





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__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=False   ,   SCREAMING_SNAKE_CASE__=False   ,   SCREAMING_SNAKE_CASE__="data2vec.")  ->       int:
  __snake_case:		str					    =		"""backbone.""" if is_semantic else """"""

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

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

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

    __snake_case:		int					    =		gamma_a
    __snake_case:		Union[str, Any]					    =		gamma_a

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

      __snake_case:		List[Any]					    =		table
      __snake_case:		int					    =		index







def      A__				(       )  ->       Dict:
  __snake_case:		int					    =		argparse.ArgumentParser(
      """Convert Data2VecVision to HF for image classification and pretraining"""   ,   add_help=SCREAMING_SNAKE_CASE__)
  parser.add_argument("""--hf_checkpoint_name"""   ,   type=SCREAMING_SNAKE_CASE__)
  parser.add_argument("""--input_size"""   ,   default=224   ,   type=SCREAMING_SNAKE_CASE__   ,   help="""images input size""")
  parser.add_argument("""--beit_checkpoint"""   ,   default=""""""   ,   help="""beit checkpoint""")

  return parser.parse_args()







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[Any]:
  def load_state_dict(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=""   ,   SCREAMING_SNAKE_CASE__="relative_position_index"):
    __snake_case:		Any					    =		[]
    __snake_case:		List[Any]					    =		[]
    __snake_case:		int					    =		[]
    # copy state_dict so _load_from_state_dict can modify it
    __snake_case:		Tuple					    =		getattr(SCREAMING_SNAKE_CASE__   ,   """_metadata"""   ,   SCREAMING_SNAKE_CASE__)
    __snake_case:		Any					    =		state_dict.copy()
    if metadata is not None:
      __snake_case:		str					    =		metadata

    def load(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=""):
      __snake_case:		List[str]					    =		{} if metadata is None else metadata.get(prefix[:-1]   ,   {})
      module._load_from_state_dict(
          SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
      for name, child in module._modules.items():
        if child is not None:
          load(SCREAMING_SNAKE_CASE__   ,   prefix + name + """.""")

    load(SCREAMING_SNAKE_CASE__   ,   prefix=SCREAMING_SNAKE_CASE__)

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

    __snake_case:		int					    =		warn_missing_keys

    if len(SCREAMING_SNAKE_CASE__) > 0:
      print(
          """Weights of {} not initialized from pretrained model: {}""".format(
              model.__class__.__name__   ,   SCREAMING_SNAKE_CASE__))
    if len(SCREAMING_SNAKE_CASE__) > 0:
      print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__   ,   SCREAMING_SNAKE_CASE__))
    if len(SCREAMING_SNAKE_CASE__) > 0:
      print(
          """Ignored weights of {} not initialized from pretrained model: {}""".format(
              model.__class__.__name__   ,   SCREAMING_SNAKE_CASE__))
    if len(SCREAMING_SNAKE_CASE__) > 0:
      print("""\n""".join(SCREAMING_SNAKE_CASE__))

  __snake_case:		Tuple					    =		{
      """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""": 0.001,
            """use_rel_pos_bias""": True,
        })

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

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

  __snake_case:		int					    =		list(checkpoint_model.keys())
  for key in all_keys:
    if "relative_position_index" in key:
      checkpoint_model.pop(SCREAMING_SNAKE_CASE__)

    if "relative_position_bias_table" in key:
      __snake_case:		Any					    =		checkpoint_model[key]
      __snake_case    ,  __snake_case:		Optional[int]					    =		rel_pos_bias.size()
      __snake_case    ,  __snake_case:		Any					    =		model.state_dict()[key].size()
      __snake_case:		str					    =		model.patch_embed.patch_shape
      if dst_patch_shape[0] != dst_patch_shape[1]:
        raise NotImplementedError()

  load_state_dict(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   prefix="""""")

  return model







def      A__				(       )  ->       Optional[int]:
  __snake_case:		List[str]					    =		get_args()

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

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

  if is_large:
    __snake_case:		Union[str, Any]					    =		1024
    __snake_case:		List[str]					    =		4096
    __snake_case:		str					    =		24
    __snake_case:		Tuple					    =		16

  # 2. Load Beit model
  __snake_case:		str					    =		load_beit_model(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  orig_model.eval()

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

  __snake_case:		Any					    =		(pixel_values,) if is_finetuned else (pixel_values, None)
  with torch.no_grad():
    __snake_case:		Dict					    =		orig_model(*SCREAMING_SNAKE_CASE__)

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    __snake_case:		List[str]					    =		DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE__)
    hf_model.eval()
    __snake_case:		Tuple					    =		False
    __snake_case:		List[str]					    =		"""data2vec_vision."""
  else:
    __snake_case:		List[Any]					    =		DataaVecVisionModel(SCREAMING_SNAKE_CASE__)
    hf_model.eval()
    __snake_case:		Tuple					    =		True
    __snake_case:		Tuple					    =		""""""

