






'''simple docstring'''





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

import sentencepiece as spm

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


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

_UpperCamelCase						:						Dict  =					'▁'

_UpperCamelCase						:						Optional[Any]  =					{'vocab_file': 'sentencepiece.bpe.model'}

_UpperCamelCase						:						int  =					{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

_UpperCamelCase						:						Tuple  =					{
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
_UpperCamelCase						:						List[Any]  =					['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__      (					snake_case_):
					a_        =					VOCAB_FILES_NAMES
					a_        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_        =					PRETRAINED_VOCAB_FILES_MAP
					a_        =					['''input_ids''', '''attention_mask''']

					a_        =					[]
					a_        =					[]




					def __init__(  self  :		int				, _A  :		List[Any]				, _A  :		int="<s>"				, _A  :		str="</s>"				, _A  :		Union[str, Any]="</s>"				, _A  :		Optional[Any]="<s>"				, _A  :		str="<unk>"				, _A  :		Any="<pad>"				, _A  :		Union[str, Any]="<mask>"				, _A  :		Tuple=None				, _A  :		Any=None				, _A  :		Optional[int]=None				, _A  :		Optional[int] = None				, _A  :		List[str]=None				, _A  :		str=False				, **_A  :		int				, )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else mask_token

								UpperCAmelCase_      :       Union[str, Any]          =       {} if sp_model_kwargs is None else sp_model_kwargs
								UpperCAmelCase_      :       Union[str, Any]          =       legacy_behaviour

								super().__init__(
								    bos_token=UpperCamelCase__				, eos_token=UpperCamelCase__				, unk_token=UpperCamelCase__				, sep_token=UpperCamelCase__				, cls_token=UpperCamelCase__				, pad_token=UpperCamelCase__				, mask_token=UpperCamelCase__				, tokenizer_file=UpperCamelCase__				, src_lang=UpperCamelCase__				, tgt_lang=UpperCamelCase__				, additional_special_tokens=UpperCamelCase__				, sp_model_kwargs=self.sp_model_kwargs				, legacy_behaviour=UpperCamelCase__				, **UpperCamelCase__				, )

								UpperCAmelCase_      :       List[Any]          =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(UpperCamelCase__      )      )
								UpperCAmelCase_      :       int          =       vocab_file
								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
								# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
								# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

								# Mimic fairseq token-to-id alignment for the first 4 token
								UpperCAmelCase_      :       List[str]          =       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       len(self.sp_model      )
								UpperCAmelCase_      :       str          =       {
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__      )
								}
								UpperCAmelCase_      :       Any          =       {v: k for k, v in self.lang_code_to_id.items()}
								UpperCAmelCase_      :       Union[str, Any]          =       len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
								UpperCAmelCase_      :       Tuple          =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
								UpperCAmelCase_      :       Optional[int]          =       list(self.lang_code_to_id.keys()      )

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

								UpperCAmelCase_      :       Tuple          =       src_lang if src_lang is not None else "eng_Latn"
								UpperCAmelCase_      :       List[Any]          =       self.lang_code_to_id[self._src_lang]
								UpperCAmelCase_      :       Optional[int]          =       tgt_lang
								self.set_src_lang_special_tokens(self._src_lang      )




					def __getstate__(  self  :		Optional[Any]      )    ->							Dict:
								UpperCAmelCase_      :       Dict          =       self.__dict__.copy()
								UpperCAmelCase_      :       Any          =       None
								UpperCAmelCase_      :       Optional[int]          =       self.sp_model.serialized_model_proto()
								return state




					def __setstate__(  self  :		Optional[Any]				, _A  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       Any          =       d

								# for backward compatibility
								if not hasattr(self				, '''sp_model_kwargs'''      ):
											UpperCAmelCase_      :       Optional[Any]          =       {}

								UpperCAmelCase_      :       Optional[int]          =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )




					@property
					def 		A      (  self  :		int      )    ->							List[str]:
								return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token




					@property
					def 		A      (  self  :		List[Any]      )    ->							str:
								return self._src_lang




					@src_lang.setter
					def 		A      (  self  :		Dict				, _A  :		Tuple      )    ->							None:
								UpperCAmelCase_      :       int          =       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang      )




					def 		A      (  self  :		Any				, _A  :		Tuple				, _A  :		List[str] = None				, _A  :		List[Any] = False      )    ->							List[int]:




								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCamelCase__				, token_ids_a=UpperCamelCase__				, already_has_special_tokens=UpperCamelCase__      )

								UpperCAmelCase_      :       Any          =       [1] * len(self.prefix_tokens      )
								UpperCAmelCase_      :       Optional[Any]          =       [1] * len(self.suffix_tokens      )
								if token_ids_a is None:
											return prefix_ones + ([0] * len(UpperCamelCase__      )) + suffix_ones
								return prefix_ones + ([0] * len(UpperCamelCase__      )) + ([0] * len(UpperCamelCase__      )) + suffix_ones




					def 		A      (  self  :		int				, _A  :		Dict				, _A  :		List[str] = None      )    ->							List[int]:




								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  :		List[str]				, _A  :		List[Any]				, _A  :		List[Any] = None      )    ->							List[int]:
								UpperCAmelCase_      :       List[str]          =       [self.sep_token_id]
								UpperCAmelCase_      :       str          =       [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  :		List[str]				, _A  :		Optional[int]				, _A  :		Dict				, _A  :		List[Any]				, _A  :		Tuple				, **_A  :		List[str]      )    ->							List[Any]:




