





"""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_	(  lowercase_   :							List[str] ,      lowercase_   :							Any=False ,      lowercase_   :							Union[str, Any]=False ,      lowercase_   :							List[str]="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       '''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE :			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 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							Optional[int]=False ,      lowercase_   :							Any=False ,      lowercase_   :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       '''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE :			Any												=       state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE :			int												=       state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE :			List[Any]												=       state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE :			Any												=       q_bias
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE :			List[Any]												=       v_bias

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

  __SCREAMING_SNAKE_CASE :			Dict												=       gamma_a
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE :			int												=       state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE :			Any												=       state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE :			str												=       table
   __SCREAMING_SNAKE_CASE :			Dict												=       index




def 		lowerCAmelCase_	(  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining''' ,      add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name''' ,      type=lowercase_		)
 parser.add_argument('''--input_size''' ,      default=224 ,      type=lowercase_ ,      help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint''' ,      default='''''' ,      help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Optional[int] ,      lowercase_   :							str		):

 '''simple docstring'''






 def load_state_dict(lowercase_   :							str ,      lowercase_   :							Dict ,      lowercase_   :							Dict="" ,      lowercase_   :							Optional[Any]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE :			Tuple												=       []
  __SCREAMING_SNAKE_CASE :			Dict												=       []
  __SCREAMING_SNAKE_CASE :			List[Any]												=       []
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE :			Any												=       getattr(lowercase_ ,      '''_metadata''' ,      lowercase_		)
  __SCREAMING_SNAKE_CASE :			Any												=       state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       metadata

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

  load(lowercase_ ,      prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE :			str												=       []
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       []
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE :			Any												=       True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE :			Optional[int]												=       False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       warn_missing_keys

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

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       {
     '''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,
      }		)

 __SCREAMING_SNAKE_CASE :			Dict												=       create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224''' ,      **lowercase_ ,      )
 __SCREAMING_SNAKE_CASE :			List[Any]												=       model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE :			int												=       (args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch.load(args.beit_checkpoint ,      map_location='''cpu'''		)

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

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

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

 load_state_dict(lowercase_ ,      lowercase_ ,      prefix=''''''		)

 return model




def 		lowerCAmelCase_	(  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       get_args()

 __SCREAMING_SNAKE_CASE :			Dict												=       '''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE :			List[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
 __SCREAMING_SNAKE_CASE :			Dict												=       DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
  __SCREAMING_SNAKE_CASE :			Any												=       False
  __SCREAMING_SNAKE_CASE :			List[str]												=       True
  __SCREAMING_SNAKE_CASE :			Dict												=       1000

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

 if is_large:
  __SCREAMING_SNAKE_CASE :			str												=       1024
  __SCREAMING_SNAKE_CASE :			Dict												=       4096
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       24
  __SCREAMING_SNAKE_CASE :			str												=       16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE :			Dict												=       load_beit_model(lowercase_ ,      lowercase_ ,      lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE :			Tuple												=       BeitImageProcessor(size=config.image_size ,      do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE :			int												=       Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE :			Any												=       image_processor(images=lowercase_ ,      return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE :			int												=       encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE :			Tuple												=       (pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE :			str												=       orig_model(*lowercase_		)

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

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

 read_in_q_k_v(lowercase_ ,      lowercase_ ,      hf_prefix=lowercase_ ,      has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       hf_model.load_state_dict(lowercase_ ,      strict=lowercase_		)
 print('''HF missing''' ,      lowercase_		)
 print('''HF unexpected_keys''' ,      lowercase_		)

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

 __SCREAMING_SNAKE_CASE :			List[Any]												=       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.allclose(lowercase_ ,      lowercase_ ,      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_torch_available, is_vision_available


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

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

