






'''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__   (					__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  List[str]=False						,	__lowerCamelCase    :  Any=False						,	__lowerCamelCase    :  str="data2vec."	):
  __UpperCAmelCase							:			Any     =						"""backbone.""" if is_semantic else """"""

  __UpperCAmelCase							:			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__   (					__lowerCamelCase    :  int						,	__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[Any]=False						,	__lowerCamelCase    :  str=False						,	__lowerCamelCase    :  List[str]="data2vec_vision."	):
  for i in range(config.num_hidden_layers	):
    __UpperCAmelCase							:			List[Any]     =						"""backbone.""" if is_semantic else """"""
    # queries, keys and values
    __UpperCAmelCase							:			Dict     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""	)
    __UpperCAmelCase							:			int     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""	)
    __UpperCAmelCase							:			Any     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""	)

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

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

    __UpperCAmelCase							:			Optional[Any]     =						gamma_a
    __UpperCAmelCase							:			Dict     =						gamma_a

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

      __UpperCAmelCase							:			Dict     =						table
      __UpperCAmelCase							:			List[Any]     =						index






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

    def load(__lowerCamelCase    :  Optional[int]						,	__lowerCamelCase    :  List[Any]=""	):
      __UpperCAmelCase							:			Dict     =						{} 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	)

    __UpperCAmelCase							:			Any     =						[]
    __UpperCAmelCase							:			List[str]     =						[]
    for key in missing_keys:
      __UpperCAmelCase							:			str     =						True
      for ignore_key in ignore_missing.split("""|"""	):
        if ignore_key in key:
          __UpperCAmelCase							:			List[str]     =						False
          break
      if keep_flag:
        warn_missing_keys.append(__lowerCamelCase	)
      else:
        ignore_missing_keys.append(__lowerCamelCase	)

    __UpperCAmelCase							:			Dict     =						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	)	)

  __UpperCAmelCase							:			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,
        }	)

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

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

  __UpperCAmelCase							:			List[str]     =						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:
      __UpperCAmelCase							:			List[str]     =						checkpoint_model[key]
      __UpperCAmelCase							:			int     =						rel_pos_bias.size()
      __UpperCAmelCase							:			Tuple     =						model.state_dict()[key].size()
      __UpperCAmelCase							:			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        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						get_args()

  __UpperCAmelCase							:			str     =						"""ft1k""" in args.hf_checkpoint_name
  __UpperCAmelCase							:			Tuple     =						"""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
  __UpperCAmelCase							:			Any     =						DataaVecVisionConfig()
  if is_finetuned:
    __UpperCAmelCase							:			List[Any]     =						True
    __UpperCAmelCase							:			Union[str, Any]     =						False
    __UpperCAmelCase							:			Tuple     =						True
    __UpperCAmelCase							:			Optional[int]     =						1000

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

  if is_large:
    __UpperCAmelCase							:			Dict     =						1024
    __UpperCAmelCase							:			Dict     =						4096
    __UpperCAmelCase							:			str     =						24
    __UpperCAmelCase							:			List[Any]     =						16

  # 2. Load Beit model
  __UpperCAmelCase							:			Any     =						load_beit_model(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)
  orig_model.eval()

  # 3. Forward Beit model
  __UpperCAmelCase							:			Tuple     =						BeitImageProcessor(size=config.image_size						,	do_center_crop=__lowerCamelCase	)
  __UpperCAmelCase							:			List[str]     =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  __UpperCAmelCase							:			Any     =						image_processor(images=__lowerCamelCase						,	return_tensors="""pt"""	)
  __UpperCAmelCase							:			Optional[Any]     =						encoding["""pixel_values"""]

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

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

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

  read_in_q_k_v(__lowerCamelCase						,	__lowerCamelCase						,	hf_prefix=__lowerCamelCase						,	has_lm_head=__lowerCamelCase	)
  __UpperCAmelCase							:			int     =						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():
    __UpperCAmelCase							:			str     =						hf_model(__lowerCamelCase	)

  __UpperCAmelCase							:			List[Any]     =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

  print(f"""max_absolute_diff = {max_absolute_diff}"""	)
  __UpperCAmelCase							:			List[str]     =						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"










a	: List[str]      =				[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 					a					:



      """simple docstring"""


      def __init__(   self      :    Union[str, Any]	,   __lowercase      :    List[str]      )							->   Optional[int]:
        __UpperCAmelCase							:			Dict     =						name
        __UpperCAmelCase							:			Optional[Any]     =						[]
        __UpperCAmelCase							:			List[Any]     =						[]
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    int      )							->   str:
        self.inbound.append(__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[Any]      )							->   List[str]:
        self.outbound.append(__lowercase      )




      def __repr__(   self      :    Any      )							->   int:
        return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  str=3						,	__lowerCamelCase    :  int=0.8_5	):
  __UpperCAmelCase							:			Optional[int]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			str     =						1

  __UpperCAmelCase							:			Union[str, Any]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			Dict     =						len(node.outbound	)

  for i in range(__lowerCamelCase	):
    print(f"""======= Iteration {i + 1} ======="""	)
    for _, node in enumerate(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound	)
    print(__lowerCamelCase	)






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						list(input("""Enter Names of the Nodes: """	).split()	)

  __UpperCAmelCase							:			Dict     =						[Node(__lowerCamelCase	) for name in names]

  for ri, row in enumerate(__lowerCamelCase	):
    for ci, col in enumerate(__lowerCamelCase	):
      if col == 1:
        nodes[ci].add_inbound(names[ri]	)
        nodes[ri].add_outbound(names[ci]	)

  print("""======= Nodes ======="""	)
  for node in nodes:
    print(__lowerCamelCase	)

  page_rank(__lowerCamelCase	)


if __name__ == "__main__":
       main()


