
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

UpperCAmelCase__	:  str      =   logging.get_logger(__name__)

UpperCAmelCase__	:  int      =   {'vocab_file': 'tokenizer.model'}

UpperCAmelCase__	:  int      =   {
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
UpperCAmelCase__	:  str      =   {
    'hf-internal-testing/llama-tokenizer': 2048,
}
UpperCAmelCase__	:  Any      =   '▁'

UpperCAmelCase__    ,UpperCAmelCase__	:  int      =   '[INST]', '[/INST]'
UpperCAmelCase__    ,UpperCAmelCase__	:  Dict      =   '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
UpperCAmelCase__	:  Tuple      =   'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''
   __UpperCamelCase	:					List[str]				       =					VOCAB_FILES_NAMES
   __UpperCamelCase	:					Union[str, Any]				       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase	:					Optional[Any]				       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase	:					Optional[int]				       =					['''input_ids''', '''attention_mask''']


   def __init__(      self  :							List[Any]    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							int="<unk>"    ,    lowerCAmelCase_  :							str="<s>"    ,    lowerCAmelCase_  :							Optional[Any]="</s>"    ,    lowerCAmelCase_  :							int=None    ,    lowerCAmelCase_  :							Optional[Dict[str, Any]] = None    ,    lowerCAmelCase_  :							Tuple=True    ,    lowerCAmelCase_  :							str=False    ,    lowerCAmelCase_  :							Union[str, Any]=False    ,    lowerCAmelCase_  :							Tuple=True    ,    **lowerCAmelCase_  :							Any    ,    ):

      """simple docstring"""
      _A:						str	    = {} if sp_model_kwargs is None else sp_model_kwargs
      _A:						Union[str, Any]	    = AddedToken(lowerCAmelCase_    ,    lstrip=lowerCAmelCase_    ,    rstrip=lowerCAmelCase_  ) if isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ) else bos_token
      _A:						Tuple	    = AddedToken(lowerCAmelCase_    ,    lstrip=lowerCAmelCase_    ,    rstrip=lowerCAmelCase_  ) if isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ) else eos_token
      _A:						List[Any]	    = AddedToken(lowerCAmelCase_    ,    lstrip=lowerCAmelCase_    ,    rstrip=lowerCAmelCase_  ) if isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ) else unk_token
      _A:						int	    = AddedToken(lowerCAmelCase_    ,    lstrip=lowerCAmelCase_    ,    rstrip=lowerCAmelCase_  ) if isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ) else pad_token
      super().__init__(
          bos_token=lowerCAmelCase_    ,    eos_token=lowerCAmelCase_    ,    unk_token=lowerCAmelCase_    ,    pad_token=lowerCAmelCase_    ,    add_bos_token=lowerCAmelCase_    ,    add_eos_token=lowerCAmelCase_    ,    sp_model_kwargs=self.sp_model_kwargs    ,    clean_up_tokenization_spaces=lowerCAmelCase_    ,    legacy=lowerCAmelCase_    ,    **lowerCAmelCase_    ,    )
      if legacy:
         logger.warning_once(
             F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
             ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''  )
      _A:						Tuple	    = legacy
      _A:						int	    = vocab_file
      _A:						Optional[int]	    = add_bos_token
      _A:						Optional[Any]	    = add_eos_token
      _A:						List[Any]	    = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(lowerCAmelCase_  )


   def __getstate__(      self  :							str  ):

      """simple docstring"""
      _A:						Optional[Any]	    = self.__dict__.copy()
      _A:						str	    = None
      _A:						Dict	    = self.sp_model.serialized_model_proto()
      return state


   def __setstate__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Tuple  ):

      """simple docstring"""
      _A:						Any	    = d
      _A:						Union[str, Any]	    = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )


   @property
   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      return self.sp_model.get_piece_size()


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						Tuple	    = {self.convert_ids_to_tokens(lowerCAmelCase_  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Tuple    ,    **lowerCAmelCase_  :							Tuple  ):

      """simple docstring"""
      # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
      # the beginning of the text
      if not self.legacy:
         _A:						Optional[int]	    = SPIECE_UNDERLINE + text.replace(lowerCAmelCase_    ,    ''' '''  )
      return super().tokenize(lowerCAmelCase_    ,    **lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]  ):

      """simple docstring"""
      if not self.legacy:
         _A:						str	    = text.startswith(lowerCAmelCase_  )
         if is_first:
            _A:						Any	    = text[1:]

      _A:						str	    = self.sp_model.encode(lowerCAmelCase_    ,    out_type=lowerCAmelCase_  )

      if not self.legacy and not is_first and not text.startswith(''' '''  ) and tokens[0].startswith(lowerCAmelCase_  ):
         _A:						List[Any]	    = ([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
      return tokens


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Tuple  ):

      """simple docstring"""
      return self.sp_model.piece_to_id(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = self.sp_model.IdToPiece(lowerCAmelCase_  )
      return token


