


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		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple=False				,						UpperCAmelCase_		:	int=False				,						UpperCAmelCase_		:	Optional[Any]="data2vec."		):




      """simple docstring"""





      a		:List[str]											= '''backbone.''' if is_semantic else ''''''

      a		: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		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Tuple=False				,						UpperCAmelCase_		:	str=False				,						UpperCAmelCase_		:	List[str]="data2vec_vision."		):




      """simple docstring"""





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

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

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

            a		:str											= gamma_a
            a		:Union[str, Any]											= gamma_a

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

                  a		:str											= table
                  a		:int											= index
def        __lowerCamelCase		(				):




      """simple docstring"""





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

      return parser.parse_args()
def        __lowerCamelCase		(				UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





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

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

            load(UpperCAmelCase_				,						prefix=UpperCAmelCase_		)

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

            a		:int											= warn_missing_keys

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

      a		: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,
                }		)

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

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

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

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

      load_state_dict(UpperCAmelCase_				,						UpperCAmelCase_				,						prefix=''''''		)

      return model
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:List[str]											= get_args()

      a		:Dict											= '''ft1k''' in args.hf_checkpoint_name
      a		: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
      a		:Any											= DataaVecVisionConfig()
      if is_finetuned:
            a		:Optional[Any]											= True
            a		:Dict											= False
            a		:Optional[Any]											= True
            a		:Tuple											= 1000

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

      if is_large:
            a		:Optional[int]											= 1024
            a		:int											= 4096
            a		:Any											= 24
            a		:int											= 16

      # 2. Load Beit model
      a		:Any											= load_beit_model(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
      orig_model.eval()

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

      a		:List[str]											= (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            a		:Tuple											= orig_model(*UpperCAmelCase_		)

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

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

      read_in_q_k_v(UpperCAmelCase_				,						UpperCAmelCase_				,						hf_prefix=UpperCAmelCase_				,						has_lm_head=UpperCAmelCase_		)
      a							,       a		:Any											= hf_model.load_state_dict(UpperCAmelCase_				,						strict=UpperCAmelCase_		)
      print('''HF missing'''				,						UpperCAmelCase_		)
      print('''HF unexpected_keys'''				,						UpperCAmelCase_		)

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

      a		:Union[str, Any]											= hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

      print(F'''max_absolute_diff = {max_absolute_diff}'''		)
      a		:List[Any]											= torch.allclose(UpperCAmelCase_				,						UpperCAmelCase_				,						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,
)


snake_case       :					Dict        =  {
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					List[str]        =  [
         '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''AlignModel''',
         '''AlignPreTrainedModel''',
         '''AlignTextModel''',
         '''AlignVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     snake_case       :					int        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





