNetModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        return


    def 						snake_case__     (      self :       str		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @unittest.skip(reason='RegNet does not use inputs_embeds'		)
    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='RegNet does not support input and output embeddings'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            A__          =   inspect.signature(model.__call__		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A__          =   [*signature.parameters.keys()]

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       List[str],lowercase_ :       List[Any],lowercase_ :       Optional[int]		):
            A__          =   model_class(lowercase_		)

            A__          =   model(**self._prepare_for_class(lowercase_,lowercase_		)		)

            A__          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A__          =   self.model_tester.num_stages
            self.assertEqual(len(lowercase_		),expected_num_stages + 1		)

        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   True
            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            A__          =   True

            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                A__          =   self._prepare_for_class(lowercase_,lowercase_		)
                A__          =   model_class(lowercase_		)

                @jax.jit
                def model_jitted(lowercase_ :       int,**lowercase_ :       Dict		):
                    return model(pixel_values=lowercase_,**lowercase_		)

                with self.subTest('JIT Enabled'		):
                    A__          =   model_jitted(**lowercase_		).to_tuple()

                with self.subTest('JIT Disabled'		):
                    with jax.disable_jit():
                        A__          =   model_jitted(**lowercase_		).to_tuple()

                self.assertEqual(len(lowercase_		),len(lowercase_		)		)
                for jitted_output, output in zip(lowercase_,lowercase_		):
                    self.assertEqual(jitted_output.shape,output.shape		)




def 						_snake_case(    )							->      Dict:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


@require_flax
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       Dict		)->    Optional[int]:





        '''simple docstring'''







        return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        A__          =   FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='np'		)

        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   jnp.array([-0.4_180, -1.5_051, -3.4_836]		)

        self.assertTrue(jnp.allclose(outputs.logits[0, :3],lowercase_,atol=1E-4		)		)




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      int:





    '''simple docstring'''






    A__          =   384
    A__          =   7
    if "tiny" in model_name:
        A__          =   96
        A__          =   (2, 2, 6, 2)
        A__          =   (3, 6, 12, 24)
    elif "small" in model_name:
        A__          =   96
        A__          =   (2, 2, 18, 2)
        A__          =   (3, 6, 12, 24)
    elif "base" in model_name:
        A__          =   128
        A__          =   (2, 2, 18, 2)
        A__          =   (4, 8, 16, 32)
        A__          =   12
        A__          =   512
    elif "large" in model_name:
        A__          =   192
        A__          =   (2, 2, 18, 2)
        A__          =   (6, 12, 24, 48)
        A__          =   12
        A__          =   768

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

    A__          =   SwinConfig(
        embed_dim=SCREAMING_SNAKE_CASE__    ,			depths=SCREAMING_SNAKE_CASE__    ,			num_heads=SCREAMING_SNAKE_CASE__    ,			window_size=SCREAMING_SNAKE_CASE__    ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,			)
    A__          =   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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Dict:





    '''simple docstring'''






    A__          =   []

    # fmt: off
    # stem
    rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')      )
    rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')      )
    rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')      )
    rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')      )

        if i < 3:
            rename_keys.append((f'backbone.stages.{i}.downsample.reduction.weight', f'backbone.encoder.layers.{i}.downsample.reduction.weight')      )
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.weight', f'backbone.encoder.layers.{i}.downsample.norm.weight')      )
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.bias', f'backbone.encoder.layers.{i}.downsample.norm.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(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Any:





    '''simple docstring'''






    A__          =   [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
    for i in range(len(backbone_config.depths      )      ):
        A__          =   num_features[i]
        for j in range(backbone_config.depths[i]      ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            A__          =   state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'      )
            A__          =   state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'      )
            # next, add query, keys and values (in that order) to the state dict
            A__          =   in_proj_weight[:dim, :]
            A__          =   in_proj_bias[: dim]
            A__          =   in_proj_weight[
                dim : dim * 2, :
            ]
            A__          =   in_proj_bias[
                dim : dim * 2
            ]
            A__          =   in_proj_weight[
                -dim :, :
            ]
            A__          =   in_proj_bias[-dim :]
            # fmt: on
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Optional[Any]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			4    ,			in_channel // 4      )
    A__          =   x[:, [0, 2, 1, 3], :].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      List[str]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			in_channel // 4    ,			4      )
    A__          =   x[:, :, [0, 2, 1, 3]].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(4    ,			in_channel // 4      )
    A__          =   x[[0, 2, 1, 3], :].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      List[Any]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(in_channel // 4    ,			4      )
    A__          =   x[:, [0, 2, 1, 3]].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   {
        'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
        'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
        'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
        'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
    }
    A__          =   model_name_to_url[model_name]
    A__          =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'    ,			file_name=SCREAMING_SNAKE_CASE__      )[
        'state_dict'
    ]

    for name, param in state_dict.items():
        print(SCREAMING_SNAKE_CASE__    ,			param.shape      )

    A__          =   get_upernet_config(SCREAMING_SNAKE_CASE__      )
    A__          =   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__      )
    model.eval()

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

    # rename keys
    A__          =   create_rename_keys(SCREAMING_SNAKE_CASE__      )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__    ,			config.backbone_config      )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                A__          =   reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE__      )
            if "norm" in key:
                A__          =   reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE__      )

    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

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

    A__          =   SegformerImageProcessor()
    A__          =   processor(SCREAMING_SNAKE_CASE__    ,			return_tensors='pt'      ).pixel_values

    with torch.no_grad():
        A__          =   model(SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits

    print(logits.shape      )
    print('First values of logits:'    ,			logits[0, 0, :3, :3]      )
    # assert values
    if model_name == "upernet-swin-tiny":
        A__          =   torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]      )
    elif model_name == "upernet-swin-small":
        A__          =   torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]      )
    elif model_name == "upernet-swin-base":
        A__          =   torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]      )
    elif model_name == "upernet-swin-large":
        A__          =   torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]      )
    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__":
     lowercase_       		=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="upernet-swin-tiny",
         type=str,
         choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
         help="Name of the Swin + 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."
     )

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