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      _A:						Any	    = []
      _A:						Optional[Any]	    = ''''''
      _A:						List[Any]	    = False
      for i, token in enumerate(lowerCAmelCase_  ):
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            if not prev_is_special and i != 0:
               out_string += " "
            out_string += self.sp_model.decode(lowerCAmelCase_  ) + token
            _A:						str	    = True
            _A:						Optional[int]	    = []
         else:
            current_sub_tokens.append(lowerCAmelCase_  )
            _A:						Optional[int]	    = False
      out_string += self.sp_model.decode(lowerCAmelCase_  )
      return out_string


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							Tuple    ,    lowerCAmelCase_  :							Optional[str] = None  ):

      """simple docstring"""
      if not os.path.isdir(lowerCAmelCase_  ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
         return
      _A:						Optional[Any]	    = os.path.join(
          lowerCAmelCase_    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCAmelCase_  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file    ,    lowerCAmelCase_  )
      elif not os.path.isfile(self.vocab_file  ):
         with open(lowerCAmelCase_    ,    '''wb'''  ) as fi:
            _A:						Any	    = self.sp_model.serialized_model_proto()
            fi.write(lowerCAmelCase_  )

      return (out_vocab_file,)


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[Any]=None  ):

      """simple docstring"""
      _A:						List[Any]	    = [self.bos_token_id] if self.add_bos_token else []
      _A:						Tuple	    = [self.eos_token_id] if self.add_eos_token else []

      _A:						Dict	    = bos_token_id + token_ids_a + eos_token_id

      if token_ids_a is not None:
         _A:						int	    = output + bos_token_id + token_ids_a + eos_token_id

      return output


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							List[int]    ,    lowerCAmelCase_  :							Optional[List[int]] = None    ,    lowerCAmelCase_  :							bool = False  ):

      """simple docstring"""
      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCAmelCase_    ,    token_ids_a=lowerCAmelCase_    ,    already_has_special_tokens=lowerCAmelCase_  )

      _A:						Union[str, Any]	    = [1] if self.add_bos_token else []
      _A:						Optional[Any]	    = [1] if self.add_eos_token else []

      if token_ids_a is None:
         return bos_token_id + ([0] * len(lowerCAmelCase_  )) + eos_token_id
      return (
          bos_token_id
          + ([0] * len(lowerCAmelCase_  ))
          + eos_token_id
          + bos_token_id
          + ([0] * len(lowerCAmelCase_  ))
          + eos_token_id
      )


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							List[int]    ,    lowerCAmelCase_  :							Optional[List[int]] = None  ):

      """simple docstring"""
      _A:						List[Any]	    = [self.bos_token_id] if self.add_bos_token else []
      _A:						Any	    = [self.eos_token_id] if self.add_eos_token else []

      _A:						Union[str, Any]	    = [0] * len(bos_token_id + token_ids_a + eos_token_id  )

      if token_ids_a is not None:
         output += [1] * len(bos_token_id + token_ids_a + eos_token_id  )

      return output


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							"Conversation"  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = list(conversation.iter_texts()  )
      if not all([is_user for is_user, msg in dialogue[::2]]  ) or not all(
          [not is_user for is_user, msg in dialogue[1::2]]  ):
         raise ValueError(
             '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''  )

      _A:						List[int]	    = []
      if len(conversation.past_user_inputs  ) > 0:
         if not conversation.past_user_inputs[0].startswith(lowerCAmelCase_  ) or E_SYS not in conversation.past_user_inputs[0]:
            _A:						List[Any]	    = (
                B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
            )
      elif not dialogue[0][1].startswith(lowerCAmelCase_  ) or E_SYS not in dialogue[0][1]:
         _A:						List[Any]	    = (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

      dialog_tokens += sum(
          [
              [self.bos_token_id]
              + self.encode(
                  F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """    ,    add_special_tokens=lowerCAmelCase_  )
              + [self.eos_token_id]
              for prompt, answer in zip(dialogue[::2]    ,    dialogue[1::2]  )
          ]    ,    []    ,    )
      if not (dialogue[-1][0]):
         raise ValueError(F"""Last message must be from user, got {dialogue[-1]['role']}"""  )
      dialog_tokens += [self.bos_token_id] + self.encode(
          F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""    ,    add_special_tokens=lowerCAmelCase_  )
      return dialog_tokens





import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					List[nn.Module]				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					list				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							Tensor    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						Optional[Any]	    = len(list(m.modules()  )  ) == 1 or isinstance(lowerCAmelCase_    ,    nn.Convad  ) or isinstance(lowerCAmelCase_    ,    nn.BatchNormad  )
      if has_not_submodules:
         if not isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ):
            self.traced.append(lowerCAmelCase_  )


   def __call__(      self  :							str    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook  )  )
      self.module(lowerCAmelCase_  )
      [x.remove() for x in self.handles]
      return self