								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_      :       Optional[Any]          =       src_lang
								UpperCAmelCase_      :       Dict          =       self(UpperCamelCase__				, add_special_tokens=UpperCamelCase__				, return_tensors=UpperCamelCase__				, **UpperCamelCase__      )
								UpperCAmelCase_      :       str          =       self.convert_tokens_to_ids(UpperCamelCase__      )
								UpperCAmelCase_      :       Optional[int]          =       tgt_lang_id
								return inputs




					def 		A      (  self  :		Dict      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       {self.convert_ids_to_tokens(UpperCamelCase__      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab




					def 		A      (  self  :		List[str]				, _A  :		List[str]      )    ->							List[str]:
								return self.sp_model.encode(UpperCamelCase__				, out_type=UpperCamelCase__      )




					def 		A      (  self  :		Union[str, Any]				, _A  :		Optional[int]      )    ->							List[str]:




								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase_      :       List[str]          =       self.sp_model.PieceToId(UpperCamelCase__      )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




					def 		A      (  self  :		Dict				, _A  :		str      )    ->							Dict:




								if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset      )




					def 		A      (  self  :		Optional[Any]				, _A  :		Optional[Any]      )    ->							Tuple:
								UpperCAmelCase_      :       int          =       "".join(UpperCamelCase__      ).replace(UpperCamelCase__				, ''' '''      ).strip()
								return out_string




					def 		A      (  self  :		List[Any]				, _A  :		Optional[Any]				, _A  :		Any = None      )    ->							Tuple[str]:




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

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

								return (out_vocab_file,)




					def 		A      (  self  :		str				, _A  :		Optional[int]				, _A  :		List[str] = "eng_Latn"				, _A  :		Union[str, Any] = None				, _A  :		List[Any] = "fra_Latn"				, **_A  :		int				, )    ->							BatchEncoding:
								UpperCAmelCase_      :       Union[str, Any]          =       src_lang
								UpperCAmelCase_      :       Any          =       tgt_lang
								return super().prepare_seqaseq_batch(UpperCamelCase__				, UpperCamelCase__				, **UpperCamelCase__      )




					def 		A      (  self  :		str      )    ->							Any:
								return self.set_src_lang_special_tokens(self.src_lang      )




					def 		A      (  self  :		int      )    ->							Union[str, Any]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang      )




					def 		A      (  self  :		Optional[Any]				, _A  :		Tuple      )    ->							None:
								UpperCAmelCase_      :       Optional[Any]          =       self.lang_code_to_id[src_lang]
								if self.legacy_behaviour:
											UpperCAmelCase_      :       Optional[int]          =       []
											UpperCAmelCase_      :       List[str]          =       [self.eos_token_id, self.cur_lang_code]
								else:
											UpperCAmelCase_      :       int          =       [self.cur_lang_code]
											UpperCAmelCase_      :       Optional[Any]          =       [self.eos_token_id]





					def 		A      (  self  :		List[str]				, _A  :		Union[str, Any]      )    ->							None:
								UpperCAmelCase_      :       Dict          =       self.lang_code_to_id[lang]
								if self.legacy_behaviour:
											UpperCAmelCase_      :       Any          =       []
											UpperCAmelCase_      :       int          =       [self.eos_token_id, self.cur_lang_code]
								else:
											UpperCAmelCase_      :       int          =       [self.cur_lang_code]
											UpperCAmelCase_      :       Union[str, Any]          =       [self.eos_token_id]






"""simple docstring"""



import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

    if model_size == "tiny":
        snake_case       : str    =       96
        snake_case       : List[Any]    =       (2, 2, 6, 2)
        snake_case       : Union[str, Any]    =       (3, 6, 12, 24)
    elif model_size == "small":
        snake_case       : int    =       96
        snake_case       : Optional[Any]    =       (2, 2, 18, 2)
        snake_case       : Any    =       (3, 6, 12, 24)
    elif model_size == "base":
        snake_case       : List[str]    =       128
        snake_case       : int    =       (2, 2, 18, 2)
        snake_case       : Any    =       (4, 8, 16, 32)
    else:
        snake_case       : List[str]    =       192
        snake_case       : str    =       (2, 2, 18, 2)
        snake_case       : Any    =       (6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





    if "patch_embed.proj" in name:
        snake_case       : int    =       name.replace("patch_embed.proj"		,				"embeddings.patch_embeddings.projection"					)
    if "patch_embed.norm" in name:
        snake_case       : Union[str, Any]    =       name.replace("patch_embed.norm"		,				"embeddings.norm"					)
    if "layers" in name:
        snake_case       : Optional[Any]    =       "encoder." + name
    if "attn.proj" in name:
        snake_case       : List[Any]    =       name.replace("attn.proj"		,				"attention.output.dense"					)
    if "attn" in name:
        snake_case       : List[str]    =       name.replace("attn"		,				"attention.self"					)
    if "norm1" in name:
        snake_case       : List[str]    =       name.replace("norm1"		,				"layernorm_before"					)
    if "norm2" in name:
        snake_case       : Optional[int]    =       name.replace("norm2"		,				"layernorm_after"					)
    if "mlp.fc1" in name:
        snake_case       : int    =       name.replace("mlp.fc1"		,				"intermediate.dense"					)
    if "mlp.fc2" in name:
        snake_case       : Tuple    =       name.replace("mlp.fc2"		,				"output.dense"					)

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

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

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

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

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

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin 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."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





