

'''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__ (						a__   ,					a__=False   ,					a__=False   ,					a__="data2vec."		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   """backbone.""" if is_semantic else """"""

     __SCREAMING_SNAKE_CASE						     =   []
     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."		):




     """simple docstring"""


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

          __SCREAMING_SNAKE_CASE						     =   in_proj_weight[
              : config.hidden_size, :
          ]
          __SCREAMING_SNAKE_CASE						     =   q_bias
          __SCREAMING_SNAKE_CASE						     =   in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __SCREAMING_SNAKE_CASE						     =   in_proj_weight[
              -config.hidden_size :, :
          ]
          __SCREAMING_SNAKE_CASE						     =   v_bias

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

          __SCREAMING_SNAKE_CASE						     =   gamma_a
          __SCREAMING_SNAKE_CASE						     =   gamma_a

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

               __SCREAMING_SNAKE_CASE						     =   table
               __SCREAMING_SNAKE_CASE						     =   index







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   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      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     def load_state_dict(a__   ,					a__   ,					a__=""   ,					a__="relative_position_index"		):
          __SCREAMING_SNAKE_CASE						     =   []
          __SCREAMING_SNAKE_CASE						     =   []
          __SCREAMING_SNAKE_CASE						     =   []
          # copy state_dict so _load_from_state_dict can modify it
          __SCREAMING_SNAKE_CASE						     =   getattr(a__   ,					"""_metadata"""   ,					a__		)
          __SCREAMING_SNAKE_CASE						     =   state_dict.copy()
          if metadata is not None:
               __SCREAMING_SNAKE_CASE						     =   metadata

          def load(a__   ,					a__=""		):
               __SCREAMING_SNAKE_CASE						     =   {} 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						     =   []
          __SCREAMING_SNAKE_CASE						     =   []
          for key in missing_keys:
               __SCREAMING_SNAKE_CASE						     =   True
               for ignore_key in ignore_missing.split("""|"""		):
                    if ignore_key in key:
                         __SCREAMING_SNAKE_CASE						     =   False
                         break
               if keep_flag:
                    warn_missing_keys.append(a__		)
               else:
                    ignore_missing_keys.append(a__		)

          __SCREAMING_SNAKE_CASE						     =   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						     =   {
         """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						     =   create_model(
         """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""   ,					**a__   ,					)
     __SCREAMING_SNAKE_CASE						     =   model.patch_embed.patch_size
     __SCREAMING_SNAKE_CASE						     =   (args.input_size // patch_size[0], args.input_size // patch_size[1])
     __SCREAMING_SNAKE_CASE						     =   torch.load(args.beit_checkpoint   ,					map_location="""cpu"""		)

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

     __SCREAMING_SNAKE_CASE						     =   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						     =   checkpoint_model[key]
               __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   rel_pos_bias.size()
               __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   model.state_dict()[key].size()
               __SCREAMING_SNAKE_CASE						     =   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__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   get_args()

     __SCREAMING_SNAKE_CASE						     =   """ft1k""" in args.hf_checkpoint_name
     __SCREAMING_SNAKE_CASE						     =   """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						     =   DataaVecVisionConfig()
     if is_finetuned:
          __SCREAMING_SNAKE_CASE						     =   True
          __SCREAMING_SNAKE_CASE						     =   False
          __SCREAMING_SNAKE_CASE						     =   True
          __SCREAMING_SNAKE_CASE						     =   10_00

          __SCREAMING_SNAKE_CASE						     =   """huggingface/label-files"""
          __SCREAMING_SNAKE_CASE						     =   """imagenet-1k-id2label.json"""
          __SCREAMING_SNAKE_CASE						     =   json.load(open(hf_hub_download(a__   ,					a__   ,					repo_type="""dataset"""		)   ,					"""r"""		)		)
          __SCREAMING_SNAKE_CASE						     =   {int(a__		): v for k, v in idalabel.items()}
          __SCREAMING_SNAKE_CASE						     =   idalabel
          __SCREAMING_SNAKE_CASE						     =   {v: k for k, v in idalabel.items()}
     else:
          __SCREAMING_SNAKE_CASE						     =   False
          __SCREAMING_SNAKE_CASE						     =   True
          __SCREAMING_SNAKE_CASE						     =   False

     if is_large:
          __SCREAMING_SNAKE_CASE						     =   10_24
          __SCREAMING_SNAKE_CASE						     =   40_96
          __SCREAMING_SNAKE_CASE						     =   24
          __SCREAMING_SNAKE_CASE						     =   16

     # 2. Load Beit model
     __SCREAMING_SNAKE_CASE						     =   load_beit_model(a__   ,					a__   ,					a__		)
     orig_model.eval()

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

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

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          __SCREAMING_SNAKE_CASE						     =   DataaVecVisionForImageClassification(a__		)
          hf_model.eval()
          __SCREAMING_SNAKE_CASE						     =   False
          __SCREAMING_SNAKE_CASE						     =   """data2vec_vision."""
     else:
          __SCREAMING_SNAKE_CASE						     =   DataaVecVisionModel(a__		)
          hf_model.eval()
          __SCREAMING_SNAKE_CASE						     =   True
          __SCREAMING_SNAKE_CASE						     =   """"""

     __SCREAMING_SNAKE_CASE						     =   create_rename_keys(a__   ,					hf_prefix=a__   ,					has_lm_head=a__		)
     __SCREAMING_SNAKE_CASE						     =   orig_model.state_dict()
     for src, dest in rename_keys:
          __SCREAMING_SNAKE_CASE						     =   state_dict.pop(a__		)
          __SCREAMING_SNAKE_CASE						     =   val

     read_in_q_k_v(a__   ,					a__   ,					hf_prefix=a__   ,					has_lm_head=a__		)
     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   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						     =   hf_model(a__		)

     __SCREAMING_SNAKE_CASE						     =   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

     print(F'max_absolute_diff = {max_absolute_diff}'		)
     __SCREAMING_SNAKE_CASE						     =   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      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   1

     while True:
          if (
              sorted(str(a__		)		)
              == sorted(str(2 * i		)		)
              == sorted(str(3 * i		)		)
              == sorted(str(4 * i		)		)
              == sorted(str(5 * i		)		)
              == sorted(str(6 * i		)		)
          ):
               return i

          i += 1


if __name__ == "__main__":
  print(solution())






