





"""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     (							a_  :	Optional[Any]				,     a_  :	List[Any]=False				,     a_  :	Any=False				,     a_  :	Union[str, Any]="data2vec."							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''backbone.''' if is_semantic else ''''''

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

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

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

            __SCREAMING_SNAKE_CASE     :str	       =						gamma_a
            __SCREAMING_SNAKE_CASE     :Dict	       =						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     :Union[str, Any]	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''							)

                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						table
                  __SCREAMING_SNAKE_CASE     :Any	       =						index



def   __lowerCamelCase     (							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :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=2_24				,     type=a_				,     help='''images input size'''							)
      parser.add_argument('''--beit_checkpoint'''				,     default=''''''				,     help='''beit checkpoint'''							)

      return parser.parse_args()



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

            def load(a_  :	Union[str, Any]				,     a_  :	str=""							):
                  __SCREAMING_SNAKE_CASE     :str	       =						{} 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_							)

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

            __SCREAMING_SNAKE_CASE     :Optional[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_							)							)

      __SCREAMING_SNAKE_CASE     :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''': 10_00,
                    '''use_mean_pooling''': True,
                    '''init_scale''': 0.001,
                    '''use_rel_pos_bias''': True,
                }							)

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

      print(f'''Load ckpt from {args.beit_checkpoint}'''							)
      __SCREAMING_SNAKE_CASE     :int	       =						None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  __SCREAMING_SNAKE_CASE     :Any	       =						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(a_							)

            if "relative_position_bias_table" in key:
                  __SCREAMING_SNAKE_CASE     :int	       =						checkpoint_model[key]
                  __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :int	       =						rel_pos_bias.size()
                  __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :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(a_				,     a_				,     prefix=''''''							)

      return model



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Any	       =						get_args()

      __SCREAMING_SNAKE_CASE     :Any	       =						'''ft1k''' in args.hf_checkpoint_name
      __SCREAMING_SNAKE_CASE     :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     :Tuple	       =						DataaVecVisionConfig()
      if is_finetuned:
            __SCREAMING_SNAKE_CASE     :Any	       =						True
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						False
            __SCREAMING_SNAKE_CASE     :Tuple	       =						True
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						10_00

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

      if is_large:
            __SCREAMING_SNAKE_CASE     :Any	       =						10_24
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						40_96
            __SCREAMING_SNAKE_CASE     :int	       =						24
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						16

      # 2. Load Beit model
      __SCREAMING_SNAKE_CASE     :Tuple	       =						load_beit_model(a_				,     a_				,     a_							)
      orig_model.eval()

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

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

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

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

      read_in_q_k_v(a_				,     a_				,     hf_prefix=a_				,     has_lm_head=a_							)
      __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[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():
            __SCREAMING_SNAKE_CASE     :int	       =						hf_model(a_							)

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

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

      print(f'''max_absolute_diff = {max_absolute_diff}'''							)
      __SCREAMING_SNAKE_CASE     :Dict	       =						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"






"""simple docstring"""
def   __lowerCamelCase     (							a_  :	str							)    ->   int:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
      for plain_chr in plain_text:
            __SCREAMING_SNAKE_CASE     :List[str]	       =						(
                ord(a_							) + (hash_value << 6) + (hash_value << 16) - hash_value
            )
      return hash_value