






"""simple docstring"""

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    '''src_vocab_file''': '''vocab-src.json''',
    '''tgt_vocab_file''': '''vocab-tgt.json''',
    '''merges_file''': '''merges.txt''',
}

lowercase           =							{
    '''src_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'''
    },
    '''tgt_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'''
    },
    '''merges_file''': {'''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'''},
}

lowercase           =							{'''stas/tiny-wmt19-en-de''': 1024}
lowercase           =							{
    '''stas/tiny-wmt19-en-de''': {
        '''langs''': ['''en''', '''de'''],
        '''model_max_length''': 1024,
        '''special_tokens_map_file''': None,
        '''full_tokenizer_file''': None,
    }
}






def _lowerCAmelCase						(	__lowerCamelCase:List[str] ):

						'''simple docstring'''






						__magic_name__      =  set()
						__magic_name__      =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char) )
												__magic_name__      =  char
						return pairs






def _lowerCAmelCase						(	__lowerCamelCase:List[str] ):

						'''simple docstring'''






						__magic_name__      =  text.replace("，"			,    "," )
						__magic_name__      =  re.sub(r"。\s*"			,    ". "			,    __lowerCamelCase )
						__magic_name__      =  text.replace("、"			,    "," )
						__magic_name__      =  text.replace("”"			,    "\"" )
						__magic_name__      =  text.replace("“"			,    "\"" )
						__magic_name__      =  text.replace("∶"			,    ":" )
						__magic_name__      =  text.replace("："			,    ":" )
						__magic_name__      =  text.replace("？"			,    "?" )
						__magic_name__      =  text.replace("《"			,    "\"" )
						__magic_name__      =  text.replace("》"			,    "\"" )
						__magic_name__      =  text.replace("）"			,    ")" )
						__magic_name__      =  text.replace("！"			,    "!" )
						__magic_name__      =  text.replace("（"			,    "(" )
						__magic_name__      =  text.replace("；"			,    ";" )
						__magic_name__      =  text.replace("１"			,    "1" )
						__magic_name__      =  text.replace("」"			,    "\"" )
						__magic_name__      =  text.replace("「"			,    "\"" )
						__magic_name__      =  text.replace("０"			,    "0" )
						__magic_name__      =  text.replace("３"			,    "3" )
						__magic_name__      =  text.replace("２"			,    "2" )
						__magic_name__      =  text.replace("５"			,    "5" )
						__magic_name__      =  text.replace("６"			,    "6" )
						__magic_name__      =  text.replace("９"			,    "9" )
						__magic_name__      =  text.replace("７"			,    "7" )
						__magic_name__      =  text.replace("８"			,    "8" )
						__magic_name__      =  text.replace("４"			,    "4" )
						__magic_name__      =  re.sub(r"．\s*"			,    ". "			,    __lowerCamelCase )
						__magic_name__      =  text.replace("～"			,    "~" )
						__magic_name__      =  text.replace("’"			,    "'" )
						__magic_name__      =  text.replace("…"			,    "..." )
						__magic_name__      =  text.replace("━"			,    "-" )
						__magic_name__      =  text.replace("〈"			,    "<" )
						__magic_name__      =  text.replace("〉"			,    ">" )
						__magic_name__      =  text.replace("【"			,    "[" )
						__magic_name__      =  text.replace("】"			,    "]" )
						__magic_name__      =  text.replace("％"			,    "%" )
						return text






def _lowerCAmelCase						(	__lowerCamelCase:Tuple ):

						'''simple docstring'''






