code: 
  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 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Optional[int]=False   , _UpperCAmelCase     :      Optional[Any]=False   , _UpperCAmelCase     :      Any="data2vec."     )      -> str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  int    			=      "backbone." if is_semantic else ""
  
  						_lowercase					:  int    			=      []
  						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 		UpperCamelCase    (					_UpperCAmelCase     :      int   , _UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      Optional[Any]=False   , _UpperCAmelCase     :      List[str]=False   , _UpperCAmelCase     :      Union[str, Any]="data2vec_vision."     )      -> List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						for i in range(config.num_hidden_layers     ):
  												_lowercase					:  Any    			=      "backbone." if is_semantic else ""
  												# queries, keys and values
  												_lowercase					:  List[str]    			=      state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""     )
  												_lowercase					:  Union[str, Any]    			=      state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""     )
  												_lowercase					:  str    			=      state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""     )
  
  												_lowercase					:  Optional[int]    			=      in_proj_weight[
  												    : config.hidden_size, :
  												]
  												_lowercase					:  List[str]    			=      q_bias
  												_lowercase					:  List[str]    			=      in_proj_weight[
  												    config.hidden_size : config.hidden_size * 2, :
  												]
  												_lowercase					:  List[Any]    			=      in_proj_weight[
  												    -config.hidden_size :, :
  												]
  												_lowercase					:  int    			=      v_bias
  
  												# gamma_1 and gamma_2
  												# we call them lambda because otherwise they are renamed when using .from_pretrained
  												_lowercase					:  int    			=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
  												_lowercase					:  Any    			=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )
  
  												_lowercase					:  int    			=      gamma_a
  												_lowercase					:  List[Any]    			=      gamma_a
  
  												# relative_position bias table + index
  												if not has_lm_head:
  																		# each layer has its own relative position bias
  																		_lowercase					:  str    			=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""     )
  																		_lowercase					:  Union[str, Any]    			=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""     )
  
  																		_lowercase					:  Dict    			=      table
  																		_lowercase					:  Dict    			=      index
  
  
  
  def 		UpperCamelCase    (					)      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Optional[int]    			=      argparse.ArgumentParser(
  						    "Convert Data2VecVision to HF for image classification and pretraining"   , add_help=_UpperCAmelCase     )
  						parser.add_argument("--hf_checkpoint_name"   , type=_UpperCAmelCase     )
  						parser.add_argument("--input_size"   , default=224   , type=_UpperCAmelCase   , help="images input size"     )
  						parser.add_argument("--beit_checkpoint"   , default=""   , help="beit checkpoint"     )
  
  						return parser.parse_args()
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      List[str]     )      -> Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						def load_state_dict(_UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      List[Any]   , _UpperCAmelCase     :      Tuple=""   , _UpperCAmelCase     :      str="relative_position_index"     ):
  												_lowercase					:  Optional[int]    			=      []
  												_lowercase					:  List[str]    			=      []
  												_lowercase					:  Tuple    			=      []
  												# copy state_dict so _load_from_state_dict can modify it
  												_lowercase					:  Tuple    			=      getattr(_UpperCAmelCase   , "_metadata"   , _UpperCAmelCase     )
  												_lowercase					:  List[str]    			=      state_dict.copy()
  												if metadata is not None:
  																		_lowercase					:  List[str]    			=      metadata
  
  												def load(_UpperCAmelCase     :      int   , _UpperCAmelCase     :      List[str]=""     ):
  																		_lowercase					:  int    			=      {} if metadata is None else metadata.get(prefix[:-1]   , {}     )
  																		module._load_from_state_dict(
  																		    _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase     )
  																		for name, child in module._modules.items():
  																								if child is not None:
  																														load(_UpperCAmelCase   , prefix + name + "."     )
  
  												load(_UpperCAmelCase   , prefix=_UpperCAmelCase     )
  
