
import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[int]			,  **SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     return RunBeamCommand(
         args.dataset			,  args.name			,  args.cache_dir			,  args.beam_pipeline_options			,  args.data_dir			,  args.all_configs			,  args.save_info or args.save_infos			,  args.ignore_verifications			,  args.force_redownload			,  **SCREAMING_SNAKE_CASE_			,  )


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    @staticmethod
    def    snake_case_       (   a):
         lowercase__					:     List[str]   = parser.add_parser('run_beam'		,     help='Run a Beam dataset processing pipeline')
         run_beam_parser.add_argument('dataset'		,     type=a		,     help='Name of the dataset to download')
         run_beam_parser.add_argument('--name'		,     type=a		,     default=a		,     help='Dataset config name')
         run_beam_parser.add_argument(
             '--cache_dir'		,     type=a		,     default=a		,     help='Cache directory where the datasets are stored'		,     )
         run_beam_parser.add_argument(
             '--beam_pipeline_options'		,     type=a		,     default=''		,     help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'		,     )
         run_beam_parser.add_argument(
             '--data_dir'		,     type=a		,     default=a		,     help='Can be used to specify a manual directory to get the files from'		,     )
         run_beam_parser.add_argument('--all_configs'		,     action='store_true'		,     help='Test all dataset configurations')
         run_beam_parser.add_argument('--save_info'		,     action='store_true'		,     help='Save the dataset infos file')
         run_beam_parser.add_argument(
             '--ignore_verifications'		,     action='store_true'		,     help='Run the test without checksums and splits checks')
         run_beam_parser.add_argument('--force_redownload'		,     action='store_true'		,     help='Force dataset redownload')
         # aliases
         run_beam_parser.add_argument('--save_infos'		,     action='store_true'		,     help='alias for save_info')
         run_beam_parser.set_defaults(func=a)





    def __init__(   self		,     a		,     a		,     a		,     a		,     a		,     a		,     a		,     a		,     a		,     **a		,     ):
         lowercase__					:     List[Any]   = dataset
         lowercase__					:     List[Any]   = name
         lowercase__					:     str   = cache_dir
         lowercase__					:     Any   = beam_pipeline_options
         lowercase__					:     Dict   = data_dir
         lowercase__					:     Dict   = all_configs
         lowercase__					:     Optional[int]   = save_infos
         lowercase__					:     Any   = ignore_verifications
         lowercase__					:     str   = force_redownload
         lowercase__					:     Union[str, Any]   = config_kwargs





    def    snake_case_       (   self):
         import apache_beam as beam

         if self._name is not None and self._all_configs:
              print('Both parameters `name` and `all_configs` can\'t be used at once.')
              exit(1)
         lowercase__					:     Optional[int]   = self._dataset, self._name
         lowercase__					:     Dict   = dataset_module_factory(a)
         lowercase__					:     Tuple   = import_main_class(dataset_module.module_path)
         lowercase__					:     List[DatasetBuilder]   = []
         if self._beam_pipeline_options:
              lowercase__					:     Union[str, Any]   = beam.options.pipeline_options.PipelineOptions(
                  flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(',') if opt])
         else:
              lowercase__					:     Any   = None
         if self._all_configs and len(builder_cls.BUILDER_CONFIGS) > 0:
              for builder_config in builder_cls.BUILDER_CONFIGS:
                   builders.append(
                       builder_cls(
                           config_name=builder_config.name		,     data_dir=self._data_dir		,     hash=dataset_module.hash		,     beam_options=a		,     cache_dir=self._cache_dir		,     base_path=dataset_module.builder_kwargs.get('base_path')		,     ))
         else:
              builders.append(
                  builder_cls(
                      config_name=a		,     data_dir=self._data_dir		,     beam_options=a		,     cache_dir=self._cache_dir		,     base_path=dataset_module.builder_kwargs.get('base_path')		,     **self._config_kwargs		,     ))

         for builder in builders:
              builder.download_and_prepare(
                  download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                  if not self._force_redownload
                  else DownloadMode.FORCE_REDOWNLOAD		,     download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH)		,     verification_mode=VerificationMode.NO_CHECKS
                  if self._ignore_verifications
                  else VerificationMode.ALL_CHECKS		,     try_from_hf_gcs=a		,     )
              if self._save_infos:
                   builder._save_infos()

         print('Apache beam run successful.')

