






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     lowerCamelCase__				(   __lowerCamelCase      :					Dict  ,		__lowerCamelCase      :					Any=False  ,		__lowerCamelCase      :					Union[str, Any]=False  ,		__lowerCamelCase      :					Optional[Any]="data2vec."		):
  __UpperCAmelCase							:     List[Any]									=  """backbone.""" if is_semantic else """"""

  __UpperCAmelCase							:     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     lowerCamelCase__				(   __lowerCamelCase      :					int  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					Any=False  ,		__lowerCamelCase      :					Optional[int]=False  ,		__lowerCamelCase      :					Optional[int]="data2vec_vision."		):
  for i in range(config.num_hidden_layers		):
    __UpperCAmelCase							:     List[Any]									=  """backbone.""" if is_semantic else """"""
    # queries, keys and values
    __UpperCAmelCase							:     int									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""		)
    __UpperCAmelCase							:     Dict									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""		)
    __UpperCAmelCase							:     Any									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""		)

    __UpperCAmelCase							:     Optional[int]									=  in_proj_weight[
        : config.hidden_size, :
    ]
    __UpperCAmelCase							:     Any									=  q_bias
    __UpperCAmelCase							:     int									=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __UpperCAmelCase							:     List[str]									=  in_proj_weight[
        -config.hidden_size :, :
    ]
    __UpperCAmelCase							:     Dict									=  v_bias

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

    __UpperCAmelCase							:     int									=  gamma_a
    __UpperCAmelCase							:     int									=  gamma_a

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

      __UpperCAmelCase							:     Any									=  table
      __UpperCAmelCase							:     List[Any]									=  index






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     List[Any]									=  argparse.ArgumentParser(
      """Convert Data2VecVision to HF for image classification and pretraining"""  ,		add_help=__lowerCamelCase		)
  parser.add_argument("""--hf_checkpoint_name"""  ,		type=__lowerCamelCase		)
  parser.add_argument("""--input_size"""  ,		default=224  ,		type=__lowerCamelCase  ,		help="""images input size"""		)
  parser.add_argument("""--beit_checkpoint"""  ,		default=""""""  ,		help="""beit checkpoint"""		)

  return parser.parse_args()






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

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

    load(__lowerCamelCase  ,		prefix=__lowerCamelCase		)

    __UpperCAmelCase							:     List[Any]									=  []
    __UpperCAmelCase							:     Dict									=  []
    for key in missing_keys:
      __UpperCAmelCase							:     int									=  True
      for ignore_key in ignore_missing.split("""|"""		):
        if ignore_key in key:
          __UpperCAmelCase							:     Optional[Any]									=  False
          break
      if keep_flag:
        warn_missing_keys.append(__lowerCamelCase		)
      else:
        ignore_missing_keys.append(__lowerCamelCase		)

    __UpperCAmelCase							:     Union[str, Any]									=  warn_missing_keys

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

  __UpperCAmelCase							:     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""": 0.0_0_1,
            """use_rel_pos_bias""": True,
        }		)

  __UpperCAmelCase							:     Tuple									=  create_model(
      """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""  ,		**__lowerCamelCase  ,		)
  __UpperCAmelCase							:     Dict									=  model.patch_embed.patch_size
  __UpperCAmelCase							:     str									=  (args.input_size // patch_size[0], args.input_size // patch_size[1])
  __UpperCAmelCase							:     Optional[Any]									=  torch.load(args.beit_checkpoint  ,		map_location="""cpu"""		)

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

  __UpperCAmelCase							:     int									=  list(checkpoint_model.keys()		)
  for key in all_keys:
    if "relative_position_index" in key:
      checkpoint_model.pop(__lowerCamelCase		)

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

  load_state_dict(__lowerCamelCase  ,		__lowerCamelCase  ,		prefix=""""""		)

  return model






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Any									=  get_args()