  __snake_case:		List[str]					    =		create_rename_keys(SCREAMING_SNAKE_CASE__   ,   hf_prefix=SCREAMING_SNAKE_CASE__   ,   has_lm_head=SCREAMING_SNAKE_CASE__)
  __snake_case:		str					    =		orig_model.state_dict()
  for src, dest in rename_keys:
    __snake_case:		Optional[int]					    =		state_dict.pop(SCREAMING_SNAKE_CASE__)
    __snake_case:		Optional[int]					    =		val

  read_in_q_k_v(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   hf_prefix=SCREAMING_SNAKE_CASE__   ,   has_lm_head=SCREAMING_SNAKE_CASE__)
  __snake_case    ,  __snake_case:		Dict					    =		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__   ,   strict=SCREAMING_SNAKE_CASE__)
  print("""HF missing"""   ,   SCREAMING_SNAKE_CASE__)
  print("""HF unexpected_keys"""   ,   SCREAMING_SNAKE_CASE__)

  # 5. Forward HF Data2VecVision model
  with torch.no_grad():
    __snake_case:		int					    =		hf_model(SCREAMING_SNAKE_CASE__)

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

  # 6. Compare
  __snake_case:		Dict					    =		torch.max(torch.abs(hf_output - orig_model_output)).item()

  print(F'''max_absolute_diff = {max_absolute_diff}''')
  __snake_case:		Union[str, Any]					    =		torch.allclose(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   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"













from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1
       lowerCAmelCase__ 						=	2
       lowerCAmelCase__ 						=	3




       def __repr__(    self					:       Optional[int]						):
         return f'''{self.__class__.__name__}.{self.name}'''







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       float:
  __snake_case    ,  __snake_case:		Union[str, Any]					    =		point
  return degrees(atana(y - miny   ,   x - minx))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Direction:
  __snake_case    ,  __snake_case:		Dict					    =		starting
  __snake_case    ,  __snake_case:		List[str]					    =		via
  __snake_case    ,  __snake_case:		Optional[int]					    =		target
  __snake_case:		int					    =		degrees(atana(ya - ya   ,   xa - xa))
  via_angle %= 360
  __snake_case:		List[Any]					    =		degrees(atana(ya - ya   ,   xa - xa))
  target_angle %= 360
  # t-
  #  \ \
  #   \ v
  #    \|
  #     s
  # via_angle is always lower than target_angle, if direction is left.
  # If they are same, it means they are on a same line of convex hull.
  if target_angle > via_angle:
    return Direction.left
  elif target_angle == via_angle:
    return Direction.straight
  else:
    return Direction.right







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       list[tuple[int, int]]:

  if len(SCREAMING_SNAKE_CASE__) <= 2:
    # There is no convex hull
    raise ValueError("""graham_scan: argument must contain more than 3 points.""")
  if len(SCREAMING_SNAKE_CASE__) == 3:
    return points
  # find the lowest and the most left point
  __snake_case:		Optional[int]					    =		0
  __snake_case    ,  __snake_case:		Optional[int]					    =		maxsize, maxsize
  for i, point in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:		Tuple					    =		point[0]
    __snake_case:		Optional[Any]					    =		point[1]
    if y < miny:
      __snake_case:		Optional[Any]					    =		y
      __snake_case:		Union[str, Any]					    =		x
      __snake_case:		Any					    =		i
    if y == miny and x < minx:
      __snake_case:		Optional[Any]					    =		x
      __snake_case:		str					    =		i

    # remove the lowest and the most left point from points for preparing for sort
  points.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:		Optional[int]					    =		sorted(SCREAMING_SNAKE_CASE__   ,   key=lambda SCREAMING_SNAKE_CASE__: angle_comparer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  # This insert actually costs complexity,
  # and you should instead add (minx, miny) into stack later.
  # I'm using insert just for easy understanding.
  sorted_points.insert(0   ,   (minx, miny))

  __snake_case:		deque[tuple[int, int]]					    =		deque()
  stack.append(sorted_points[0])
  stack.append(sorted_points[1])
  stack.append(sorted_points[2])
  # In any ways, the first 3 points line are towards left.
  # Because we sort them the angle from minx, miny.
  __snake_case:		Optional[Any]					    =		Direction.left

  for i in range(3   ,   len(SCREAMING_SNAKE_CASE__)):
    while True:
      __snake_case:		Optional[Any]					    =		stack[-2]
      __snake_case:		List[Any]					    =		stack[-1]
      __snake_case:		int					    =		sorted_points[i]
      __snake_case:		Union[str, Any]					    =		check_direction(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

      if next_direction == Direction.left:
        __snake_case:		Dict					    =		Direction.left
        break
      if next_direction == Direction.straight:
        if current_direction == Direction.left:
          # We keep current_direction as left.
          # Because if the straight line keeps as straight,
          # we want to know if this straight line is towards left.
          break
        elif current_direction == Direction.right:
          # If the straight line is towards right,
          # every previous points on those straigh line is not convex hull.
          stack.pop()
      if next_direction == Direction.right:
        stack.pop()
    stack.append(sorted_points[i])
  return list(SCREAMING_SNAKE_CASE__)