         # If save_infos=True, the dataset infos file is created next to the loaded module file.
         # Let's move it to the original directory of the dataset script, to allow the user to
         # upload them on S3 at the same time afterwards.
         if self._save_infos:
              lowercase__					:     Tuple   = os.path.join(builder_cls.get_imported_module_dir()		,     config.DATASETDICT_INFOS_FILENAME)

              lowercase__					:     str   = Path(a).name + '.py'

              lowercase__					:     Optional[int]   = os.path.join(a		,     a)
              if os.path.isfile(a):
                   lowercase__					:     Optional[Any]   = os.path.dirname(a)
              elif os.path.isfile(a):
                   lowercase__					:     str   = path
              else:  # in case of a remote dataset
                   print(f"""Dataset Infos file saved at {dataset_infos_path}""")
                   exit(1)

              # Move datasetinfo back to the user
              lowercase__					:     Dict   = os.path.join(a		,     config.DATASETDICT_INFOS_FILENAME)
              copyfile(a		,     a)
              print(f"""Dataset Infos file saved at {user_dataset_infos_path}""")




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]="data2vec."    ):



     '''simple docstring'''





     lowercase__					:     str   = 'backbone.' if is_semantic else ''

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
             (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
             (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
         ]    )

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', f"""{hf_prefix}embeddings.mask_token"""),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]    )
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]    )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', f"""{hf_prefix}pooler.layernorm.weight"""),
                  ('fc_norm.bias', f"""{hf_prefix}pooler.layernorm.bias"""),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]    )

     return rename_keys

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					Dict=False			,  SCREAMING_SNAKE_CASE_	:					List[str]="data2vec_vision."    ):



     '''simple docstring'''





     for i in range(config.num_hidden_layers    ):
          lowercase__					:     Optional[int]   = 'backbone.' if is_semantic else ''
          # queries, keys and values
          lowercase__					:     List[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""    )
          lowercase__					:     Optional[int]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""    )

          lowercase__					:     Union[str, Any]   = in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase__					:     List[str]   = q_bias
          lowercase__					:     Tuple   = in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase__					:     Union[str, Any]   = in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase__					:     Union[str, Any]   = v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""    )
          lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""    )

          lowercase__					:     List[str]   = gamma_a
          lowercase__					:     Dict   = gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               lowercase__					:     Tuple   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""    )
               lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""    )

               lowercase__					:     int   = table
               lowercase__					:     List[str]   = index

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = argparse.ArgumentParser(
         'Convert Data2VecVision to HF for image classification and pretraining'			,  add_help=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--hf_checkpoint_name'			,  type=SCREAMING_SNAKE_CASE_    )
     parser.add_argument('--input_size'			,  default=224			,  type=SCREAMING_SNAKE_CASE_			,  help='images input size'    )
     parser.add_argument('--beit_checkpoint'			,  default=''			,  help='beit checkpoint'    )

     return parser.parse_args()

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





     def load_state_dict(SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""			,  SCREAMING_SNAKE_CASE_	:					List[Any]="relative_position_index"    ):
          lowercase__					:     str   = []
          lowercase__					:     str   = []
          lowercase__					:     Dict   = []
          # copy state_dict so _load_from_state_dict can modify it
          lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  '_metadata'			,  SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Optional[int]   = state_dict.copy()
          if metadata is not None:
               lowercase__					:     Dict   = metadata

          def load(SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""    ):
               lowercase__					:     Optional[Any]   = {} if metadata is None else metadata.get(prefix[:-1]			,  {}    )
               module._load_from_state_dict(
                   SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
               for name, child in module._modules.items():
                    if child is not None:
                         load(SCREAMING_SNAKE_CASE_			,  prefix + name + '.'    )

          load(SCREAMING_SNAKE_CASE_			,  prefix=SCREAMING_SNAKE_CASE_    )

          lowercase__					:     List[Any]   = []
          lowercase__					:     Dict   = []
          for key in missing_keys:
               lowercase__					:     Any   = True
               for ignore_key in ignore_missing.split('|'    ):
                    if ignore_key in key:
                         lowercase__					:     Union[str, Any]   = False
                         break
               if keep_flag:
                    warn_missing_keys.append(SCREAMING_SNAKE_CASE_    )
               else:
                    ignore_missing_keys.append(SCREAMING_SNAKE_CASE_    )

          lowercase__					:     Tuple   = warn_missing_keys

          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print(
                   'Ignored weights of {} not initialized from pretrained model: {}'.format(
                       model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
          if len(SCREAMING_SNAKE_CASE_    ) > 0:
               print('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     lowercase__					:     int   = {
         'pretrained': False,
         'use_shared_rel_pos_bias': True,
         'use_abs_pos_emb': False,
         'init_values': 0.1,
     }

     if is_finetuned:
          model_kwargs.update(
              {
                  'num_classes': 1_000,
                  'use_mean_pooling': True,
                  'init_scale': 0.001,
                  'use_rel_pos_bias': True,
              }    )

     lowercase__					:     Dict   = create_model(
         'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'			,  **SCREAMING_SNAKE_CASE_			,  )
     lowercase__					:     int   = model.patch_embed.patch_size
     lowercase__					:     int   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
     lowercase__					:     str   = torch.load(args.beit_checkpoint			,  map_location='cpu'    )

