


'''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_mbart import MBartTokenizer
else:
 UpperCAmelCase_ :     str								=      None


UpperCAmelCase_ :     Optional[int]								=      logging.get_logger(__name__)


UpperCAmelCase_ :     Union[str, Any]								=      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_ :     List[Any]								=      {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_ :     Optional[int]								=      {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
UpperCAmelCase_ :     Optional[int]								=      ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']

class 						_lowerCamelCase					(    __snake_case				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						VOCAB_FILES_NAMES
 __lowercase				:		Optional[int]    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase				:		str    							=						PRETRAINED_VOCAB_FILES_MAP
 __lowercase				:		Optional[Any]    							=						['''input_ids''', '''attention_mask''']
 __lowercase				:		Tuple    							=						MBartTokenizer

 __lowercase				:		Union[str, Any]    							=						[]
 __lowercase				:		List[str]    							=						[]


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase="<s>"      ,      __lowercase="</s>"      ,      __lowercase="</s>"      ,      __lowercase="<s>"      ,      __lowercase="<unk>"      ,      __lowercase="<pad>"      ,      __lowercase="<mask>"      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			AddedToken(__UpperCamelCase      ,      lstrip=__UpperCamelCase      ,      rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase      ,      __UpperCamelCase						) else mask_token

  super().__init__(
      vocab_file=__UpperCamelCase      ,      tokenizer_file=__UpperCamelCase      ,      bos_token=__UpperCamelCase      ,      eos_token=__UpperCamelCase      ,      sep_token=__UpperCamelCase      ,      cls_token=__UpperCamelCase      ,      unk_token=__UpperCamelCase      ,      pad_token=__UpperCamelCase      ,      mask_token=__UpperCamelCase      ,      src_lang=__UpperCamelCase      ,      tgt_lang=__UpperCamelCase      ,      additional_special_tokens=__UpperCamelCase      ,      **__UpperCamelCase      ,      )

  __A  :		List[Any]       =			vocab_file
  __A  :		List[str]       =			False if not self.vocab_file else True

  __A  :		int       =			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}						)
  __A  :		str       =			{
      lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
  }

  __A  :		List[Any]       =			src_lang if src_lang is not None else 'en_XX'
  __A  :		int       =			self.convert_tokens_to_ids(self._src_lang						)
  __A  :		Dict       =			tgt_lang
  self.set_src_lang_special_tokens(self._src_lang						)


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return self._src_lang


 @src_lang.setter
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			new_src_lang
  self.set_src_lang_special_tokens(self._src_lang						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = 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  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		str       =			[self.sep_token_id]
  __A  :		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  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      **__lowercase						):

  """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'						)
  __A  :		Union[str, Any]       =			src_lang
  __A  :		Optional[Any]       =			self(__UpperCamelCase      ,      add_special_tokens=__UpperCamelCase      ,      return_tensors=__UpperCamelCase      ,      **__UpperCamelCase						)
  __A  :		Optional[Any]       =			self.convert_tokens_to_ids(__UpperCamelCase						)
  __A  :		Union[str, Any]       =			tgt_lang_id
  return inputs


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = "en_XX"      ,      __lowercase = None      ,      __lowercase = "ro_RO"      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			src_lang
  __A  :		Dict       =			tgt_lang
  return super().prepare_seqaseq_batch(__UpperCamelCase      ,      __UpperCamelCase      ,      **__UpperCamelCase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.set_src_lang_special_tokens(self.src_lang						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.set_tgt_lang_special_tokens(self.tgt_lang						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.convert_tokens_to_ids(__UpperCamelCase						)
  __A  :		Optional[int]       =			[]
  __A  :		Optional[Any]       =			[self.eos_token_id, self.cur_lang_code]

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

  __A  :		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  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			self.convert_tokens_to_ids(__UpperCamelCase						)
  __A  :		Any       =			[]
  __A  :		Union[str, Any]       =			[self.eos_token_id, self.cur_lang_code]

  __A  :		int       =			self.convert_ids_to_tokens(self.prefix_tokens						)
  __A  :		List[str]       =			self.convert_ids_to_tokens(self.suffix_tokens						)

