
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


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

UpperCAmelCase__	:  List[Any]     =   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCAmelCase__	:  str     =   {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

UpperCAmelCase__	:  List[Any]     =   {
    'openai-gpt': 512,
}






def 						lowerCamelCase__							(      a   )   ->     List[str]:
   _A:						List[str]	    = text.strip()
   if not text:
      return []
   _A:						List[Any]	    = text.split()
   return tokens






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							Any    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							Optional[int]=None    ,    lowerCAmelCase_  :							Optional[int]=True    ,    lowerCAmelCase_  :							Optional[int]=None    ,    lowerCAmelCase_  :							Any=True    ,    ):

      """simple docstring"""






      if never_split is None:
         _A:						Tuple	    = []
      _A:						str	    = do_lower_case
      _A:						Optional[Any]	    = set(lowerCAmelCase_  )
      _A:						Dict	    = tokenize_chinese_chars
      _A:						str	    = strip_accents
      _A:						int	    = do_split_on_punc


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

      """simple docstring"""
      _A:						int	    = self.never_split.union(set(lowerCAmelCase_  )  ) if never_split else self.never_split
      _A:						Tuple	    = self._clean_text(lowerCAmelCase_  )

      # This was added on November 1st, 2018 for the multilingual and Chinese
      # models. This is also applied to the English models now, but it doesn't
      # matter since the English models were not trained on any Chinese data
      # and generally don't have any Chinese data in them (there are Chinese
      # characters in the vocabulary because Wikipedia does have some Chinese
      # words in the English Wikipedia.).
      if self.tokenize_chinese_chars:
         _A:						int	    = self._tokenize_chinese_chars(lowerCAmelCase_  )
      # prevents treating the same character with different unicode codepoints as different characters
      _A:						List[Any]	    = unicodedata.normalize('''NFC'''    ,    lowerCAmelCase_  )
      _A:						Tuple	    = whitespace_tokenize(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = []
      for token in orig_tokens:
         if token not in never_split:
            if self.do_lower_case:
               _A:						Optional[int]	    = token.lower()
               if self.strip_accents is not False:
                  _A:						Optional[int]	    = self._run_strip_accents(lowerCAmelCase_  )
            elif self.strip_accents:
               _A:						List[str]	    = self._run_strip_accents(lowerCAmelCase_  )
         split_tokens.extend(self._run_split_on_punc(lowerCAmelCase_    ,    lowerCAmelCase_  )  )

      _A:						Optional[Any]	    = whitespace_tokenize(''' '''.join(lowerCAmelCase_  )  )
      return output_tokens


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

      """simple docstring"""
      _A:						Tuple	    = unicodedata.normalize('''NFD'''    ,    lowerCAmelCase_  )
      _A:						List[Any]	    = []
      for char in text:
         _A:						Tuple	    = unicodedata.category(lowerCAmelCase_  )
         if cat == "Mn":
            continue
         output.append(lowerCAmelCase_  )
      return "".join(lowerCAmelCase_  )


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

      """simple docstring"""






      if not self.do_split_on_punc or (never_split is not None and text in never_split):
         return [text]
      _A:						Union[str, Any]	    = list(lowerCAmelCase_  )
      _A:						Optional[Any]	    = 0
      _A:						Optional[int]	    = True
      _A:						Any	    = []
      while i < len(lowerCAmelCase_  ):
         _A:						str	    = chars[i]
         if _is_punctuation(lowerCAmelCase_  ):
            output.append([char]  )
            _A:						Any	    = True
         else:
            if start_new_word:
               output.append([]  )
            _A:						Dict	    = False
            output[-1].append(lowerCAmelCase_  )
         i += 1

      return ["".join(lowerCAmelCase_  ) for x in output]


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

      """simple docstring"""
      _A:						Optional[int]	    = []
      for char in text:
         _A:						Tuple	    = ord(lowerCAmelCase_  )
         if self._is_chinese_char(lowerCAmelCase_  ):
            output.append(''' '''  )
            output.append(lowerCAmelCase_  )
            output.append(''' '''  )
         else:
            output.append(lowerCAmelCase_  )
      return "".join(lowerCAmelCase_  )


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

      """simple docstring"""






      if (
          (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
          or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
          or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
          or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
          or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
          or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
          or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
          or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
      ):  #
         return True

      return False


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

      """simple docstring"""
      _A:						Tuple	    = []
      for char in text:
         _A:						Union[str, Any]	    = ord(lowerCAmelCase_  )
         if cp == 0 or cp == 0xF_F_F_D or _is_control(lowerCAmelCase_  ):
            continue
         if _is_whitespace(lowerCAmelCase_  ):
            output.append(''' '''  )
         else:
            output.append(lowerCAmelCase_  )
      return "".join(lowerCAmelCase_  )






def 						lowerCamelCase__							(      a   )   ->     Dict:
   _A:						int	    = set()
   _A:						Dict	    = word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      _A:						List[str]	    = char
   return pairs






def 						lowerCamelCase__							(      a   )   ->     Union[str, Any]:
   _A:						List[Any]	    = text.replace('''—'''	,	'''-'''   )
   _A:						Optional[Any]	    = text.replace('''–'''	,	'''-'''   )
   _A:						List[Any]	    = text.replace('''―'''	,	'''-'''   )
   _A:						Any	    = text.replace('''…'''	,	'''...'''   )
   _A:						str	    = text.replace('''´'''	,	'''\''''   )
   _A:						str	    = re.sub(R'''(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''	,	R''' \1 '''	,	a   )
   _A:						Union[str, Any]	    = re.sub(R'''\s*\n\s*'''	,	''' \n '''	,	a   )
   _A:						Any	    = re.sub(R'''[^\S\n]+'''	,	''' '''	,	a   )
   return text.strip()