						__magic_name__      =  []
						for char in text:
												__magic_name__      =  unicodedata.category(__lowerCamelCase )
												if cat.startswith("C" ):
																		continue
												output.append(__lowerCamelCase )
						return "".join(__lowerCamelCase )


class     A_				(   snake_case_	):
				UpperCAmelCase__										=					VOCAB_FILES_NAMES
				UpperCAmelCase__										=					PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__										=					PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase__										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__										=					['''input_ids''', '''attention_mask''']







				def __init__(				self				:    Tuple    ,     __lowerCamelCase				:    Optional[int]=None    ,     __lowerCamelCase				:    Tuple=None    ,     __lowerCamelCase				:    List[Any]=None    ,     __lowerCamelCase				:    List[Any]=None    ,     __lowerCamelCase				:    int=False    ,     __lowerCamelCase				:    List[str]="<unk>"    ,     __lowerCamelCase				:    int="<s>"    ,     __lowerCamelCase				:    List[str]="</s>"    ,     __lowerCamelCase				:    List[Any]="<pad>"    ,     **__lowerCamelCase				:    str    ,     )					->       str:
										super().__init__(
										    langs=__lowerCamelCase    ,     src_vocab_file=__lowerCamelCase    ,     tgt_vocab_file=__lowerCamelCase    ,     merges_file=__lowerCamelCase    ,     do_lower_case=__lowerCamelCase    ,     unk_token=__lowerCamelCase    ,     bos_token=__lowerCamelCase    ,     sep_token=__lowerCamelCase    ,     pad_token=__lowerCamelCase    ,     **__lowerCamelCase    ,     )

										try:
																import sacremoses
										except ImportError:
																raise ImportError(
																    "You need to install sacremoses to use XLMTokenizer. "
																    "See https://pypi.org/project/sacremoses/ for installation."						)

										__magic_name__      =  sacremoses

										__magic_name__      =  src_vocab_file
										__magic_name__      =  tgt_vocab_file
										__magic_name__      =  merges_file
										__magic_name__      =  do_lower_case

										# cache of sm.MosesPunctNormalizer instance
										__magic_name__      =  {}
										# cache of sm.MosesTokenizer instance
										__magic_name__      =  {}
										__magic_name__      =  {}

										if langs and len(__lowerCamelCase						) == 2:
																__magic_name__   ,     __magic_name__      =  langs
										else:
																raise ValueError(
																    f'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
																    "Usually that means that tokenizer can't find a mapping for the given model path "
																    "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."						)

										with open(__lowerCamelCase    ,     encoding="utf-8"						) as src_vocab_handle:
																__magic_name__      =  json.load(__lowerCamelCase						)
										with open(__lowerCamelCase    ,     encoding="utf-8"						) as tgt_vocab_handle:
																__magic_name__      =  json.load(__lowerCamelCase						)
																__magic_name__      =  {v: k for k, v in tgt_vocab.items()}
										with open(__lowerCamelCase    ,     encoding="utf-8"						) as merges_handle:
																__magic_name__      =  merges_handle.read().split("\n"						)[:-1]
										__magic_name__      =  [tuple(merge.split()[:2]						) for merge in merges]
										__magic_name__      =  dict(zip(__lowerCamelCase    ,     range(len(__lowerCamelCase						)						)						)						)
										__magic_name__      =  {}







				def      _snake_case       (				self				:    Any						)					->       Dict[str, int]:
										return self.get_src_vocab()







				@property
				def      _snake_case       (				self				:    List[str]						)					->       int:
										return self.src_vocab_size







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    int						)					->       List[str]:
										if lang not in self.cache_moses_punct_normalizer:
																__magic_name__      =  self.sm.MosesPunctNormalizer(lang=__lowerCamelCase						)
																__magic_name__      =  punct_normalizer
										return self.cache_moses_punct_normalizer[lang].normalize(__lowerCamelCase						)







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    List[str]						)					->       List[Any]:
										if lang not in self.cache_moses_tokenizer:
																__magic_name__      =  self.sm.MosesTokenizer(lang=__lowerCamelCase						)
																__magic_name__      =  moses_tokenizer
										return self.cache_moses_tokenizer[lang].tokenize(
										    __lowerCamelCase    ,     aggressive_dash_splits=__lowerCamelCase    ,     return_str=__lowerCamelCase    ,     escape=__lowerCamelCase						)







