






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       a__						(  A		,				A=False		,				A=False		,				A="data2vec."					):
   lowercase__						:	Optional[Any]         = """backbone.""" if is_semantic else """"""

   lowercase__						:	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       a__						(  A		,				A		,				A=False		,				A=False		,				A="data2vec_vision."					):
   for i in range(config.num_hidden_layers					):
      lowercase__						:	Union[str, Any]         = """backbone.""" if is_semantic else """"""
      # queries, keys and values
      lowercase__						:	str         = state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''					)
      lowercase__						:	Optional[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[Any]         = in_proj_weight[
          : config.hidden_size, :
      ]
      lowercase__						:	Any         = q_bias
      lowercase__						:	Union[str, Any]         = in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase__						:	int         = 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__						:	Tuple         = state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''					)
      lowercase__						:	int         = state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''					)

      lowercase__						:	Optional[int]         = 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__						:	int         = state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''					)
         lowercase__						:	List[str]         = state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''					)

         lowercase__						:	Optional[Any]         = table
         lowercase__						:	Optional[Any]         = index
def       a__						(  ):
   lowercase__						:	Optional[int]         = argparse.ArgumentParser(
       """Convert Data2VecVision to HF for image classification and pretraining"""		,				add_help=A					)
   parser.add_argument("""--hf_checkpoint_name"""		,				type=A					)
   parser.add_argument("""--input_size"""		,				default=224		,				type=A		,				help="""images input size"""					)
   parser.add_argument("""--beit_checkpoint"""		,				default=""""""		,				help="""beit checkpoint"""					)

   return parser.parse_args()
def       a__						(  A		,				A		,				A					):
   def load_state_dict(A		,				A		,				A=""		,				A="relative_position_index"					):
      lowercase__						:	str         = []
      lowercase__						:	Any         = []
      lowercase__						:	int         = []
      # copy state_dict so _load_from_state_dict can modify it
      lowercase__						:	Union[str, Any]         = getattr(A		,				"""_metadata"""		,				A					)
      lowercase__						:	Any         = state_dict.copy()
      if metadata is not None:
         lowercase__						:	int         = metadata

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

      load(A		,				prefix=A					)

      lowercase__						:	Tuple         = []
      lowercase__						:	Any         = []
      for key in missing_keys:
         lowercase__						:	Optional[int]         = True
         for ignore_key in ignore_missing.split("""|"""					):
            if ignore_key in key:
               lowercase__						:	Union[str, Any]         = False
               break
         if keep_flag:
            warn_missing_keys.append(A					)
         else:
            ignore_missing_keys.append(A					)

      lowercase__						:	List[Any]         = warn_missing_keys

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

   lowercase__						:	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""": 1_000,
              """use_mean_pooling""": True,
              """init_scale""": 0.0_0_1,
              """use_rel_pos_bias""": True,
          }					)

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

   print(F'''Load ckpt from {args.beit_checkpoint}'''					)
   lowercase__						:	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[str]         = list(checkpoint_model.keys()					)
   for key in all_keys:
      if "relative_position_index" in key:
         checkpoint_model.pop(A					)

      if "relative_position_bias_table" in key:
         lowercase__						:	int         = checkpoint_model[key]
         lowercase__     ,					lowercase__						:	List[str]         = rel_pos_bias.size()
         lowercase__     ,					lowercase__						:	Union[str, Any]         = model.state_dict()[key].size()
         lowercase__						:	Any         = model.patch_embed.patch_shape
         if dst_patch_shape[0] != dst_patch_shape[1]:
            raise NotImplementedError()

   load_state_dict(A		,				A		,				prefix=""""""					)

   return model
def       a__						(  ):
   lowercase__						:	List[str]         = get_args()

   lowercase__						:	str         = """ft1k""" in args.hf_checkpoint_name
   lowercase__						:	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
   lowercase__						:	str         = DataaVecVisionConfig()
   if is_finetuned:
      lowercase__						:	Union[str, Any]         = True
      lowercase__						:	Optional[int]         = False
      lowercase__						:	Optional[int]         = True
      lowercase__						:	Optional[int]         = 1_000

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

   if is_large:
      lowercase__						:	Optional[Any]         = 1_024
      lowercase__						:	List[Any]         = 4_096
      lowercase__						:	Any         = 24
      lowercase__						:	Union[str, Any]         = 16

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

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

   lowercase__						:	Optional[int]         = (pixel_values,) if is_finetuned else (pixel_values, None)
   with torch.no_grad():
      lowercase__						:	Optional[int]         = orig_model(*A					)

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

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

   read_in_q_k_v(A		,				A		,				hf_prefix=A		,				has_lm_head=A					)
   lowercase__     ,					lowercase__						:	Union[str, Any]         = hf_model.load_state_dict(A		,				strict=A					)
   print("""HF missing"""		,				A					)
   print("""HF unexpected_keys"""		,				A					)

   # 5. Forward HF Data2VecVision model
   with torch.no_grad():
      lowercase__						:	Dict         = hf_model(A					)

   lowercase__						:	Dict         = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

   # 6. Compare
   lowercase__						:	Any         = torch.max(torch.abs(hf_output - orig_model_output					)					).item()

   print(F'''max_absolute_diff = {max_absolute_diff}'''					)
   lowercase__						:	Optional[int]         = torch.allclose(A		,				A		,				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_torch_available


a    =       {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a    =       [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

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


