





"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_nllb import NllbTokenizer
else:
  lowercase__				:Optional[Any]   =       None


lowercase__				:List[str]   =       logging.get_logger(__name__)


lowercase__				:Any   =       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__				:List[str]   =       {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__				:Dict   =       {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
lowercase__				:Dict   =       ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Tuple         =       VOCAB_FILES_NAMES
      _A     :       List[str]         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A     :       List[str]         =       PRETRAINED_VOCAB_FILES_MAP
      _A     :       Optional[int]         =       ['input_ids', 'attention_mask']
      _A     :       int         =       NllbTokenizer

      _A     :       List[int]         =       []
      _A     :       List[int]         =       []

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


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						AddedToken(__lowercase						,				lstrip=__lowercase						,				rstrip=__lowercase ) if isinstance(__lowercase						,				__lowercase ) else mask_token
        __UpperCAmelCase    :						Any       =						legacy_behaviour
        super().__init__(
            vocab_file=__lowercase						,				tokenizer_file=__lowercase						,				bos_token=__lowercase						,				eos_token=__lowercase						,				sep_token=__lowercase						,				cls_token=__lowercase						,				unk_token=__lowercase						,				pad_token=__lowercase						,				mask_token=__lowercase						,				src_lang=__lowercase						,				tgt_lang=__lowercase						,				additional_special_tokens=__lowercase						,				legacy_behaviour=__lowercase						,				**__lowercase						,				)

        __UpperCAmelCase    :						Union[str, Any]       =						vocab_file
        __UpperCAmelCase    :						List[str]       =						False if not self.vocab_file else True

        __UpperCAmelCase    :						List[Any]       =						FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens] )

        self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
        __UpperCAmelCase    :						Tuple       =						{
            lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        __UpperCAmelCase    :						str       =						src_lang if src_lang is not None else '''eng_Latn'''
        __UpperCAmelCase    :						Union[str, Any]       =						self.convert_tokens_to_ids(self._src_lang )
        __UpperCAmelCase    :						Optional[Any]       =						tgt_lang
        self.set_src_lang_special_tokens(self._src_lang )

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        return self._src_lang

      @src_lang.setter
      def       A_					(							self       :      str						,				__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						new_src_lang
        self.set_src_lang_special_tokens(self._src_lang )

      def       A_					(							self       :      Any						,				__lowercase       :      List[int]						,				__lowercase       :      Optional[List[int]] = None ):


        '''simple docstring'''




        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

      def       A_					(							self       :      str						,				__lowercase       :      List[int]						,				__lowercase       :      Optional[List[int]] = None ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						[self.sep_token_id]
        __UpperCAmelCase    :						Dict       =						[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 + sep + token_ids_a + sep ) * [0]

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      str						,				__lowercase       :      Optional[str]						,				__lowercase       :      Optional[str]						,				**__lowercase       :      int ):


        '''simple docstring'''




        if src_lang is None or tgt_lang is None:
          raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
        __UpperCAmelCase    :						str       =						src_lang
        __UpperCAmelCase    :						Tuple       =						self(__lowercase						,				add_special_tokens=__lowercase						,				return_tensors=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						self.convert_tokens_to_ids(__lowercase )
        __UpperCAmelCase    :						Any       =						tgt_lang_id
        return inputs

      def       A_					(							self       :      int						,				__lowercase       :      List[str]						,				__lowercase       :      str = "eng_Latn"						,				__lowercase       :      Optional[List[str]] = None						,				__lowercase       :      str = "fra_Latn"						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						src_lang
        __UpperCAmelCase    :						str       =						tgt_lang
        return super().prepare_seqaseq_batch(__lowercase						,				__lowercase						,				**__lowercase )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        return self.set_src_lang_special_tokens(self.src_lang )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return self.set_tgt_lang_special_tokens(self.tgt_lang )

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.convert_tokens_to_ids(__lowercase )

        if self.legacy_behaviour:
          __UpperCAmelCase    :						int       =						[]
          __UpperCAmelCase    :						Dict       =						[self.eos_token_id, self.cur_lang_code]
        else:
          __UpperCAmelCase    :						Any       =						[self.cur_lang_code]
          __UpperCAmelCase    :						Union[str, Any]       =						[self.eos_token_id]