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    int						)					->       Dict:
										if lang not in self.cache_moses_detokenizer:
																__magic_name__      =  self.sm.MosesDetokenizer(lang=__lowerCamelCase						)
																__magic_name__      =  moses_detokenizer
										return self.cache_moses_detokenizer[lang].detokenize(__lowerCamelCase						)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    Optional[Any]						)					->       str:
										__magic_name__      =  replace_unicode_punct(__lowerCamelCase						)
										__magic_name__      =  self.moses_punct_norm(__lowerCamelCase    ,     __lowerCamelCase						)
										__magic_name__      =  remove_non_printing_char(__lowerCamelCase						)
										return text







				@property
				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[Any]:
										return len(self.encoder						)







				@property
				def      _snake_case       (				self				:    Any						)					->       List[str]:
										return len(self.decoder						)







				def      _snake_case       (				self				:    Any						)					->       Any:
										return dict(self.encoder    ,     **self.added_tokens_encoder						)







				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[Any]:
										return dict(self.decoder    ,     **self.added_tokens_decoder						)







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Union[str, Any]						)					->       Tuple:
										__magic_name__      =  tuple(token[:-1]						) + (token[-1] + "</w>",)
										if token in self.cache:
																return self.cache[token]
										__magic_name__      =  get_pairs(__lowerCamelCase						)

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

										while True:
																__magic_name__      =  min(__lowerCamelCase    ,     key=lambda __lowerCamelCase						: self.bpe_ranks.get(__lowerCamelCase    ,     float("inf"						)						)						)
																if bigram not in self.bpe_ranks:
																						break
																__magic_name__   ,     __magic_name__      =  bigram
																__magic_name__      =  []
																__magic_name__      =  0
																while i < len(__lowerCamelCase						):
																						try:
																												__magic_name__      =  word.index(__lowerCamelCase    ,     __lowerCamelCase						)
																						except ValueError:
																												new_word.extend(word[i:]						)
																												break
																						else:
																												new_word.extend(word[i:j]						)
																												__magic_name__      =  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
																__magic_name__      =  tuple(__lowerCamelCase						)
																__magic_name__      =  new_word
																if len(__lowerCamelCase						) == 1:
																						break
																else:
																						__magic_name__      =  get_pairs(__lowerCamelCase						)
										__magic_name__      =  " ".join(__lowerCamelCase						)
										if word == "\n  </w>":
																__magic_name__      =  "\n</w>"
										__magic_name__      =  word
										return word







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Union[str, Any]="en"    ,     __lowerCamelCase				:    Any=False						)					->       str:
										__magic_name__      =  self.src_lang

										if self.do_lower_case:
																__magic_name__      =  text.lower()

										if bypass_tokenizer:
																__magic_name__      =  text.split()
										else:
																__magic_name__      =  self.moses_pipeline(__lowerCamelCase    ,     lang=__lowerCamelCase						)
																__magic_name__      =  self.moses_tokenize(__lowerCamelCase    ,     lang=__lowerCamelCase						)

										__magic_name__      =  []
										for token in text:
																if token:
																						split_tokens.extend(list(self.bpe(__lowerCamelCase						).split(" "						)						)						)

										return split_tokens







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    List[str]						)					->       Optional[int]:
										return self.encoder.get(__lowerCamelCase    ,     self.encoder.get(self.unk_token						)						)







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    List[Any]						)					->       List[str]:
										return self.decoder.get(__lowerCamelCase    ,     self.unk_token						)







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Tuple						)					->       List[Any]:
										__magic_name__      =  [t.replace(" "    ,     ""						).replace("</w>"    ,     " "						) for t in tokens]
										__magic_name__      =  "".join(__lowerCamelCase						).split()
										# detokenize
										__magic_name__      =  self.moses_detokenize(__lowerCamelCase    ,     self.tgt_lang						)
										return text