   @property
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda lowerCAmelCase_  : len(list(x.state_dict().keys()  )  ) > 0    ,    self.traced  )  )






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					int				       =					0
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						int	    = Tracker(self.dest  )(lowerCAmelCase_  ).parametrized
      _A:						Union[str, Any]	    = Tracker(self.src  )(lowerCAmelCase_  ).parametrized

      _A:						Any	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.src_skip    ,    lowerCAmelCase_  )  )
      _A:						int	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.dest_skip    ,    lowerCAmelCase_  )  )

      if len(lowerCAmelCase_  ) != len(lowerCAmelCase_  ):
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(lowerCAmelCase_  )} operations while"""
             F""" destination module has {len(lowerCAmelCase_  )}."""  )

      for dest_m, src_m in zip(lowerCAmelCase_    ,    lowerCAmelCase_  ):
         dest_m.load_state_dict(src_m.state_dict()  )
         if self.verbose == 1:
            print(F"""Transfered from={src_m} to={dest_m}"""  )






def 						lowerCamelCase__							(      a	,	a   )   ->     nn.Module:
   # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
   _A:						Tuple	    = from_model.state_dict()
   _A:						Dict	    = our_model.state_dict()
   _A:						str	    = our_model.config
   _A:						List[str]	    = []
   for stage_idx in range(len(config.hidden_sizes   )   ):
      for block_id in range(config.depths[stage_idx]   ):
         _A:						Tuple	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
         _A:						List[str]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

         all_keys.append((from_key, to_key)   )
         _A:						str	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
         _A:						Union[str, Any]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

         all_keys.append((from_key, to_key)   )

   for from_key, to_key in all_keys:
      _A:						Union[str, Any]	    = from_state_dict.pop(a   )

   our_model.load_state_dict(a   )
   return our_model






def 						lowerCamelCase__							(      a	,	a	,	a	,	a	,	a	,	a = True	,	)   ->     Dict:
   print(f"""Downloading weights for {name}..."""   )
   _A:						Dict	    = cached_download(a   )
   print(f"""Converting {name}..."""   )
   _A:						int	    = torch.load(a   )['''state_dict''']
   from_model.load_state_dict(a   )
   from_model.eval()
   with torch.no_grad():
      _A:						Any	    = VanForImageClassification(a   ).eval()
      _A:						Tuple	    = ModuleTransfer(src=a	,	dest=a   )
      _A:						List[str]	    = torch.randn((1, 3, 2_24, 2_24)   )
      module_transfer(a   )
      _A:						Tuple	    = copy_parameters(a	,	a   )

   if not torch.allclose(from_model(a   )	,	our_model(a   ).logits   ):
      raise ValueError('''The model logits don\'t match the original one.'''   )

   _A:						str	    = name
   print(a   )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=a	,	)

      # we can use the convnext one
      _A:						Tuple	    = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=a	,	)

      print(f"""Pushed {checkpoint_name}"""   )






def 						lowerCamelCase__							(      a	,	a = None	,	a = True   )   ->     int:
   _A:						str	    = '''imagenet-1k-id2label.json'''
   _A:						Optional[int]	    = 10_00

   _A:						str	    = '''huggingface/label-files'''
   _A:						List[str]	    = num_labels
   _A:						Union[str, Any]	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
   _A:						List[str]	    = {int(a   ): v for k, v in idalabel.items()}

   _A:						Any	    = idalabel
   _A:						Union[str, Any]	    = {v: k for k, v in idalabel.items()}

   _A:						Optional[int]	    = partial(a	,	num_labels=a	,	idalabel=a	,	labelaid=a   )

   _A:						Optional[int]	    = {
       '''van-tiny''': ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-small''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-base''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-large''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
   }

   _A:						str	    = {
       '''van-tiny''': van_tiny,
       '''van-small''': van_small,
       '''van-base''': van_base,
       '''van-large''': van_large,
   }

   _A:						Union[str, Any]	    = {
       '''van-tiny''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
       ),
       '''van-small''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
       ),
       '''van-base''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
       ),
       '''van-large''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
       ),
   }

   if model_name:
      convert_weight_and_push(
          a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             a	,	a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)


if __name__ == "__main__":
   UpperCAmelCase__	:  Tuple      =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model-name',
       default=None,
       type=str,
       help=(
           'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
           ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=Path,
       required=True,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument(
       '--van_dir',
       required=True,
       type=Path,
       help=(
           'A path to VAN\'s original implementation directory. You can download from here:'
           ' https://github.com/Visual-Attention-Network/VAN-Classification'
       ),
   )
   parser.add_argument(
       '--push_to_hub',
       default=True,
       type=bool,
       required=False,
       help='If True, push model and image processor to the hub.',
   )

   UpperCAmelCase__	:  Dict      =   parser.parse_args()
   UpperCAmelCase__	:  Path      =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   UpperCAmelCase__	:  Optional[int]      =   args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



