





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

  def load(_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  Optional[Any]=""       ):
   lowerCAmelCase  							=      {} 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  							=      []
  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(_UpperCAmelCase       )
   else:
    ignore_missing_keys.append(_UpperCAmelCase       )

  lowerCAmelCase  							=      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  							=      {
     '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,
      }       )

 lowerCAmelCase  							=      create_model(
     'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'   ,			**_UpperCAmelCase   ,			)
 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(_UpperCAmelCase       )

  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(_UpperCAmelCase   ,			_UpperCAmelCase   ,			prefix=''       )

 return model

def 						_SCREAMING_SNAKE_CASE       ():
 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(_UpperCAmelCase   ,			_UpperCAmelCase   ,			repo_type='dataset'       )   ,			'r'       )       )
  lowerCAmelCase  							=      {int(_UpperCAmelCase       ): 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(_UpperCAmelCase   ,			_UpperCAmelCase   ,			_UpperCAmelCase       )
 orig_model.eval()

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

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

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

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

 read_in_q_k_v(_UpperCAmelCase   ,			_UpperCAmelCase   ,			hf_prefix=_UpperCAmelCase   ,			has_lm_head=_UpperCAmelCase       )
 lowerCAmelCase							,lowerCAmelCase  							=      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  							=      hf_model(_UpperCAmelCase       )

 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(_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"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 	a				(					a__   ):
 snake_case__    	=    '''AutoFeatureExtractor'''
 snake_case__    	=    '''Speech2Text2Tokenizer'''



 def __init__(			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  super().__init__(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False



 def __call__(			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  if self._in_target_context_manager:
   return self.current_processor(*_snake_case							,    **_snake_case			)

  if "raw_speech" in kwargs:
   warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
   lowerCAmelCase  							=      kwargs.pop('raw_speech'			)
  else:
   lowerCAmelCase  							=      kwargs.pop('audio'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('sampling_rate'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('text'							,    _snake_case			)
  if len(_snake_case			) > 0:
   lowerCAmelCase  							=      args[0]
   lowerCAmelCase  							=      args[1:]

  if audio is None and text is None:
   raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

  if audio is not None:
   lowerCAmelCase  							=      self.feature_extractor(_snake_case							,    *_snake_case							,    sampling_rate=_snake_case							,    **_snake_case			)
  if text is not None:
   lowerCAmelCase  							=      self.tokenizer(_snake_case							,    **_snake_case			)

  if text is None:
   return inputs
  elif audio is None:
   return encodings
  else:
   lowerCAmelCase  							=      encodings['input_ids']
   return inputs



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.batch_decode(*_snake_case							,    **_snake_case			)



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.decode(*_snake_case							,    **_snake_case			)



 @contextmanager
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  warnings.warn(
      '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
      'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
      'your audio inputs, or in a separate call.'			)
  lowerCAmelCase  							=      True
  lowerCAmelCase  							=      self.tokenizer
  yield
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False