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    List[int]    ,     __lowerCamelCase				:    Optional[List[int]] = None						)					->       List[int]:
										__magic_name__      =  [self.sep_token_id]

										# no bos used in fairseq
										if token_ids_a is None:
																return token_ids_a + sep
										return token_ids_a + sep + token_ids_a + sep







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    List[int]    ,     __lowerCamelCase				:    Optional[List[int]] = None    ,     __lowerCamelCase				:    bool = False						)					->       List[int]:

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=__lowerCamelCase    ,     token_ids_a=__lowerCamelCase    ,     already_has_special_tokens=__lowerCamelCase						)
										# no bos used in fairseq
										if token_ids_a is not None:
																return ([0] * len(__lowerCamelCase						)) + [1] + ([0] * len(__lowerCamelCase						)) + [1]
										return ([0] * len(__lowerCamelCase						)) + [1]







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    List[int]    ,     __lowerCamelCase				:    Optional[List[int]] = None						)					->       List[int]:
										__magic_name__      =  [self.sep_token_id]

										# no bos used in fairseq
										if token_ids_a is None:
																return len(token_ids_a + sep						) * [0]
										return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Optional[str] = None						)					->       Tuple[str]:
										if not os.path.isdir(__lowerCamelCase						):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																return

										__magic_name__      =  os.path.join(
										    __lowerCamelCase    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]						)
										__magic_name__      =  os.path.join(
										    __lowerCamelCase    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]						)
										__magic_name__      =  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"						)

										with open(__lowerCamelCase    ,     "w"    ,     encoding="utf-8"						) as f:
																__magic_name__      =  {v: k for k, v in self.decoder.items()}
																f.write(json.dumps(__lowerCamelCase    ,     indent=2    ,     sort_keys=__lowerCamelCase    ,     ensure_ascii=__lowerCamelCase						) + "\n"						)

										__magic_name__      =  0
										with open(__lowerCamelCase    ,     "w"    ,     encoding="utf-8"						) as writer:
																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 {merges_file}: BPE merge indices are not consecutive.'''
																												    " Please check that the tokenizer is not corrupted!"						)
																												__magic_name__      =  token_index
																						writer.write(" ".join(__lowerCamelCase						) + "\n"						)
																						index += 1

										return src_vocab_file, tgt_vocab_file, merges_file







				def __getstate__(				self				:    int						)					->       Union[str, Any]:
										__magic_name__      =  self.__dict__.copy()
										__magic_name__      =  None
										return state







				def __setstate__(				self				:    str    ,     __lowerCamelCase				:    List[Any]						)					->       Any:
										__magic_name__      =  d

										try:
																import sacremoses
										except ImportError:
																raise ImportError(
																    "You need to install sacremoses to use XLMTokenizer. "
																    "See https://pypi.org/project/sacremoses/ for installation."						)

										__magic_name__      =  sacremoses











"""simple docstring"""

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()
lowercase           =							logging.get_logger(__name__)

@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Tensor    ,     __lowerCamelCase				:    Tensor						)					->       Dict:
										__magic_name__      =  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				:    int    ,     __lowerCamelCase				:    Tensor						)					->       List[str]:
										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      _snake_case       (				self				:    List[Any]						)					->       int:
										# 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     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					0
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def __call__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Tensor						)					->       str:
										__magic_name__      =  Tracker(self.dest						)(__lowerCamelCase						).parametrized
										__magic_name__      =  Tracker(self.src						)(__lowerCamelCase						).parametrized

										__magic_name__      =  list(filter(lambda __lowerCamelCase						: type(__lowerCamelCase						) not in self.src_skip    ,     __lowerCamelCase						)						)
										__magic_name__      =  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						(	__lowerCamelCase:nn.Module			,    __lowerCamelCase:nn.Module ):

						'''simple docstring'''