  												_lowercase					:  Optional[Any]    			=      []
  												_lowercase					:  Optional[Any]    			=      []
  												for key in missing_keys:
  																		_lowercase					:  Any    			=      True
  																		for ignore_key in ignore_missing.split("|"     ):
  																								if ignore_key in key:
  																														_lowercase					:  int    			=      False
  																														break
  																		if keep_flag:
  																								warn_missing_keys.append(_UpperCAmelCase     )
  																		else:
  																								ignore_missing_keys.append(_UpperCAmelCase     )
  
  												_lowercase					:  List[str]    			=      warn_missing_keys
  
  												if len(_UpperCAmelCase     ) > 0:
  																		print(
  																		    "Weights of {} not initialized from pretrained model: {}".format(
  																		        model.__class__.__name__   , _UpperCAmelCase     )     )
  												if len(_UpperCAmelCase     ) > 0:
  																		print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__   , _UpperCAmelCase     )     )
  												if len(_UpperCAmelCase     ) > 0:
  																		print(
  																		    "Ignored weights of {} not initialized from pretrained model: {}".format(
  																		        model.__class__.__name__   , _UpperCAmelCase     )     )
  												if len(_UpperCAmelCase     ) > 0:
  																		print("\n".join(_UpperCAmelCase     )     )
  
  						_lowercase					:  Optional[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": 1000,
  												        "use_mean_pooling": True,
  												        "init_scale": 0.001,
  												        "use_rel_pos_bias": True,
  												    }     )
  
  						_lowercase					:  Optional[int]    			=      create_model(
  						    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"   , **_UpperCAmelCase   , )
  						_lowercase					:  Any    			=      model.patch_embed.patch_size
  						_lowercase					:  Optional[int]    			=      (args.input_size // patch_size[0], args.input_size // patch_size[1])
  						_lowercase					:  List[str]    			=      torch.load(args.beit_checkpoint   , map_location="cpu"     )
  
  						print(f"""Load ckpt from {args.beit_checkpoint}"""     )
  						_lowercase					:  Optional[int]    			=      None
  						for model_key in ("model", "module"):
  												if model_key in checkpoint:
  																		_lowercase					:  List[Any]    			=      checkpoint[model_key]
  																		print(f"""Load state_dict by model_key = {model_key}"""     )
  																		break
  
  						_lowercase					:  List[Any]    			=      list(checkpoint_model.keys()     )
  						for key in all_keys:
  												if "relative_position_index" in key:
  																		checkpoint_model.pop(_UpperCAmelCase     )
  
  												if "relative_position_bias_table" in key:
  																		_lowercase					:  Tuple    			=      checkpoint_model[key]
  																		_lowercase			,						_lowercase					:  Union[str, Any]    			=      rel_pos_bias.size()
  																		_lowercase			,						_lowercase					:  Tuple    			=      model.state_dict()[key].size()
  																		_lowercase					:  List[Any]    			=      model.patch_embed.patch_shape
  																		if dst_patch_shape[0] != dst_patch_shape[1]:
  																								raise NotImplementedError()
  
  						load_state_dict(_UpperCAmelCase   , _UpperCAmelCase   , prefix=""     )
  
  						return model
  
  
  
  def 		UpperCamelCase    (					)      -> Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Any    			=      get_args()
  
  						_lowercase					:  List[Any]    			=      "ft1k" in args.hf_checkpoint_name
  						_lowercase					:  int    			=      "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					:  Dict    			=      DataaVecVisionConfig()
  						if is_finetuned:
  												_lowercase					:  Dict    			=      True
  												_lowercase					:  Dict    			=      False
  												_lowercase					:  List[Any]    			=      True
  												_lowercase					:  Tuple    			=      1000
  
  												_lowercase					:  str    			=      "huggingface/label-files"
  												_lowercase					:  str    			=      "imagenet-1k-id2label.json"
  												_lowercase					:  str    			=      json.load(open(hf_hub_download(_UpperCAmelCase   , _UpperCAmelCase   , repo_type="dataset"     )   , "r"     )     )
  												_lowercase					:  Union[str, Any]    			=      {int(_UpperCAmelCase     ): v for k, v in idalabel.items()}
  												_lowercase					:  Any    			=      idalabel
  												_lowercase					:  str    			=      {v: k for k, v in idalabel.items()}
  						else:
  												_lowercase					:  List[Any]    			=      False
  												_lowercase					:  Any    			=      True
  												_lowercase					:  Dict    			=      False
  
  						if is_large:
  												_lowercase					:  str    			=      1024
  												_lowercase					:  Optional[Any]    			=      4096
  												_lowercase					:  List[Any]    			=      24
  												_lowercase					:  Any    			=      16
  
