
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					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]=False		,				__SCREAMING_SNAKE_CASE					:  List[Any]=False		,				__SCREAMING_SNAKE_CASE					:  int="data2vec."    ):






  """simple docstring"""




  _lowerCAmelCase						=		'backbone.' if is_semantic else ''

  _lowerCAmelCase						=		[]
  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					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Dict=False		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]=False		,				__SCREAMING_SNAKE_CASE					:  List[Any]="data2vec_vision."    ):






  """simple docstring"""




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

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

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

    _lowerCAmelCase						=		gamma_a
    _lowerCAmelCase						=		gamma_a

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

      _lowerCAmelCase						=		table
      _lowerCAmelCase						=		index



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		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=224		,				type=_a		,				help='images input size'    )
  parser.add_argument('--beit_checkpoint'		,				default=''		,				help='beit checkpoint'    )

  return parser.parse_args()



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




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

    def load(__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Dict=""    ):
      _lowerCAmelCase						=		{} 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    )

    _lowerCAmelCase						=		[]
    _lowerCAmelCase						=		[]
    for key in missing_keys:
      _lowerCAmelCase						=		True
      for ignore_key in ignore_missing.split('|'    ):
        if ignore_key in key:
          _lowerCAmelCase						=		False
          break
      if keep_flag:
        warn_missing_keys.append(_a    )
      else:
        ignore_missing_keys.append(_a    )

    _lowerCAmelCase						=		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    )    )

  _lowerCAmelCase						=		{
      '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,
        }    )

  _lowerCAmelCase						=		create_model(
      'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'		,				**_a		,				)
  _lowerCAmelCase						=		model.patch_embed.patch_size
  _lowerCAmelCase						=		(args.input_size // patch_size[0], args.input_size // patch_size[1])
  _lowerCAmelCase						=		torch.load(args.beit_checkpoint		,				map_location='cpu'    )

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

  _lowerCAmelCase						=		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:
      _lowerCAmelCase						=		checkpoint_model[key]
      _lowerCAmelCase	,       _lowerCAmelCase						=		rel_pos_bias.size()
      _lowerCAmelCase	,       _lowerCAmelCase						=		model.state_dict()[key].size()
      _lowerCAmelCase						=		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"""




  _lowerCAmelCase						=		get_args()

  _lowerCAmelCase						=		'ft1k' in args.hf_checkpoint_name
  _lowerCAmelCase						=		'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
  _lowerCAmelCase						=		DataaVecVisionConfig()
  if is_finetuned:
    _lowerCAmelCase						=		True
    _lowerCAmelCase						=		False
    _lowerCAmelCase						=		True
    _lowerCAmelCase						=		1000

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

  if is_large:
    _lowerCAmelCase						=		1024
    _lowerCAmelCase						=		4096
    _lowerCAmelCase						=		24
    _lowerCAmelCase						=		16

  # 2. Load Beit model
  _lowerCAmelCase						=		load_beit_model(_a		,				_a		,				_a    )
  orig_model.eval()

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

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

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    _lowerCAmelCase						=		DataaVecVisionForImageClassification(_a    )
    hf_model.eval()
    _lowerCAmelCase						=		False
    _lowerCAmelCase						=		'data2vec_vision.'
  else:
    _lowerCAmelCase						=		DataaVecVisionModel(_a    )
    hf_model.eval()
    _lowerCAmelCase						=		True
    _lowerCAmelCase						=		''

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

  read_in_q_k_v(_a		,				_a		,				hf_prefix=_a		,				has_lm_head=_a    )
  _lowerCAmelCase	,       _lowerCAmelCase						=		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():
    _lowerCAmelCase						=		hf_model(_a    )

  _lowerCAmelCase						=		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

  print(f'''max_absolute_diff = {max_absolute_diff}'''    )
  _lowerCAmelCase						=		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'''





