

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowercase_       		=  "\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowercase_       		=  "\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

lowercase_       		=  "\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,							_KWARGS_DESCRIPTION )
class  A						(							datasets.Metric ):







    """simple docstring"""





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





        '''simple docstring'''







        if version.parse(scb.__version__		) < version.parse('1.4.12'		):
            raise ImportWarning(
                'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                'You can install it with `pip install "sacrebleu>=1.4.12"`.'		)
        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='http://www.cs.umd.edu/~snover/tercom/',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Sequence(datasets.Value('string',id='sequence'		),id='references'		),
                }		),codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'],reference_urls=[
                'https://github.com/jhclark/tercom',
            ],)


    def 						snake_case__     (      self :       int,lowercase_ :       Any,lowercase_ :       Optional[Any],lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,)->    Optional[Any]:





        '''simple docstring'''







        A__          =   len(references[0]		)
        if any(len(lowercase_		) != references_per_prediction for refs in references		):
            raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
        A__          =   [[refs[i] for refs in references] for i in range(lowercase_		)]

        A__          =   TER(
            normalized=lowercase_,no_punct=lowercase_,asian_support=lowercase_,case_sensitive=lowercase_,)
        A__          =   sb_ter.corpus_score(lowercase_,lowercase_		)

        return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}




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)