  __UpperCAmelCase							:     Optional[int]									=  """ft1k""" in args.hf_checkpoint_name
  __UpperCAmelCase							:     Union[str, Any]									=  """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
  __UpperCAmelCase							:     List[str]									=  DataaVecVisionConfig()
  if is_finetuned:
    __UpperCAmelCase							:     Dict									=  True
    __UpperCAmelCase							:     Tuple									=  False
    __UpperCAmelCase							:     int									=  True
    __UpperCAmelCase							:     Union[str, Any]									=  1000

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

  if is_large:
    __UpperCAmelCase							:     int									=  1024
    __UpperCAmelCase							:     Tuple									=  4096
    __UpperCAmelCase							:     int									=  24
    __UpperCAmelCase							:     List[Any]									=  16

  # 2. Load Beit model
  __UpperCAmelCase							:     int									=  load_beit_model(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)
  orig_model.eval()

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

  __UpperCAmelCase							:     int									=  (pixel_values,) if is_finetuned else (pixel_values, None)
  with torch.no_grad():
    __UpperCAmelCase							:     Union[str, Any]									=  orig_model(*__lowerCamelCase		)

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    __UpperCAmelCase							:     str									=  DataaVecVisionForImageClassification(__lowerCamelCase		)
    hf_model.eval()
    __UpperCAmelCase							:     Tuple									=  False
    __UpperCAmelCase							:     Tuple									=  """data2vec_vision."""
  else:
    __UpperCAmelCase							:     Any									=  DataaVecVisionModel(__lowerCamelCase		)
    hf_model.eval()
    __UpperCAmelCase							:     Any									=  True
    __UpperCAmelCase							:     List[Any]									=  """"""

  __UpperCAmelCase							:     Optional[Any]									=  create_rename_keys(__lowerCamelCase  ,		hf_prefix=__lowerCamelCase  ,		has_lm_head=__lowerCamelCase		)
  __UpperCAmelCase							:     List[Any]									=  orig_model.state_dict()
  for src, dest in rename_keys:
    __UpperCAmelCase							:     str									=  state_dict.pop(__lowerCamelCase		)
    __UpperCAmelCase							:     Tuple									=  val

  read_in_q_k_v(__lowerCamelCase  ,		__lowerCamelCase  ,		hf_prefix=__lowerCamelCase  ,		has_lm_head=__lowerCamelCase		)
  __UpperCAmelCase							:     Tuple									=  hf_model.load_state_dict(__lowerCamelCase  ,		strict=__lowerCamelCase		)
  print("""HF missing"""  ,		__lowerCamelCase		)
  print("""HF unexpected_keys"""  ,		__lowerCamelCase		)

  # 5. Forward HF Data2VecVision model
  with torch.no_grad():
    __UpperCAmelCase							:     Any									=  hf_model(__lowerCamelCase		)

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

  # 6. Compare
  __UpperCAmelCase							:     Tuple									=  torch.max(torch.abs(hf_output - orig_model_output		)		).item()

  print(f"""max_absolute_diff = {max_absolute_diff}"""		)
  __UpperCAmelCase							:     List[Any]									=  torch.allclose(__lowerCamelCase  ,		__lowerCamelCase  ,		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"










def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int		):
  for index, item in enumerate(__lowerCamelCase		):
    if item == target:
      return index
  return -1






def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  if not (0 <= high < len(__lowerCamelCase		) and 0 <= low < len(__lowerCamelCase		)):
    raise Exception("""Invalid upper or lower bound!"""		)
  if high < low:
    return -1
  if sequence[low] == target:
    return low
  if sequence[high] == target:
    return high
  return rec_linear_search(__lowerCamelCase  ,		low + 1  ,		high - 1  ,		__lowerCamelCase		)


if __name__ == "__main__":
       a		:	List[str]      					=			input("Enter numbers separated by comma:\n").strip()
       a		:	Tuple      					=			[int(item.strip()) for item in user_input.split(",")]

       a		:	List[str]      					=			int(input("Enter a single number to be found in the list:\n").strip())
       a		:	Optional[Any]      					=			linear_search(sequence, target)
       if result != -1:
              print(f"""linear_search({sequence}, {target}) = {result}""")
       else:
              print(f"""{target} was not found in {sequence}""")


