
"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE	       =      {
    """vocab_file""": {
        """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt""",
        """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt""",
        """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/vocab.txt""",
        """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/vocab.txt""",
        """bert-base-multilingual-uncased""": (
            """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"""
        ),
        """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt""",
        """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt""",
        """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt""",
        """bert-large-uncased-whole-word-masking""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"""
        ),
        """bert-large-cased-whole-word-masking""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"""
        ),
        """bert-large-uncased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
        ),
        """bert-large-cased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
        ),
        """bert-base-cased-finetuned-mrpc""": (
            """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"""
        ),
        """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt""",
        """bert-base-german-dbmdz-uncased""": (
            """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"""
        ),
        """TurkuNLP/bert-base-finnish-cased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"""
        ),
        """TurkuNLP/bert-base-finnish-uncased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"""
        ),
        """wietsedv/bert-base-dutch-cased""": (
            """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json""",
        """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json""",
        """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json""",
        """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json""",
        """bert-base-multilingual-uncased""": (
            """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"""
        ),
        """bert-base-multilingual-cased""": (
            """https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
        """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json""",
        """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json""",
        """bert-large-uncased-whole-word-masking""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"""
        ),
        """bert-large-cased-whole-word-masking""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"""
        ),
        """bert-large-uncased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
        ),
        """bert-large-cased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
        ),
        """bert-base-cased-finetuned-mrpc""": (
            """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"""
        ),
        """bert-base-german-dbmdz-cased""": (
            """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"""
        ),
        """bert-base-german-dbmdz-uncased""": (
            """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"""
        ),
        """TurkuNLP/bert-base-finnish-cased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"""
        ),
        """TurkuNLP/bert-base-finnish-uncased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"""
        ),
        """wietsedv/bert-base-dutch-cased""": (
            """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"""
        ),
    },
}

_SCREAMING_SNAKE_CASE	       =      {
    """bert-base-uncased""": 512,
    """bert-large-uncased""": 512,
    """bert-base-cased""": 512,
    """bert-large-cased""": 512,
    """bert-base-multilingual-uncased""": 512,
    """bert-base-multilingual-cased""": 512,
    """bert-base-chinese""": 512,
    """bert-base-german-cased""": 512,
    """bert-large-uncased-whole-word-masking""": 512,
    """bert-large-cased-whole-word-masking""": 512,
    """bert-large-uncased-whole-word-masking-finetuned-squad""": 512,
    """bert-large-cased-whole-word-masking-finetuned-squad""": 512,
    """bert-base-cased-finetuned-mrpc""": 512,
    """bert-base-german-dbmdz-cased""": 512,
    """bert-base-german-dbmdz-uncased""": 512,
    """TurkuNLP/bert-base-finnish-cased-v1""": 512,
    """TurkuNLP/bert-base-finnish-uncased-v1""": 512,
    """wietsedv/bert-base-dutch-cased""": 512,
}

_SCREAMING_SNAKE_CASE	       =      {
    """bert-base-uncased""": {"""do_lower_case""": True},
    """bert-large-uncased""": {"""do_lower_case""": True},
    """bert-base-cased""": {"""do_lower_case""": False},
    """bert-large-cased""": {"""do_lower_case""": False},
    """bert-base-multilingual-uncased""": {"""do_lower_case""": True},
    """bert-base-multilingual-cased""": {"""do_lower_case""": False},
    """bert-base-chinese""": {"""do_lower_case""": False},
    """bert-base-german-cased""": {"""do_lower_case""": False},
    """bert-large-uncased-whole-word-masking""": {"""do_lower_case""": True},
    """bert-large-cased-whole-word-masking""": {"""do_lower_case""": False},
    """bert-large-uncased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": True},
    """bert-large-cased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": False},
    """bert-base-cased-finetuned-mrpc""": {"""do_lower_case""": False},
    """bert-base-german-dbmdz-cased""": {"""do_lower_case""": False},
    """bert-base-german-dbmdz-uncased""": {"""do_lower_case""": True},
    """TurkuNLP/bert-base-finnish-cased-v1""": {"""do_lower_case""": False},
    """TurkuNLP/bert-base-finnish-uncased-v1""": {"""do_lower_case""": True},
    """wietsedv/bert-base-dutch-cased""": {"""do_lower_case""": False},
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   PRETRAINED_INIT_CONFIGURATION
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					Dict     							=   BertTokenizer





