code: 
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					bool    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if not inplace:
            lowercase__					:     str   = ma + ma
       else:
            ma += ma
  
       return ma
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					List[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = -1 * len(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     int   = list(range(len(tensor.shape[:zero_index]    )    )    )
       return tensor.permute(first_inds + [zero_index + i for i in inds]    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       return t.reshape(t.shape[:-no_dims] + (-1,)    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					float = 1E-4    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[Any]   = mask.expand(*value.shape    )
       return torch.sum(mask * value			,  dim=SCREAMING_SNAKE_CASE_    ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_			,  dim=SCREAMING_SNAKE_CASE_    ))
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					torch.types.Number = 2.3125			,  SCREAMING_SNAKE_CASE_	:					torch.types.Number = 21.6875			,  SCREAMING_SNAKE_CASE_	:					int = 64    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = torch.linspace(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  no_bins - 1			,  device=pts.device    )
       lowercase__					:     str   = torch.sqrt(torch.sum((pts.unsqueeze(-2    ) - pts.unsqueeze(-3    )) ** 2			,  dim=-1    )    )
       return torch.bucketize(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[list], Any]			,  SCREAMING_SNAKE_CASE_	:					List[dict]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = dicts[0]
       lowercase__					:     str   = {}
       for k, v in first.items():
            lowercase__					:     List[Any]   = [d[k] for d in dicts]
            if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     Optional[Any]   = dict_multimap(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
            else:
                 lowercase__					:     Union[str, Any]   = fn(SCREAMING_SNAKE_CASE_    )
  
       return new_dict
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					torch.Tensor    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = v_bins.view(((1,) * len(x.shape    )) + (len(SCREAMING_SNAKE_CASE_    ),)    )
       lowercase__					:     Tuple   = x[..., None] - reshaped_bins
       lowercase__					:     Any   = torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_    )			,  dim=-1    )
       return nn.functional.one_hot(SCREAMING_SNAKE_CASE_			,  num_classes=len(SCREAMING_SNAKE_CASE_    )    ).float()
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					torch.Tensor			,  SCREAMING_SNAKE_CASE_	:					int = 0			,  SCREAMING_SNAKE_CASE_	:					int = 0    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[Union[slice, torch.Tensor]]   = []
       for i, s in enumerate(data.shape[:no_batch_dims]    ):
            lowercase__					:     int   = torch.arange(SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Dict   = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape    ) - i - 1)))    )
            ranges.append(SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     List[Union[slice, torch.Tensor]]   = [slice(SCREAMING_SNAKE_CASE_    ) for _ in range(len(data.shape    ) - no_batch_dims    )]
       lowercase__					:     Optional[Any]   = inds
       ranges.extend(SCREAMING_SNAKE_CASE_    )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(SCREAMING_SNAKE_CASE_    )]
  
  
  snake_case_    						=				TypeVar('''T''')
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[T], Any]			,  SCREAMING_SNAKE_CASE_	:					Dict[Any, Union[dict, list, tuple, T]]			,  SCREAMING_SNAKE_CASE_	:					Type[T]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict[Any, Union[dict, list, tuple, Any]]   = {}
       for k, v in dic.items():
            if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     List[Any]   = dict_map(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
            else:
                 lowercase__					:     List[Any]   = tree_map(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       return new_dict
  
  @overload
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[T], Any]			,  SCREAMING_SNAKE_CASE_	:					T			,  SCREAMING_SNAKE_CASE_	:					Type[T]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       ...
  
  @overload
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[T], Any]			,  SCREAMING_SNAKE_CASE_	:					dict			,  SCREAMING_SNAKE_CASE_	:					Type[T]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       ...
  
  @overload
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[T], Any]			,  SCREAMING_SNAKE_CASE_	:					list			,  SCREAMING_SNAKE_CASE_	:					Type[T]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       ...
  
  @overload
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Callable[[T], Any]			,  SCREAMING_SNAKE_CASE_	:					tuple			,  SCREAMING_SNAKE_CASE_	:					Type[T]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       ...
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            return dict_map(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       elif isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            return [tree_map(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) for x in tree]
       elif isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            return tuple(tree_map(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) for x in tree    )
       elif isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            return fn(SCREAMING_SNAKE_CASE_    )
       else:
            print(type(SCREAMING_SNAKE_CASE_    )    )
            raise ValueError('Not supported'    )
  
  snake_case_    						=				partial(tree_map, leaf_type=torch.Tensor)
  
  
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"
  
  