


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_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_=False				,						SCREAMING_SNAKE_CASE_=False				,						SCREAMING_SNAKE_CASE_="data2vec."     ):


  '''simple docstring'''

  lowerCamelCase			: Tuple   =    "backbone." if is_semantic else ""

  lowerCamelCase			: Tuple   =    []
  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_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_=False				,						SCREAMING_SNAKE_CASE_=False				,						SCREAMING_SNAKE_CASE_="data2vec_vision."     ):


  '''simple docstring'''

  for i in range(config.num_hidden_layers     ):
    lowerCamelCase			: Union[str, Any]   =    "backbone." if is_semantic else ""
    # queries, keys and values
    lowerCamelCase			: int   =    state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""     )
    lowerCamelCase			: int   =    state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""     )
    lowerCamelCase			: Tuple   =    state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""     )

    lowerCamelCase			: Tuple   =    in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase			: Union[str, Any]   =    q_bias
    lowerCamelCase			: int   =    in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase			: Any   =    in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase			: List[str]   =    v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCamelCase			: Dict   =    state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
    lowerCamelCase			: Optional[int]   =    state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )

    lowerCamelCase			: Union[str, Any]   =    gamma_a
    lowerCamelCase			: Tuple   =    gamma_a

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

      lowerCamelCase			: Union[str, Any]   =    table
      lowerCamelCase			: str   =    index






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Dict   =    argparse.ArgumentParser(
      "Convert Data2VecVision to HF for image classification and pretraining"				,						add_help=snake_case__     )
  parser.add_argument("--hf_checkpoint_name"				,						type=snake_case__     )
  parser.add_argument("--input_size"				,						default=224				,						type=snake_case__				,						help="images input size"     )
  parser.add_argument("--beit_checkpoint"				,						default=""				,						help="beit checkpoint"     )

  return parser.parse_args()






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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

    def load(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_=""     ):
      lowerCamelCase			: Tuple   =    {} if metadata is None else metadata.get(prefix[:-1]				,						{}     )
      module._load_from_state_dict(
          snake_case__				,						snake_case__				,						snake_case__				,						snake_case__				,						snake_case__				,						snake_case__				,						snake_case__     )
      for name, child in module._modules.items():
        if child is not None:
          load(snake_case__				,						prefix + name + "."     )

    load(snake_case__				,						prefix=snake_case__     )

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

    lowerCamelCase			: str   =    warn_missing_keys

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

  lowerCamelCase			: Dict   =    {
      "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,
        }     )

  lowerCamelCase			: str   =    create_model(
      "beit_large_patch16_224" if is_large else "beit_base_patch16_224"				,						**snake_case__				,						)
  lowerCamelCase			: str   =    model.patch_embed.patch_size
  lowerCamelCase			: str   =    (args.input_size // patch_size[0], args.input_size // patch_size[1])
  lowerCamelCase			: Union[str, Any]   =    torch.load(args.beit_checkpoint				,						map_location="cpu"     )

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

  lowerCamelCase			: Dict   =    list(checkpoint_model.keys()     )
  for key in all_keys:
    if "relative_position_index" in key:
      checkpoint_model.pop(snake_case__     )

    if "relative_position_bias_table" in key:
      lowerCamelCase			: Tuple   =    checkpoint_model[key]
      lowerCamelCase   ,     lowerCamelCase			: Dict   =    rel_pos_bias.size()
      lowerCamelCase   ,     lowerCamelCase			: Dict   =    model.state_dict()[key].size()
      lowerCamelCase			: Optional[int]   =    model.patch_embed.patch_shape
      if dst_patch_shape[0] != dst_patch_shape[1]:
        raise NotImplementedError()

  load_state_dict(snake_case__				,						snake_case__				,						prefix=""     )

  return model






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Dict   =    get_args()

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

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

  if is_large:
    lowerCamelCase			: Dict   =    1024
    lowerCamelCase			: Union[str, Any]   =    4096
    lowerCamelCase			: Any   =    24
    lowerCamelCase			: Optional[int]   =    16

  # 2. Load Beit model
  lowerCamelCase			: Optional[Any]   =    load_beit_model(snake_case__				,						snake_case__				,						snake_case__     )
  orig_model.eval()

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

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

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    lowerCamelCase			: str   =    DataaVecVisionForImageClassification(snake_case__     )
    hf_model.eval()
    lowerCamelCase			: int   =    False
    lowerCamelCase			: Tuple   =    "data2vec_vision."
  else:
    lowerCamelCase			: Any   =    DataaVecVisionModel(snake_case__     )
    hf_model.eval()
    lowerCamelCase			: Dict   =    True
    lowerCamelCase			: Optional[int]   =    ""

  lowerCamelCase			: Optional[Any]   =    create_rename_keys(snake_case__				,						hf_prefix=snake_case__				,						has_lm_head=snake_case__     )
  lowerCamelCase			: Union[str, Any]   =    orig_model.state_dict()
  for src, dest in rename_keys:
    lowerCamelCase			: Dict   =    state_dict.pop(snake_case__     )
    lowerCamelCase			: Optional[int]   =    val

  read_in_q_k_v(snake_case__				,						snake_case__				,						hf_prefix=snake_case__				,						has_lm_head=snake_case__     )
  lowerCamelCase   ,     lowerCamelCase			: List[Any]   =    hf_model.load_state_dict(snake_case__				,						strict=snake_case__     )
  print("HF missing"				,						snake_case__     )
  print("HF unexpected_keys"				,						snake_case__     )

  # 5. Forward HF Data2VecVision model
  with torch.no_grad():
    lowerCamelCase			: int   =    hf_model(snake_case__     )

  lowerCamelCase			: List[str]   =    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

  # 6. Compare
  lowerCamelCase			: List[str]   =    torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

  print(f"""max_absolute_diff = {max_absolute_diff}"""     )
  lowerCamelCase			: List[str]   =    torch.allclose(snake_case__				,						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"







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''HubertForCTC''',
       '''HubertForSequenceClassification''',
       '''HubertModel''',
       '''HubertPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFHubertForCTC''',
       '''TFHubertModel''',
       '''TFHubertPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



