




"""simple docstring"""
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			:		Optional[int]  ,	__lowerCAmelCase			:		Tuple=False  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		Dict="data2vec."						)    ->       List[str]:
 _UpperCamelCase      :	List[str]					      =  "backbone." if is_semantic else ""

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

  _UpperCamelCase      :	Tuple					      =  in_proj_weight[
      : config.hidden_size, :
  ]
  _UpperCamelCase      :	Optional[Any]					      =  q_bias
  _UpperCamelCase      :	Optional[Any]					      =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _UpperCamelCase      :	Optional[Any]					      =  in_proj_weight[
      -config.hidden_size :, :
  ]
  _UpperCamelCase      :	Tuple					      =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  _UpperCamelCase      :	str					      =  state_dict.pop(f"{prefix}blocks.{i}.gamma_1"						)
  _UpperCamelCase      :	Tuple					      =  state_dict.pop(f"{prefix}blocks.{i}.gamma_2"						)

  _UpperCamelCase      :	Union[str, Any]					      =  gamma_a
  _UpperCamelCase      :	Union[str, Any]					      =  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      :	Optional[Any]					      =  state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"						)

   _UpperCamelCase      :	str					      =  table
   _UpperCamelCase      :	Dict					      =  index






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

  def load(__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		int=""						):
   _UpperCamelCase      :	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      :	Tuple					      =  []
  _UpperCamelCase      :	Union[str, Any]					      =  []
  for key in missing_keys:
   _UpperCamelCase      :	Union[str, Any]					      =  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      :	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      :	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": 0.001,
          "use_rel_pos_bias": True,
      }						)

 _UpperCamelCase      :	List[Any]					      =  create_model(
     "beit_large_patch16_224" if is_large else "beit_base_patch16_224"  ,	**__lowerCAmelCase  ,	)
 _UpperCamelCase      :	List[str]					      =  model.patch_embed.patch_size
 _UpperCamelCase      :	Union[str, Any]					      =  (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      :	int					      =  None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   _UpperCamelCase      :	Dict					      =  checkpoint[model_key]
   print(f"Load state_dict by model_key = {model_key}"						)
   break

 _UpperCamelCase      :	Any					      =  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      ,	_UpperCamelCase      :	str					      =  rel_pos_bias.size()
   _UpperCamelCase      ,	_UpperCamelCase      :	List[Any]					      =  model.state_dict()[key].size()
   _UpperCamelCase      :	str					      =  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					(					)    ->       Union[str, Any]:
 _UpperCamelCase      :	Any					      =  get_args()

 _UpperCamelCase      :	List[str]					      =  "ft1k" in args.hf_checkpoint_name
 _UpperCamelCase      :	Optional[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      :	Any					      =  DataaVecVisionConfig()
 if is_finetuned:
  _UpperCamelCase      :	Tuple					      =  True
  _UpperCamelCase      :	Optional[Any]					      =  False
  _UpperCamelCase      :	List[str]					      =  True
  _UpperCamelCase      :	List[str]					      =  1000

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

 if is_large:
  _UpperCamelCase      :	List[Any]					      =  1024
  _UpperCamelCase      :	int					      =  4096
  _UpperCamelCase      :	Any					      =  24
  _UpperCamelCase      :	str					      =  16

 # 2. Load Beit model
 _UpperCamelCase      :	Optional[int]					      =  load_beit_model(__lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase						)
 orig_model.eval()

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

 _UpperCamelCase      :	int					      =  (pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  _UpperCamelCase      :	List[str]					      =  orig_model(*__lowerCAmelCase						)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  _UpperCamelCase      :	List[Any]					      =  DataaVecVisionForImageClassification(__lowerCAmelCase						)
  hf_model.eval()
  _UpperCamelCase      :	Optional[int]					      =  False
  _UpperCamelCase      :	str					      =  "data2vec_vision."
 else:
  _UpperCamelCase      :	Any					      =  DataaVecVisionModel(__lowerCAmelCase						)
  hf_model.eval()
  _UpperCamelCase      :	Any					      =  True
  _UpperCamelCase      :	Union[str, Any]					      =  ""

 _UpperCamelCase      :	Any					      =  create_rename_keys(__lowerCAmelCase  ,	hf_prefix=__lowerCAmelCase  ,	has_lm_head=__lowerCAmelCase						)
 _UpperCamelCase      :	Optional[Any]					      =  orig_model.state_dict()
 for src, dest in rename_keys:
  _UpperCamelCase      :	Any					      =  state_dict.pop(__lowerCAmelCase						)
  _UpperCamelCase      :	Optional[int]					      =  val

 read_in_q_k_v(__lowerCAmelCase  ,	__lowerCAmelCase  ,	hf_prefix=__lowerCAmelCase  ,	has_lm_head=__lowerCAmelCase						)
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  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      :	Optional[int]					      =  hf_model(__lowerCAmelCase						)

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

 # 6. Compare
 _UpperCamelCase      :	Optional[int]					      =  torch.max(torch.abs(hf_output - orig_model_output						)						).item()

 print(f"max_absolute_diff = {max_absolute_diff}"						)
 _UpperCamelCase      :	Optional[int]					      =  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"









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



