


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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=False,    _UpperCamelCase=False,    _UpperCamelCase="data2vec."     )    ->Optional[Any]:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		'''backbone.''' if is_semantic else ''''''

      lowercase			:   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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=False,    _UpperCamelCase=False,    _UpperCamelCase="data2vec_vision."     )    ->Union[str, Any]:







      """simple docstring"""

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

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

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

            lowercase			:   Optional[Any]					   =		gamma_a
            lowercase			:   Optional[int]					   =		gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  lowercase			:   Union[str, Any]					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""     )
                  lowercase			:   Dict					   =		state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""     )

                  lowercase			:   List[str]					   =		table
                  lowercase			:   int					   =		index




def 			__lowercase	(						)    ->List[Any]:







      """simple docstring"""

      lowercase			:   Any					   =		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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->List[str]:







      """simple docstring"""

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

            def load(_UpperCamelCase,    _UpperCamelCase=""     ):
                  lowercase			:   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     )

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

            lowercase			:   List[Any]					   =		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     )     )

      lowercase			:   List[str]					   =		{
          '''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''': 0.0_0_1,
                    '''use_rel_pos_bias''': True,
                }     )

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

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

      lowercase			:   List[Any]					   =		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:
                  lowercase			:   Dict					   =		checkpoint_model[key]
                  lowercase   ,			lowercase			:   Any					   =		rel_pos_bias.size()
                  lowercase   ,			lowercase			:   List[Any]					   =		model.state_dict()[key].size()
                  lowercase			:   Dict					   =		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 			__lowercase	(						)    ->Optional[Any]:







      """simple docstring"""

      lowercase			:   Any					   =		get_args()

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

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

      if is_large:
            lowercase			:   str					   =		1024
            lowercase			:   Optional[Any]					   =		4096
            lowercase			:   Optional[Any]					   =		24
            lowercase			:   Any					   =		16

      # 2. Load Beit model
      lowercase			:   str					   =		load_beit_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
      orig_model.eval()

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

      lowercase			:   Any					   =		(pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            lowercase			:   List[str]					   =		orig_model(*_UpperCamelCase     )

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

      lowercase			:   Union[str, Any]					   =		create_rename_keys(_UpperCamelCase,    hf_prefix=_UpperCamelCase,    has_lm_head=_UpperCamelCase     )
      lowercase			:   List[Any]					   =		orig_model.state_dict()
      for src, dest in rename_keys:
            lowercase			:   str					   =		state_dict.pop(_UpperCamelCase     )
            lowercase			:   Union[str, Any]					   =		val

      read_in_q_k_v(_UpperCamelCase,    _UpperCamelCase,    hf_prefix=_UpperCamelCase,    has_lm_head=_UpperCamelCase     )
      lowercase   ,			lowercase			:   int					   =		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():
            lowercase			:   str					   =		hf_model(_UpperCamelCase     )

      lowercase			:   List[str]					   =		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      lowercase			:   Union[str, Any]					   =		torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

      print(f"""max_absolute_diff = {max_absolute_diff}"""     )
      lowercase			:   Union[str, 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"





__a   				=      [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->bool:







      """simple docstring"""

      return 0 <= row < rows and 0 <= col < cols




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->int:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		len(_UpperCamelCase     )
      lowercase			:   str					   =		len(mat[0]     )
      if is_safe(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     ) and (row, col) not in seen and mat[row][col] == 1:
            seen.add((row, col)     )
            return (
                1
                + depth_first_search(row + 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(row - 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(_UpperCamelCase,    col + 1,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(_UpperCamelCase,    col - 1,    _UpperCamelCase,    _UpperCamelCase     )
            )
      else:
            return 0




def 			__lowercase	(						_UpperCamelCase     )    ->int:







      """simple docstring"""

      lowercase			:   set					   =		set()

      lowercase			:   Tuple					   =		0
      for row, line in enumerate(_UpperCamelCase     ):
            for col, item in enumerate(_UpperCamelCase     ):
                  if item == 1 and (row, col) not in seen:
                        # Maximizing the area
                        lowercase			:   Tuple					   =		max(_UpperCamelCase,    depth_first_search(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )     )
      return max_area


if __name__ == "__main__":
  import doctest

  print(find_max_area(matrix))  # Output -> 6
  doctest.testmod()