  __A  :		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  snake_case__   (      self      ,      __lowercase      ,      __lowercase = 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(__UpperCamelCase						):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
   return
  __A  :		List[Any]       =			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						):
   copyfile(self.vocab_file      ,      __UpperCamelCase						)

  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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Tuple=False			):
 __A  :		List[str]       =			[]

 # fmt: off
 # stem:
 rename_keys.append(('cls_token', 'vit.embeddings.cls_token')			)
 rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')			)

 rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')			)
 rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')			)

 # backbone
 rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')			)

 for stage_idx in range(len(config.backbone_config.depths			)			):
  for layer_idx in range(config.backbone_config.depths[stage_idx]			):
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")			)

  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")			)

 # transformer encoder
 for i in range(config.num_hidden_layers			):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
          ('pre_logits.fc.weight', 'pooler.dense.weight'),
          ('pre_logits.fc.bias', 'pooler.dense.bias'),
      ]			)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __A  :		List[str]       =			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('norm.weight', 'vit.layernorm.weight'),
          ('norm.bias', 'vit.layernorm.bias'),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]			)
 # fmt: on

 return rename_keys


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[Any]=False			):
 for i in range(config.num_hidden_layers			):
  if base_model:
   __A  :		Optional[Any]       =			''
  else:
   __A  :		int       =			'vit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
  # next, add query, keys and values (in that order) to the state dict
  __A  :		List[Any]       =			in_proj_weight[
      : config.hidden_size, :
  ]
  __A  :		List[Any]       =			in_proj_bias[: config.hidden_size]
  __A  :		Optional[int]       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		int       =			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __A  :		Dict       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		Optional[Any]       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Dict       =			dct.pop(UpperCamelCase__			)
 __A  :		Optional[Any]       =			val


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any=False			):
 __A  :		Optional[Any]       =			BitConfig(
     global_padding='same',				layer_type='bottleneck',				depths=(3, 4, 9),				out_features=['stage3'],				embedding_dynamic_padding=UpperCamelCase__,				)
 __A  :		str       =			ViTHybridConfig(backbone_config=UpperCamelCase__,				image_size=384,				num_labels=1000			)
 __A  :		Union[str, Any]       =			False

 # load original model from timm
 __A  :		List[Any]       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __A  :		Optional[int]       =			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase__			)
 __A  :		List[str]       =			create_rename_keys(UpperCamelCase__,				UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __A  :		List[Any]       =			ViTHybridModel(UpperCamelCase__			).eval()
 else:
  __A  :		int       =			ViTHybridForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # create image processor
 __A  :		Tuple       =			create_transform(**resolve_data_config({},				model=UpperCamelCase__			)			)
 __A  :		Union[str, Any]       =			transform.transforms

 __A  :		str       =			{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 __A  :		List[str]       =			ViTHybridImageProcessor(
     do_resize=UpperCamelCase__,				size={'shortest_edge': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=UpperCamelCase__,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},				do_normalize=UpperCamelCase__,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

 __A  :		Optional[Any]       =			prepare_img()
 __A  :		Any       =			transform(UpperCamelCase__			).unsqueeze(0			)
 __A  :		Dict       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

 # verify pixel values
 assert torch.allclose(UpperCamelCase__,				UpperCamelCase__			)

 # verify logits
 with torch.no_grad():
  __A  :		Union[str, Any]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

 print('Predicted class:',				logits.argmax(-1			).item()			)
 if base_model:
  __A  :		Tuple       =			timm_model.forward_features(UpperCamelCase__			)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCamelCase__,				outputs.pooler_output,				atol=1E-3			)
 else:
  __A  :		Any       =			timm_model(UpperCamelCase__			)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__,				outputs.logits,				atol=1E-3			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
  processor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  print(f"""Pushing model and processor to the hub {vit_name}"""			)
  model.push_to_hub(f"""ybelkada/{vit_name}"""			)
  processor.push_to_hub(f"""ybelkada/{vit_name}"""			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--vit_name',
     default='vit_base_r50_s16_384',
     type=str,
     help='Name of the hybrid ViT 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.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
 )

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






