


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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__=False			,       SCREAMING_SNAKE_CASE__=False			,       SCREAMING_SNAKE_CASE__="data2vec."						):
 __lowerCamelCase		:							Union[str, Any]							=						'backbone.' if is_semantic else ''

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

  __lowerCamelCase		:							int							=						in_proj_weight[
      : config.hidden_size, :
  ]
  __lowerCamelCase		:							int							=						q_bias
  __lowerCamelCase		:							Union[str, Any]							=						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowerCamelCase		:							List[Any]							=						in_proj_weight[
      -config.hidden_size :, :
  ]
  __lowerCamelCase		:							Dict							=						v_bias

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

  __lowerCamelCase		:							Optional[int]							=						gamma_a
  __lowerCamelCase		:							List[str]							=						gamma_a

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

   __lowerCamelCase		:							str							=						table
   __lowerCamelCase		:							Union[str, Any]							=						index
def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							int							=						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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 def load_state_dict(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__=""			,       SCREAMING_SNAKE_CASE__="relative_position_index"						):
  __lowerCamelCase		:							List[str]							=						[]
  __lowerCamelCase		:							List[Any]							=						[]
  __lowerCamelCase		:							int							=						[]
  # copy state_dict so _load_from_state_dict can modify it
  __lowerCamelCase		:							Tuple							=						getattr(_UpperCAmelCase			,       '_metadata'			,       _UpperCAmelCase						)
  __lowerCamelCase		:							List[Any]							=						state_dict.copy()
  if metadata is not None:
   __lowerCamelCase		:							str							=						metadata

  def load(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__=""						):
   __lowerCamelCase		:							Optional[Any]							=						{} 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						)

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

  __lowerCamelCase		:							Optional[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						)						)

 __lowerCamelCase		:							int							=						{
     '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': 1_000,
          'use_mean_pooling': True,
          'init_scale': 0.001,
          'use_rel_pos_bias': True,
      }						)

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

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

 __lowerCamelCase		:							Union[str, 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:
   __lowerCamelCase		:							int							=						checkpoint_model[key]
   __lowerCamelCase					,     __lowerCamelCase		:							Dict							=						rel_pos_bias.size()
   __lowerCamelCase					,     __lowerCamelCase		:							int							=						model.state_dict()[key].size()
   __lowerCamelCase		:							List[Any]							=						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       UpperCamelCase__     (  ):
 __lowerCamelCase		:							List[Any]							=						get_args()

 __lowerCamelCase		:							Optional[Any]							=						'ft1k' in args.hf_checkpoint_name
 __lowerCamelCase		:							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
 __lowerCamelCase		:							List[Any]							=						DataaVecVisionConfig()
 if is_finetuned:
  __lowerCamelCase		:							Any							=						True
  __lowerCamelCase		:							Union[str, Any]							=						False
  __lowerCamelCase		:							int							=						True
  __lowerCamelCase		:							List[Any]							=						1_000

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

 if is_large:
  __lowerCamelCase		:							int							=						1_024
  __lowerCamelCase		:							Tuple							=						4_096
  __lowerCamelCase		:							Optional[int]							=						24
  __lowerCamelCase		:							List[str]							=						16

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

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

 __lowerCamelCase		:							Tuple							=						(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __lowerCamelCase		:							Any							=						orig_model(*_UpperCAmelCase						)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __lowerCamelCase		:							Any							=						DataaVecVisionForImageClassification(_UpperCAmelCase						)
  hf_model.eval()
  __lowerCamelCase		:							Optional[Any]							=						False
  __lowerCamelCase		:							Optional[int]							=						'data2vec_vision.'
 else:
  __lowerCamelCase		:							Optional[Any]							=						DataaVecVisionModel(_UpperCAmelCase						)
  hf_model.eval()
  __lowerCamelCase		:							List[Any]							=						True
  __lowerCamelCase		:							Tuple							=						''

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

 read_in_q_k_v(_UpperCAmelCase			,       _UpperCAmelCase			,       hf_prefix=_UpperCAmelCase			,       has_lm_head=_UpperCAmelCase						)
 __lowerCamelCase					,     __lowerCamelCase		:							List[str]							=						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():
  __lowerCamelCase		:							Any							=						hf_model(_UpperCAmelCase						)

 __lowerCamelCase		:							int							=						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __lowerCamelCase		:							Tuple							=						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

 print(f'max_absolute_diff = {max_absolute_diff}'						)
 __lowerCamelCase		:							Optional[int]							=						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"








"""simple docstring"""
from typing import TYPE_CHECKING

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


__UpperCamelCase			:		List[Any]		    =   {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase			:		Dict		    =   [
           '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''YosoForMaskedLM''',
           '''YosoForMultipleChoice''',
           '''YosoForQuestionAnswering''',
           '''YosoForSequenceClassification''',
           '''YosoForTokenClassification''',
           '''YosoLayer''',
           '''YosoModel''',
           '''YosoPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_yoso import (
                  YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  YosoForMaskedLM,
                  YosoForMultipleChoice,
                  YosoForQuestionAnswering,
                  YosoForSequenceClassification,
                  YosoForTokenClassification,
                  YosoLayer,
                  YosoModel,
                  YosoPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase			:		Any		    =   _LazyModule(__name__, globals()['''__file__'''], _import_structure)