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


  def __call__(self	:			int						,			lowerCAmelCase_	:			Tuple     ) ->							int:
        snake_case_    							=				[]
        for img in imgs:
              snake_case_							,						snake_case_    							=				img.shape[:2]
              # later: provide list and randomly choose index for resize
              snake_case_    							=				np.random.randint(self.short_edge_length[0]						,			self.short_edge_length[1] + 1     )
              if size == 0:
                    return img
              snake_case_    							=				size * 1.0 / min(lowerCAmelCase_						,			lowerCAmelCase_     )
              if h < w:
                    snake_case_							,						snake_case_    							=				size, scale * w
              else:
                    snake_case_							,						snake_case_    							=				scale * h, size
              if max(lowerCAmelCase_						,			lowerCAmelCase_     ) > self.max_size:
                    snake_case_    							=				self.max_size * 1.0 / max(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				newh * scale
                    snake_case_    							=				neww * scale
              snake_case_    							=				int(neww + 0.5     )
              snake_case_    							=				int(newh + 0.5     )

              if img.dtype == np.uinta:
                    snake_case_    							=				Image.fromarray(lowerCAmelCase_     )
                    snake_case_    							=				pil_image.resize((neww, newh)						,			PILImageResampling.BILINEAR     )
                    snake_case_    							=				np.asarray(lowerCAmelCase_     )
              else:
                    snake_case_    							=				img.permute(2						,			0						,			1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
                    snake_case_    							=				nn.functional.interpolate(
                        lowerCAmelCase_						,			(newh, neww)						,			mode=self.interp_method						,			align_corners=lowerCAmelCase_     ).squeeze(0     )
              img_augs.append(lowerCAmelCase_     )

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]     ) ->							int:
        snake_case_    							=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,			cfg.INPUT.MAX_SIZE_TEST     )
        snake_case_    							=				cfg.INPUT.FORMAT
        snake_case_    							=				cfg.SIZE_DIVISIBILITY
        snake_case_    							=				cfg.PAD_VALUE
        snake_case_    							=				cfg.INPUT.MAX_SIZE_TEST
        snake_case_    							=				cfg.MODEL.DEVICE
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_STD     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_MEAN     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				lambda lowerCAmelCase_     : (x - self.pixel_mean) / self.pixel_std

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            nn.functional.pad(
                lowerCAmelCase_						,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,			value=self.pad_value						,			)
            for size, im in zip(lowerCAmelCase_						,			lowerCAmelCase_     )
        ]

        return torch.stack(lowerCAmelCase_     ), torch.tensor(lowerCAmelCase_     )


  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[images]
              if single_image:
                    assert len(lowerCAmelCase_     ) == 1
              for i in range(len(lowerCAmelCase_     )     ):
                    if isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(lowerCAmelCase_						,			images.pop(lowerCAmelCase_     ).to(self.device     ).float()     )
                    elif not isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(
                              lowerCAmelCase_						,			torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_     )						,			input_format=self.input_format     )     )
                              .to(self.device     )
                              .float()						,			)
            # resize smallest edge
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				self.aug(lowerCAmelCase_     )
              # transpose images and convert to torch tensors
              # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
              # now normalize before pad to avoid useless arithmetic
              snake_case_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

              if self.size_divisibility > 0:
                    raise NotImplementedError()
              # pad
              snake_case_    							=				torch.true_divide(lowerCAmelCase_						,			lowerCAmelCase_     )
              if single_image:
                    return images[0], sizes[0], scales_yx[0]
              else:
                    return images, sizes, scales_yx

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      assert torch.isfinite(_a      ).all(), "Box tensor contains infinite or NaN!"
      snake_case_							,						snake_case_    							=				box_size
      tensor[:, 0].clamp_(min=0			,       max=_a      )
      tensor[:, 1].clamp_(min=0			,       max=_a      )
      tensor[:, 2].clamp_(min=0			,       max=_a      )
      tensor[:, 3].clamp_(min=0			,       max=_a      )