        __UpperCAmelCase    :						List[Any]       =						self.convert_ids_to_tokens(self.prefix_tokens )
        __UpperCAmelCase    :						List[Any]       =						self.convert_ids_to_tokens(self.suffix_tokens )

        __UpperCAmelCase    :						Union[str, Any]       =						processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str						,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str						,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,				self.prefix_tokens + self.suffix_tokens ) )						,				)

      def       A_					(							self       :      List[str]						,				__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						self.convert_tokens_to_ids(__lowercase )
        if self.legacy_behaviour:
          __UpperCAmelCase    :						int       =						[]
          __UpperCAmelCase    :						Dict       =						[self.eos_token_id, self.cur_lang_code]
        else:
          __UpperCAmelCase    :						str       =						[self.cur_lang_code]
          __UpperCAmelCase    :						int       =						[self.eos_token_id]

        __UpperCAmelCase    :						Any       =						self.convert_ids_to_tokens(self.prefix_tokens )
        __UpperCAmelCase    :						Union[str, Any]       =						self.convert_ids_to_tokens(self.suffix_tokens )

        __UpperCAmelCase    :						Optional[int]       =						processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str						,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str						,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,				self.prefix_tokens + self.suffix_tokens ) )						,				)







      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      str						,				__lowercase       :      Optional[str] = None ):


        '''simple docstring'''




        if not self.can_save_slow_tokenizer:
          raise ValueError(
              '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
              '''tokenizer.''' )

        if not os.path.isdir(__lowercase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
          return
        __UpperCAmelCase    :						Tuple       =						os.path.join(
            __lowercase						,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
          copyfile(self.vocab_file						,				__lowercase )

        return (out_vocab_file,)






"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						SwinvaConfig()
  __UpperCAmelCase    :						Any       =						swinva_name.split('''_'''  )

  __UpperCAmelCase    :						Optional[Any]       =						name_split[1]
  if "to" in name_split[3]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[3][-3:]  )
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						int(name_split[3]  )
  if "to" in name_split[2]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[2][-2:]  )
  else:
    __UpperCAmelCase    :						str       =						int(name_split[2][6:]  )

  if model_size == "tiny":
    __UpperCAmelCase    :						Union[str, Any]       =						96
    __UpperCAmelCase    :						str       =						(2, 2, 6, 2)
    __UpperCAmelCase    :						Union[str, Any]       =						(3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase    :						List[Any]       =						96
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase    :						Any       =						1_28
    __UpperCAmelCase    :						int       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						Dict       =						(4, 8, 16, 32)
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						1_92
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __UpperCAmelCase    :						Optional[int]       =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __UpperCAmelCase    :						int       =						2_18_41
    __UpperCAmelCase    :						Optional[Any]       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-22k-id2label.json'''
    __UpperCAmelCase    :						Any       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						List[Any]       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Optional[Any]       =						idalabel
    __UpperCAmelCase    :						Optional[int]       =						{v: k for k, v in idalabel.items()}

  else:
    __UpperCAmelCase    :						List[Any]       =						10_00
    __UpperCAmelCase    :						str       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Tuple       =						'''imagenet-1k-id2label.json'''
    __UpperCAmelCase    :						Optional[int]       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						str       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Any       =						idalabel
    __UpperCAmelCase    :						List[Any]       =						{v: k for k, v in idalabel.items()}