					def __init__(				self   :							Tuple							,						snake_case_   :							Dict=None							,						snake_case_   :							Optional[int]=None							,						snake_case_   :							int=True							,						snake_case_   :							Dict="[UNK]"							,						snake_case_   :							List[Any]="[SEP]"							,						snake_case_   :							Union[str, Any]="[PAD]"							,						snake_case_   :							Tuple="[CLS]"							,						snake_case_   :							Any="[MASK]"							,						snake_case_   :							List[Any]=True							,						snake_case_   :							Optional[int]=None							,						**snake_case_   :							Optional[Any]							,						):
												super().__init__(
												    snake_case_							,						tokenizer_file=snake_case_							,						do_lower_case=snake_case_							,						unk_token=snake_case_							,						sep_token=snake_case_							,						pad_token=snake_case_							,						cls_token=snake_case_							,						mask_token=snake_case_							,						tokenize_chinese_chars=snake_case_							,						strip_accents=snake_case_							,						**snake_case_							,						)

												__snake_case     			=       json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
												if (
												    normalizer_state.get("lowercase"							,						snake_case_    ) != do_lower_case
												    or normalizer_state.get("strip_accents"							,						snake_case_    ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"							,						snake_case_    ) != tokenize_chinese_chars
												):
																			__snake_case     			=       getattr(snake_case_							,						normalizer_state.pop("type"    )    )
																			__snake_case     			=       do_lower_case
																			__snake_case     			=       strip_accents
																			__snake_case     			=       tokenize_chinese_chars
																			__snake_case     			=       normalizer_class(**snake_case_    )

												__snake_case     			=       do_lower_case





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]=None    ):
												__snake_case     			=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None    ):
												__snake_case     			=       [self.sep_token_id]
												__snake_case     			=       [self.cls_token_id]
												if token_ids_a is None:
																			return len(cls + token_ids_a + sep    ) * [0]
												return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
												__snake_case     			=       self._tokenizer.model.save(snake_case_							,						name=snake_case_    )
												return tuple(snake_case_    )


"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       3_84
							if "tiny" in model_name:
														__snake_case     			=       [3, 3, 9, 3]
														__snake_case     			=       [96, 1_92, 3_84, 7_68]
							if "small" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [96, 1_92, 3_84, 7_68]
							if "base" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [1_28, 2_56, 5_12, 10_24]
														__snake_case     			=       5_12
							if "large" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [1_92, 3_84, 7_68, 15_36]
														__snake_case     			=       7_68
							if "xlarge" in model_name:
														__snake_case     			=       [3, 3, 27, 3]
														__snake_case     			=       [2_56, 5_12, 10_24, 20_48]
														__snake_case     			=       10_24

							# set label information
							__snake_case     			=       1_50
							__snake_case     			=       "huggingface/label-files"
							__snake_case     			=       "ade20k-id2label.json"
							__snake_case     			=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    repo_type="dataset" )							,    "r" ) )
							__snake_case     			=       {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
							__snake_case     			=       {v: k for k, v in idalabel.items()}

							__snake_case     			=       ConvNextConfig(
							    depths=SCREAMING_SNAKE_CASE							,    hidden_sizes=SCREAMING_SNAKE_CASE							,    out_features=["stage1", "stage2", "stage3", "stage4"] )
							__snake_case     			=       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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							# 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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       dct.pop(SCREAMING_SNAKE_CASE )
							__snake_case     			=       val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {
							    "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     			=       model_name_to_url[model_name]
							__snake_case     			=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							,    map_location="cpu" )["state_dict"]

							__snake_case     			=       get_upernet_config(SCREAMING_SNAKE_CASE )
							__snake_case     			=       UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE )
							model.eval()

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

							# rename keys
							__snake_case     			=       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
							__snake_case     			=       "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
							__snake_case     			=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,    stream=SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )

							__snake_case     			=       SegformerImageProcessor()
							__snake_case     			=       processor(SCREAMING_SNAKE_CASE							,    return_tensors="pt" ).pixel_values

							with torch.no_grad():
														__snake_case     			=       model(SCREAMING_SNAKE_CASE )

							if model_name == "upernet-convnext-tiny":
														__snake_case     			=       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     			=       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     			=       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     			=       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     			=       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]							,    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__":
		_SCREAMING_SNAKE_CASE	       =      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."""
		)

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