  						# 2. Load Beit model
  						_lowercase					:  int    			=      load_beit_model(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase     )
  						orig_model.eval()
  
  						# 3. Forward Beit model
  						_lowercase					:  Dict    			=      BeitImageProcessor(size=config.image_size   , do_center_crop=_UpperCAmelCase     )
  						_lowercase					:  List[str]    			=      Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"     )
  						_lowercase					:  List[str]    			=      image_processor(images=_UpperCAmelCase   , return_tensors="pt"     )
  						_lowercase					:  Tuple    			=      encoding["pixel_values"]
  
  						_lowercase					:  Optional[Any]    			=      (pixel_values,) if is_finetuned else (pixel_values, None)
  						with torch.no_grad():
  												_lowercase					:  Optional[int]    			=      orig_model(*_UpperCAmelCase     )
  
  						# 4. Load HF Data2VecVision model
  						if is_finetuned:
  												_lowercase					:  Dict    			=      DataaVecVisionForImageClassification(_UpperCAmelCase     )
  												hf_model.eval()
  												_lowercase					:  str    			=      False
  												_lowercase					:  Tuple    			=      "data2vec_vision."
  						else:
  												_lowercase					:  List[str]    			=      DataaVecVisionModel(_UpperCAmelCase     )
  												hf_model.eval()
  												_lowercase					:  Tuple    			=      True
  												_lowercase					:  int    			=      ""
  
  						_lowercase					:  Optional[int]    			=      create_rename_keys(_UpperCAmelCase   , hf_prefix=_UpperCAmelCase   , has_lm_head=_UpperCAmelCase     )
  						_lowercase					:  List[str]    			=      orig_model.state_dict()
  						for src, dest in rename_keys:
  												_lowercase					:  Union[str, Any]    			=      state_dict.pop(_UpperCAmelCase     )
  												_lowercase					:  Dict    			=      val
  
  						read_in_q_k_v(_UpperCAmelCase   , _UpperCAmelCase   , hf_prefix=_UpperCAmelCase   , has_lm_head=_UpperCAmelCase     )
  						_lowercase			,						_lowercase					:  Any    			=      hf_model.load_state_dict(_UpperCAmelCase   , strict=_UpperCAmelCase     )
  						print("HF missing"   , _UpperCAmelCase     )
  						print("HF unexpected_keys"   , _UpperCAmelCase     )
  
  						# 5. Forward HF Data2VecVision model
  						with torch.no_grad():
  												_lowercase					:  Tuple    			=      hf_model(_UpperCAmelCase     )
  
  						_lowercase					:  Any    			=      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
  						# 6. Compare
  						_lowercase					:  List[str]    			=      torch.max(torch.abs(hf_output - orig_model_output     )     ).item()
  
  						print(f"""max_absolute_diff = {max_absolute_diff}"""     )
  						_lowercase					:  int    			=      torch.allclose(_UpperCAmelCase   , _UpperCAmelCase   , 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"
  
  
  
  
  
style_context: 
  import os
  
  
  
  def 		UpperCamelCase    (					)      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Tuple    			=      0
  						_lowercase					:  List[Any]    			=      0
  						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
  												_lowercase					:  Any    			=      str(file.readlines()[0]     )
  												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )
  
  						name.sort()
  						for i in range(len(_UpperCAmelCase     )     ):
  												for j in name[i]:
  																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
  												total_sum += (i + 1) * temp_sum
  												_lowercase					:  Tuple    			=      0
  						return total_sum
  
  
  if __name__ == "__main__":
  							print(solution())
  
  
  
  
  