     print(f"""Load ckpt from {args.beit_checkpoint}"""    )
     lowercase__					:     int   = None
     for model_key in ("model", "module"):
          if model_key in checkpoint:
               lowercase__					:     Tuple   = checkpoint[model_key]
               print(f"""Load state_dict by model_key = {model_key}"""    )
               break

     lowercase__					:     Optional[Any]   = list(checkpoint_model.keys()    )
     for key in all_keys:
          if "relative_position_index" in key:
               checkpoint_model.pop(SCREAMING_SNAKE_CASE_    )

          if "relative_position_bias_table" in key:
               lowercase__					:     Any   = checkpoint_model[key]
               lowercase__   ,				lowercase__					:     List[Any]   = rel_pos_bias.size()
               lowercase__   ,				lowercase__					:     Optional[int]   = model.state_dict()[key].size()
               lowercase__					:     Dict   = model.patch_embed.patch_shape
               if dst_patch_shape[0] != dst_patch_shape[1]:
                    raise NotImplementedError()

     load_state_dict(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  prefix=''    )

     return model

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     List[str]   = get_args()

     lowercase__					:     Union[str, Any]   = 'ft1k' in args.hf_checkpoint_name
     lowercase__					:     Dict   = 'large' in args.hf_checkpoint_name

     if is_finetuned:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
          # into this folder.
          import modeling_finetune  # noqa: F401
     else:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
          # into this folder
          # IMPORTANT: Note that for now we've only converted the down-stream
          # model and not the full pretrained model. This means for the integration
          # test you need to add a `return x` after the following line:
          # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
          # to make the integration test pass.
          import modeling_cyclical  # noqa: F401

     # 1. Create model config
     lowercase__					:     Optional[Any]   = DataaVecVisionConfig()
     if is_finetuned:
          lowercase__					:     int   = True
          lowercase__					:     Any   = False
          lowercase__					:     Union[str, Any]   = True
          lowercase__					:     Union[str, Any]   = 1_000

          lowercase__					:     Any   = 'huggingface/label-files'
          lowercase__					:     Any   = 'imagenet-1k-id2label.json'
          lowercase__					:     Union[str, Any]   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )			,  'r'    )    )
          lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
          lowercase__					:     Optional[int]   = idalabel
          lowercase__					:     Optional[int]   = {v: k for k, v in idalabel.items()}
     else:
          lowercase__					:     Dict   = False
          lowercase__					:     int   = True
          lowercase__					:     List[str]   = False

     if is_large:
          lowercase__					:     List[str]   = 1_024
          lowercase__					:     Union[str, Any]   = 4_096
          lowercase__					:     Union[str, Any]   = 24
          lowercase__					:     Optional[int]   = 16

     # 2. Load Beit model
     lowercase__					:     List[Any]   = load_beit_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
     orig_model.eval()

     # 3. Forward Beit model
     lowercase__					:     Optional[Any]   = BeitImageProcessor(size=config.image_size			,  do_center_crop=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Tuple   = Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'    )
     lowercase__					:     Tuple   = image_processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
     lowercase__					:     List[Any]   = encoding['pixel_values']

     lowercase__					:     List[Any]   = (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
          lowercase__					:     List[str]   = orig_model(*SCREAMING_SNAKE_CASE_    )

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          lowercase__					:     Optional[int]   = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     Union[str, Any]   = False
          lowercase__					:     Dict   = 'data2vec_vision.'
     else:
          lowercase__					:     Tuple   = DataaVecVisionModel(SCREAMING_SNAKE_CASE_    )
          hf_model.eval()
          lowercase__					:     int   = True
          lowercase__					:     Tuple   = ''

     lowercase__					:     Tuple   = create_rename_keys(SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__					:     str   = orig_model.state_dict()
     for src, dest in rename_keys:
          lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
          lowercase__					:     Dict   = val

     read_in_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
     lowercase__   ,				lowercase__					:     List[Any]   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
     print('HF missing'			,  SCREAMING_SNAKE_CASE_    )
     print('HF unexpected_keys'			,  SCREAMING_SNAKE_CASE_    )

     # 5. Forward HF Data2VecVision model
     with torch.no_grad():
          lowercase__					:     Optional[int]   = hf_model(SCREAMING_SNAKE_CASE_    )

     lowercase__					:     Optional[int]   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

     # 6. Compare
     lowercase__					:     Dict   = torch.max(torch.abs(hf_output - orig_model_output    )    ).item()

     print(f"""max_absolute_diff = {max_absolute_diff}"""    )
     lowercase__					:     Dict   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  atol=1E-3    )
     print('Do both models output the same tensors?'			,  '🔥' if success else '💩'    )
     if not success:
          raise Exception('Something went wRoNg'    )

     # 7. Save
     print(f"""Saving to {args.hf_checkpoint_name}"""    )
     hf_model.save_pretrained(args.hf_checkpoint_name    )
     image_processor.save_pretrained(args.hf_checkpoint_name    )


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