  __UpperCAmelCase    :						Optional[int]       =						img_size
  __UpperCAmelCase    :						int       =						num_classes
  __UpperCAmelCase    :						Tuple       =						embed_dim
  __UpperCAmelCase    :						str       =						depths
  __UpperCAmelCase    :						Optional[Any]       =						num_heads
  __UpperCAmelCase    :						Optional[int]       =						window_size

  return config


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  if "patch_embed.proj" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''patch_embed.proj'''  ,			'''embeddings.patch_embeddings.projection'''  )
  if "patch_embed.norm" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''patch_embed.norm'''  ,			'''embeddings.norm'''  )
  if "layers" in name:
    __UpperCAmelCase    :						Optional[int]       =						'''encoder.''' + name
  if "attn.proj" in name:
    __UpperCAmelCase    :						Optional[Any]       =						name.replace('''attn.proj'''  ,			'''attention.output.dense'''  )
  if "attn" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''attn'''  ,			'''attention.self'''  )
  if "norm1" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''norm1'''  ,			'''layernorm_before'''  )
  if "norm2" in name:
    __UpperCAmelCase    :						Dict       =						name.replace('''norm2'''  ,			'''layernorm_after'''  )
  if "mlp.fc1" in name:
    __UpperCAmelCase    :						str       =						name.replace('''mlp.fc1'''  ,			'''intermediate.dense'''  )
  if "mlp.fc2" in name:
    __UpperCAmelCase    :						int       =						name.replace('''mlp.fc2'''  ,			'''output.dense'''  )
  if "q_bias" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''q_bias'''  ,			'''query.bias'''  )
  if "k_bias" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''k_bias'''  ,			'''key.bias'''  )
  if "v_bias" in name:
    __UpperCAmelCase    :						str       =						name.replace('''v_bias'''  ,			'''value.bias'''  )
  if "cpb_mlp" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''cpb_mlp'''  ,			'''continuous_position_bias_mlp'''  )
  if name == "norm.weight":
    __UpperCAmelCase    :						int       =						'''layernorm.weight'''
  if name == "norm.bias":
    __UpperCAmelCase    :						Optional[Any]       =						'''layernorm.bias'''

  if "head" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''head'''  ,			'''classifier'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						'''swinv2.''' + name

  return name


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase    :						Any       =						orig_state_dict.pop(UpperCAmelCase_  )

    if "mask" in key:
      continue
    elif "qkv" in key:
      __UpperCAmelCase    :						int       =						key.split('''.'''  )
      __UpperCAmelCase    :						int       =						int(key_split[1]  )
      __UpperCAmelCase    :						str       =						int(key_split[3]  )
      __UpperCAmelCase    :						List[str]       =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase    :						Optional[Any]       =						val[:dim, :]
        __UpperCAmelCase    :						Union[str, Any]       =						val[dim : dim * 2, :]
        __UpperCAmelCase    :						List[Any]       =						val[-dim:, :]
      else:
        __UpperCAmelCase    :						Optional[int]       =						val[:dim]
        __UpperCAmelCase    :						Any       =						val[
            dim : dim * 2
        ]
        __UpperCAmelCase    :						List[str]       =						val[-dim:]
    else:
      __UpperCAmelCase    :						Tuple       =						val

  return orig_state_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

  __UpperCAmelCase    :						Any       =						get_swinva_config(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						SwinvaForImageClassification(UpperCAmelCase_  )
  model.eval()

  __UpperCAmelCase    :						int       =						convert_state_dict(timm_model.state_dict()  ,			UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  __UpperCAmelCase    :						Optional[Any]       =						AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''  ,			'''-'''  )  )  )
  __UpperCAmelCase    :						List[Any]       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  )
  __UpperCAmelCase    :						Union[str, Any]       =						image_processor(images=UpperCAmelCase_  ,			return_tensors='''pt'''  )

  __UpperCAmelCase    :						List[Any]       =						timm_model(inputs['''pixel_values''']  )
  __UpperCAmelCase    :						Tuple       =						model(**UpperCAmelCase_  ).logits

  assert torch.allclose(UpperCAmelCase_  ,			UpperCAmelCase_  ,			atol=1e-3  )

  print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(UpperCAmelCase_  )

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(UpperCAmelCase_  )

  model.push_to_hub(
      repo_path_or_name=Path(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			organization='''nandwalritik'''  ,			commit_message='''Add model'''  ,			)


if __name__ == "__main__":
  lowercase__				:List[Any]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swinv2_name',
      default='swinv2_tiny_patch4_window8_256',
      type=str,
      help='Name of the Swinv2 timm 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.'
  )

  lowercase__				:List[Any]				   =       parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)