code: 
  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}""")
  
  
style_context: 
  
  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"
  
  