						__magic_name__      =  from_model.state_dict()
						__magic_name__      =  our_model.state_dict()
						__magic_name__      =  our_model.config
						__magic_name__      =  []
						for stage_idx in range(len(config.hidden_sizes ) ):
												for block_id in range(config.depths[stage_idx] ):
																		__magic_name__      =  f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																		__magic_name__      =  f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																		all_keys.append((from_key, to_key) )
																		__magic_name__      =  f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		__magic_name__      =  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:
												__magic_name__      =  from_state_dict.pop(__lowerCamelCase )

						our_model.load_state_dict(__lowerCamelCase )
						return our_model






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:VanConfig			,    __lowerCamelCase:str			,    __lowerCamelCase:nn.Module			,    __lowerCamelCase:Path			,    __lowerCamelCase:bool = True			,    ):

						'''simple docstring'''






						print(f'''Downloading weights for {name}...''' )
						__magic_name__      =  cached_download(__lowerCamelCase )
						print(f'''Converting {name}...''' )
						__magic_name__      =  torch.load(__lowerCamelCase )["state_dict"]
						from_model.load_state_dict(__lowerCamelCase )
						from_model.eval()
						with torch.no_grad():
												__magic_name__      =  VanForImageClassification(__lowerCamelCase ).eval()
												__magic_name__      =  ModuleTransfer(src=__lowerCamelCase			,    dest=__lowerCamelCase )
												__magic_name__      =  torch.randn((1, 3, 2_2_4, 2_2_4) )
												module_transfer(__lowerCamelCase )
												__magic_name__      =  copy_parameters(__lowerCamelCase			,    __lowerCamelCase )

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

						__magic_name__      =  name
						print(__lowerCamelCase )

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

												# we can use the convnext one
												__magic_name__      =  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=__lowerCamelCase			,    )

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






def _lowerCAmelCase						(	__lowerCamelCase:Path			,    __lowerCamelCase:str = None			,    __lowerCamelCase:bool = True ):

						'''simple docstring'''






						__magic_name__      =  "imagenet-1k-id2label.json"
						__magic_name__      =  1_0_0_0

						__magic_name__      =  "huggingface/label-files"
						__magic_name__      =  num_labels
						__magic_name__      =  json.load(open(hf_hub_download(__lowerCamelCase			,    __lowerCamelCase			,    repo_type="dataset" )			,    "r" ) )
						__magic_name__      =  {int(__lowerCamelCase ): v for k, v in idalabel.items()}

						__magic_name__      =  idalabel
						__magic_name__      =  {v: k for k, v in idalabel.items()}

						__magic_name__      =  partial(__lowerCamelCase			,    num_labels=__lowerCamelCase			,    idalabel=__lowerCamelCase			,    labelaid=__lowerCamelCase )

						__magic_name__      =  {
						    "van-tiny": ImageNetPreTrainedConfig(
						        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]			,    depths=[3, 3, 5, 2]			,    mlp_ratios=[8, 8, 4, 4]			,    ),
						    "van-small": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,    depths=[2, 2, 4, 2]			,    mlp_ratios=[8, 8, 4, 4]			,    ),
						    "van-base": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,    depths=[3, 3, 1_2, 3]			,    mlp_ratios=[8, 8, 4, 4]			,    ),
						    "van-large": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]			,    depths=[3, 5, 2_7, 3]			,    mlp_ratios=[8, 8, 4, 4]			,    ),
						}

						__magic_name__      =  {
						    "van-tiny": van_tiny,
						    "van-small": van_small,
						    "van-base": van_base,
						    "van-large": van_large,
						}

						__magic_name__      =  {
						    "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(
												    __lowerCamelCase			,    names_to_config[model_name]			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=__lowerCamelCase			,    push_to_hub=__lowerCamelCase			,    )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    __lowerCamelCase			,    __lowerCamelCase			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=__lowerCamelCase			,    push_to_hub=__lowerCamelCase			,    )


if __name__ == "__main__":
			lowercase           =							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.''',
			)

			lowercase           =							parser.parse_args()
			lowercase           =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			lowercase           =							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)



