






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE :    List[str]    =						{'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Any    =						['''LevitFeatureExtractor''']
  _SCREAMING_SNAKE_CASE :    Tuple    =						['''LevitImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Optional[int]    =						[
      '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''LevitForImageClassification''',
      '''LevitForImageClassificationWithTeacher''',
      '''LevitModel''',
      '''LevitPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_levit import LevitFeatureExtractor
    from .image_processing_levit import LevitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_levit import (
        LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
        LevitPreTrainedModel,
    )
else:
  import sys

  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












"""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	:		List[str]		,    _lowerCamelCase	:		List[str]=False		,    _lowerCamelCase	:		List[Any]=False		,    _lowerCamelCase	:		Dict="data2vec."			)				->    List[Any]:
  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 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Union[str, Any]=False		,    _lowerCamelCase	:		Dict=False		,    _lowerCamelCase	:		Union[str, Any]="data2vec_vision."			)				->    List[str]:
  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 					lowerCamelCase__	(							)				->    Union[str, Any]:
  lowerCamelCase_   					=						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	:		Optional[Any]		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Any			)				->    List[str]:
  def load_state_dict(_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		str=""		,    _lowerCamelCase	:		Any="relative_position_index"			):
    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]
    # copy state_dict so _load_from_state_dict can modify it
    lowerCamelCase_   					=						getattr(_lowerCamelCase		,    '_metadata'		,    _lowerCamelCase			)
    lowerCamelCase_   					=						state_dict.copy()
    if metadata is not None:
      lowerCamelCase_   					=						metadata

    def load(_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		List[str]=""			):
      lowerCamelCase_   					=						{} 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			)

    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(_lowerCamelCase			)
      else:
        ignore_missing_keys.append(_lowerCamelCase			)

    lowerCamelCase_   					=						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			)			)

  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_01,
            'use_rel_pos_bias': True,
        }			)

  lowerCamelCase_   					=						create_model(
      'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'		,    **_lowerCamelCase		,    )
  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(_lowerCamelCase			)

    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(_lowerCamelCase		,    _lowerCamelCase		,    prefix=''			)

  return model







def 					lowerCamelCase__	(							)				->    Dict:
  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(_lowerCamelCase		,    _lowerCamelCase		,    repo_type='dataset'			)		,    'r'			)			)
    lowerCamelCase_   					=						{int(_lowerCamelCase			): 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(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
  orig_model.eval()

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

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

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

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

  read_in_q_k_v(_lowerCamelCase		,    _lowerCamelCase		,    hf_prefix=_lowerCamelCase		,    has_lm_head=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						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():
    lowerCamelCase_   					=						hf_model(_lowerCamelCase			)

  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(_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"




