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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=False,    _UpperCamelCase=False,    _UpperCamelCase="data2vec."     )    ->Optional[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		'''backbone.''' if is_semantic else ''''''
  
        lowercase			:   List[Any]					   =		[]
        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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=False,    _UpperCamelCase=False,    _UpperCamelCase="data2vec_vision."     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for i in range(config.num_hidden_layers     ):
              lowercase			:   Union[str, Any]					   =		'''backbone.''' if is_semantic else ''''''
              # queries, keys and values
              lowercase			:   Tuple					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""     )
              lowercase			:   Optional[Any]					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""     )
              lowercase			:   Optional[Any]					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""     )
  
              lowercase			:   Optional[Any]					   =		in_proj_weight[
                  : config.hidden_size, :
              ]
              lowercase			:   Tuple					   =		q_bias
              lowercase			:   Any					   =		in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase			:   str					   =		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			:   Union[str, Any]					   =		state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
              lowercase			:   List[str]					   =		state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )
  
              lowercase			:   Optional[Any]					   =		gamma_a
              lowercase			:   Optional[int]					   =		gamma_a
  
              # relative_position bias table + index
              if not has_lm_head:
                    # each layer has its own relative position bias
                    lowercase			:   Union[str, Any]					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""     )
                    lowercase			:   Dict					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""     )
  
                    lowercase			:   List[str]					   =		table
                    lowercase			:   int					   =		index
  
  
  
  
  
  def 			__lowercase	(						)    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        def load_state_dict(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase="",    _UpperCamelCase="relative_position_index"     ):
              lowercase			:   Union[str, Any]					   =		[]
              lowercase			:   str					   =		[]
              lowercase			:   Tuple					   =		[]
              # copy state_dict so _load_from_state_dict can modify it
              lowercase			:   int					   =		getattr(_UpperCamelCase,    '''_metadata''',    _UpperCamelCase     )
              lowercase			:   Dict					   =		state_dict.copy()
              if metadata is not None:
                    lowercase			:   List[str]					   =		metadata
  
              def load(_UpperCamelCase,    _UpperCamelCase=""     ):
                    lowercase			:   Tuple					   =		{} 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			:   Dict					   =		[]
              lowercase			:   Tuple					   =		[]
              for key in missing_keys:
                    lowercase			:   Tuple					   =		True
                    for ignore_key in ignore_missing.split('''|'''     ):
                          if ignore_key in key:
                                lowercase			:   List[Any]					   =		False
                                break
                    if keep_flag:
                          warn_missing_keys.append(_UpperCamelCase     )
                    else:
                          ignore_missing_keys.append(_UpperCamelCase     )
  
              lowercase			:   List[Any]					   =		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			:   List[str]					   =		{
            '''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''': 0.0_0_1,
                      '''use_rel_pos_bias''': True,
                  }     )
  
        lowercase			:   int					   =		create_model(
            '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224''',    **_UpperCamelCase,    )
        lowercase			:   List[Any]					   =		model.patch_embed.patch_size
        lowercase			:   Any					   =		(args.input_size // patch_size[0], args.input_size // patch_size[1])
        lowercase			:   Tuple					   =		torch.load(args.beit_checkpoint,    map_location='''cpu'''     )
  
        print(f"""Load ckpt from {args.beit_checkpoint}"""     )
        lowercase			:   List[Any]					   =		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			:   Dict					   =		checkpoint_model[key]
                    lowercase			:   Any					   =		rel_pos_bias.size()
                    lowercase			:   List[Any]					   =		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(_UpperCamelCase,    _UpperCamelCase,    prefix=''''''     )
  
        return model
  
  
  
  
  
  def 			__lowercase	(						)    ->Optional[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		get_args()
  
        lowercase			:   str					   =		'''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			:   str					   =		DataaVecVisionConfig()
        if is_finetuned:
              lowercase			:   Optional[int]					   =		True
              lowercase			:   Optional[int]					   =		False
              lowercase			:   Tuple					   =		True
              lowercase			:   List[str]					   =		1000
  
              lowercase			:   Any					   =		'''huggingface/label-files'''
              lowercase			:   Tuple					   =		'''imagenet-1k-id2label.json'''
              lowercase			:   Optional[Any]					   =		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			:   Optional[int]					   =		idalabel
              lowercase			:   Dict					   =		{v: k for k, v in idalabel.items()}
        else:
              lowercase			:   Optional[int]					   =		False
              lowercase			:   str					   =		True
              lowercase			:   List[str]					   =		False
  
        if is_large:
              lowercase			:   str					   =		1024
              lowercase			:   Optional[Any]					   =		4096
              lowercase			:   Optional[Any]					   =		24
              lowercase			:   Any					   =		16
  
        # 2. Load Beit model
        lowercase			:   str					   =		load_beit_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
        orig_model.eval()
  
        # 3. Forward Beit model
        lowercase			:   int					   =		BeitImageProcessor(size=config.image_size,    do_center_crop=_UpperCamelCase     )
        lowercase			:   List[str]					   =		Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''     )
        lowercase			:   str					   =		image_processor(images=_UpperCamelCase,    return_tensors='''pt'''     )
        lowercase			:   str					   =		encoding['''pixel_values''']
  
        lowercase			:   Any					   =		(pixel_values,) if is_finetuned else (pixel_values, None)
        with torch.no_grad():
              lowercase			:   List[str]					   =		orig_model(*_UpperCamelCase     )
  
        # 4. Load HF Data2VecVision model
        if is_finetuned:
              lowercase			:   str					   =		DataaVecVisionForImageClassification(_UpperCamelCase     )
              hf_model.eval()
              lowercase			:   Optional[Any]					   =		False
              lowercase			:   Union[str, Any]					   =		'''data2vec_vision.'''
        else:
              lowercase			:   Tuple					   =		DataaVecVisionModel(_UpperCamelCase     )
              hf_model.eval()
              lowercase			:   Tuple					   =		True
              lowercase			:   Any					   =		''''''
  
        lowercase			:   Union[str, Any]					   =		create_rename_keys(_UpperCamelCase,    hf_prefix=_UpperCamelCase,    has_lm_head=_UpperCamelCase     )
        lowercase			:   List[Any]					   =		orig_model.state_dict()
        for src, dest in rename_keys:
              lowercase			:   str					   =		state_dict.pop(_UpperCamelCase     )
              lowercase			:   Union[str, Any]					   =		val
  
        read_in_q_k_v(_UpperCamelCase,    _UpperCamelCase,    hf_prefix=_UpperCamelCase,    has_lm_head=_UpperCamelCase     )
        lowercase			:   int					   =		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			:   str					   =		hf_model(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
        # 6. Compare
        lowercase			:   Union[str, Any]					   =		torch.max(torch.abs(hf_output - orig_model_output     )     ).item()
  
        print(f"""max_absolute_diff = {max_absolute_diff}"""     )
        lowercase			:   Union[str, Any]					   =		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 argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
      datefmt='''%m/%d/%Y %H:%M:%S''',
      level=logging.INFO,
  )
  __a   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        np.random.seed(args.seed     )
        torch.manual_seed(args.seed     )
        if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
        parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
        parser.add_argument(
            '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()
  
        logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
        set_seed(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )
  
        # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
        # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
        model.to(args.device     )
  
        if args.fpaa:
              model.half()
  
        logger.info(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]
  
              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  