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





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


   def __init__(      self  :							Tuple    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Tuple    ,    lowerCAmelCase_  :							Any="<unk>"    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      super().__init__(unk_token=lowerCAmelCase_    ,    **lowerCAmelCase_  )

      try:
         import ftfy
         from spacy.lang.en import English

         _A:						List[Any]	    = English()
         _A:						Optional[int]	    = _nlp.tokenizer
         _A:						Tuple	    = ftfy.fix_text
      except ImportError:
         logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''  )
         _A:						Dict	    = BasicTokenizer(do_lower_case=lowerCAmelCase_  )
         _A:						str	    = None

      with open(lowerCAmelCase_    ,    encoding='''utf-8'''  ) as vocab_handle:
         _A:						str	    = json.load(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = {v: k for k, v in self.encoder.items()}
      with open(lowerCAmelCase_    ,    encoding='''utf-8'''  ) as merges_handle:
         _A:						Dict	    = merges_handle.read().split('''\n'''  )[1:-1]
      _A:						Dict	    = [tuple(merge.split()  ) for merge in merges]
      _A:						str	    = dict(zip(lowerCAmelCase_    ,    range(len(lowerCAmelCase_  )  )  )  )
      _A:						List[Any]	    = {}


   @property
   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      return True


   @property
   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      return len(self.encoder  )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      return dict(self.encoder    ,    **self.added_tokens_encoder  )


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

      """simple docstring"""
      _A:						List[str]	    = tuple(token[:-1]  ) + (token[-1] + "</w>",)
      if token in self.cache:
         return self.cache[token]
      _A:						Any	    = get_pairs(lowerCAmelCase_  )

      if not pairs:
         return token + "</w>"

      while True:
         _A:						List[Any]	    = min(lowerCAmelCase_    ,    key=lambda lowerCAmelCase_  : self.bpe_ranks.get(lowerCAmelCase_    ,    float('''inf'''  )  )  )
         if bigram not in self.bpe_ranks:
            break
         _A:						List[str]	    = bigram
         _A:						Union[str, Any]	    = []
         _A:						Optional[int]	    = 0
         while i < len(lowerCAmelCase_  ):
            try:
               _A:						List[Any]	    = word.index(lowerCAmelCase_    ,    lowerCAmelCase_  )
            except ValueError:
               new_word.extend(word[i:]  )
               break
            else:
               new_word.extend(word[i:j]  )
               _A:						Any	    = j

            if word[i] == first and i < len(lowerCAmelCase_  ) - 1 and word[i + 1] == second:
               new_word.append(first + second  )
               i += 2
            else:
               new_word.append(word[i]  )
               i += 1
         _A:						List[Any]	    = tuple(lowerCAmelCase_  )
         _A:						Optional[Any]	    = new_word
         if len(lowerCAmelCase_  ) == 1:
            break
         else:
            _A:						Optional[Any]	    = get_pairs(lowerCAmelCase_  )
      _A:						List[str]	    = " ".join(lowerCAmelCase_  )
      if word == "\n  </w>":
         _A:						Tuple	    = "\n</w>"
      _A:						Tuple	    = word
      return word


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

      """simple docstring"""
      _A:						str	    = []
      if self.fix_text is None:
         # Using BERT's BasicTokenizer
         _A:						Any	    = self.nlp.tokenize(lowerCAmelCase_  )
         for token in text:
            split_tokens.extend(list(self.bpe(lowerCAmelCase_  ).split(''' '''  )  )  )
      else:
         # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
         _A:						Union[str, Any]	    = self.nlp(text_standardize(self.fix_text(lowerCAmelCase_  )  )  )
         for token in text:
            split_tokens.extend(list(self.bpe(token.text.lower()  ).split(''' '''  )  )  )
      return split_tokens


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

      """simple docstring"""
      return self.encoder.get(lowerCAmelCase_    ,    self.encoder.get(self.unk_token  )  )


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

      """simple docstring"""
      return self.decoder.get(lowerCAmelCase_    ,    self.unk_token  )


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = "".join(lowerCAmelCase_  ).replace('''</w>'''    ,    ''' '''  ).strip()
      return out_string


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

      """simple docstring"""






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

      with open(lowerCAmelCase_    ,    '''w'''    ,    encoding='''utf-8'''  ) as f:
         f.write(json.dumps(self.encoder    ,    indent=2    ,    sort_keys=lowerCAmelCase_    ,    ensure_ascii=lowerCAmelCase_  ) + '''\n'''  )

      _A:						int	    = 0
      with open(lowerCAmelCase_    ,    '''w'''    ,    encoding='''utf-8'''  ) as writer:
         writer.write('''#version: 0.2\n'''  )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,    key=lambda lowerCAmelCase_  : kv[1]  ):
            if index != token_index:
               logger.warning(
                   F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                   ''' Please check that the tokenizer is not corrupted!'''  )
               _A:						Tuple	    = token_index
            writer.write(''' '''.join(lowerCAmelCase_  ) + '''\n'''  )
            index += 1

      return vocab_file, merge_file





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)



