





'''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  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_=False      ,   lowerCAmelCase_=False      ,   lowerCAmelCase_="data2vec."						):





      """simple docstring"""



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

      _snake_case		:						Optional[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     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_=False      ,   lowerCAmelCase_=False      ,   lowerCAmelCase_="data2vec_vision."						):





      """simple docstring"""



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

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

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

            _snake_case		:						Optional[Any]         =  gamma_a
            _snake_case		:						List[str]         =  gamma_a

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

                  _snake_case		:						Union[str, Any]         =  table
                  _snake_case		:						Tuple         =  index




def  _a     (				):





      """simple docstring"""



      _snake_case		:						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  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



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

            def load(lowerCAmelCase_      ,   lowerCAmelCase_=""						):
                  _snake_case		:						List[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_						)

            _snake_case		:						Dict         =  []
            _snake_case		:						Dict         =  []
            for key in missing_keys:
                  _snake_case		:						Union[str, Any]         =  True
                  for ignore_key in ignore_missing.split('''|'''						):
                        if ignore_key in key:
                              _snake_case		:						List[str]         =  False
                              break
                  if keep_flag:
                        warn_missing_keys.append(lowerCAmelCase_						)
                  else:
                        ignore_missing_keys.append(lowerCAmelCase_						)

            _snake_case		:						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_						)						)

      _snake_case		:						Optional[int]         =  {
          '''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''': 0.001,
                    '''use_rel_pos_bias''': True,
                }						)

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

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

      _snake_case		:						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:
                  _snake_case		:						Optional[Any]         =  checkpoint_model[key]
                  _snake_case  ,				_snake_case		:						List[str]         =  rel_pos_bias.size()
                  _snake_case  ,				_snake_case		:						int         =  model.state_dict()[key].size()
                  _snake_case		:						List[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  _a     (				):





      """simple docstring"""



      _snake_case		:						Any         =  get_args()

      _snake_case		:						List[str]         =  '''ft1k''' in args.hf_checkpoint_name
      _snake_case		:						List[str]         =  '''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
      _snake_case		:						Optional[Any]         =  DataaVecVisionConfig()
      if is_finetuned:
            _snake_case		:						Any         =  True
            _snake_case		:						str         =  False
            _snake_case		:						Any         =  True
            _snake_case		:						Optional[int]         =  1_000

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

      if is_large:
            _snake_case		:						List[str]         =  1_024
            _snake_case		:						int         =  4_096
            _snake_case		:						int         =  24
            _snake_case		:						Optional[int]         =  16

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

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

      _snake_case		:						Dict         =  (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            _snake_case		:						List[str]         =  orig_model(*lowerCAmelCase_						)

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

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

      read_in_q_k_v(lowerCAmelCase_      ,   lowerCAmelCase_      ,   hf_prefix=lowerCAmelCase_      ,   has_lm_head=lowerCAmelCase_						)
      _snake_case  ,				_snake_case		:						Optional[Any]         =  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():
            _snake_case		:						Union[str, Any]         =  hf_model(lowerCAmelCase_						)

      _snake_case		:						Tuple         =  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

      print(f'''max_absolute_diff = {max_absolute_diff}'''						)
      _snake_case		:						List[Any]         =  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 __future__ import annotations




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  sorted(lowerCAmelCase_						)
      _snake_case		:						Any         =  len(lowerCAmelCase_						)
      _snake_case		:						List[str]         =  length >> 1
      return (
          (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
          if length % 2 == 0
          else sorted_list[mid_index]
      )




def  _a     (				):





      """simple docstring"""



      import doctest

      doctest.testmod()


if __name__ == "__main__":
   main()


