

'''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__		(						SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Dict=False       ,   SCREAMING_SNAKE_CASE      :   List[Any]=False       ,   SCREAMING_SNAKE_CASE      :   int="data2vec."						):
  UpperCAmelCase													=	'backbone.' if is_semantic else ''

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

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

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

    UpperCAmelCase													=	gamma_a
    UpperCAmelCase													=	gamma_a

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

      UpperCAmelCase													=	table
      UpperCAmelCase													=	index


def       lowerCamelCase__		(						):
  UpperCAmelCase													=	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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  def load_state_dict(SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   str=""       ,   SCREAMING_SNAKE_CASE      :   Optional[int]="relative_position_index"						):
    UpperCAmelCase													=	[]
    UpperCAmelCase													=	[]
    UpperCAmelCase													=	[]
    # copy state_dict so _load_from_state_dict can modify it
    UpperCAmelCase													=	getattr(SCREAMING_SNAKE_CASE       ,   '_metadata'       ,   SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	state_dict.copy()
    if metadata is not None:
      UpperCAmelCase													=	metadata

    def load(SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   Dict=""						):
      UpperCAmelCase													=	{} 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						)

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

    UpperCAmelCase													=	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						)						)

  UpperCAmelCase													=	{
      '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,
        }						)

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

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

  UpperCAmelCase													=	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:
      UpperCAmelCase													=	checkpoint_model[key]
      UpperCAmelCase							,      UpperCAmelCase													=	rel_pos_bias.size()
      UpperCAmelCase							,      UpperCAmelCase													=	model.state_dict()[key].size()
      UpperCAmelCase													=	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       lowerCamelCase__		(						):
  UpperCAmelCase													=	get_args()

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

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

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

  # 2. Load Beit model
  UpperCAmelCase													=	load_beit_model(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  orig_model.eval()

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

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

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

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

  read_in_q_k_v(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_prefix=SCREAMING_SNAKE_CASE       ,   has_lm_head=SCREAMING_SNAKE_CASE						)
  UpperCAmelCase							,      UpperCAmelCase													=	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():
    UpperCAmelCase													=	hf_model(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

  print(f'''max_absolute_diff = {max_absolute_diff}'''						)
  UpperCAmelCase													=	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"





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a				: Optional[int] 							=						logging.get_logger(__name__)

_a				: Dict 							=						{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: List[str]      =   "mega"
  def __init__(     self   , a_=3_0_5_2_2   , a_=1_2_8   , a_=4   , a_=2_5_6   , a_=1_6   , a_=True   , a_=6_4   , a_=False   , a_=-1   , a_=None   , a_=True   , a_="scalenorm"   , a_=True   , a_="silu"   , a_="softmax"   , a_=0.1   , a_=0.1   , a_=0.1   , a_=False   , a_=True   , a_=2_5_6   , a_=True   , a_=0.1   , a_=2_0_4_8   , a_=False   , a_=2   , a_=0.02   , a_=0.2   , a_=0.02   , a_=1.0   , a_=1   , a_=0   , a_=2   , a_="rotary"   , a_=None   , a_=True   , a_=True   , **a_   , )							->				Any:


    """simple docstring"""
    super().__init__(pad_token_id=a_   , bos_token_id=a_   , eos_token_id=a_   , **a_     )

    UpperCAmelCase													=	vocab_size
    UpperCAmelCase													=	hidden_size
    UpperCAmelCase													=	num_hidden_layers
    UpperCAmelCase													=	activation
    UpperCAmelCase													=	attention_activation
    UpperCAmelCase													=	intermediate_size
    UpperCAmelCase													=	ema_projection_size
    UpperCAmelCase													=	bidirectional
    UpperCAmelCase													=	shared_representation_size
    UpperCAmelCase													=	use_chunking
    UpperCAmelCase													=	chunk_size
    UpperCAmelCase													=	truncation
    UpperCAmelCase													=	normalize_before_mega
    UpperCAmelCase													=	normalization_type
    UpperCAmelCase													=	norm_affine
    UpperCAmelCase													=	dropout_prob
    UpperCAmelCase													=	hidden_dropout_prob
    UpperCAmelCase													=	attention_probs_dropout_prob
    UpperCAmelCase													=	use_feature_dropout
    UpperCAmelCase													=	use_normalized_ffn
    UpperCAmelCase													=	nffn_hidden_size
    UpperCAmelCase													=	normalize_before_ffn
    UpperCAmelCase													=	nffn_activation_dropout_prob
    UpperCAmelCase													=	max_positions
    UpperCAmelCase													=	add_token_type_embeddings
    UpperCAmelCase													=	type_vocab_size
    UpperCAmelCase													=	initializer_range
    UpperCAmelCase													=	ema_delta_alpha_range
    UpperCAmelCase													=	ema_beta_range
    UpperCAmelCase													=	ema_gamma_omega_range
    UpperCAmelCase													=	relative_positional_bias
    UpperCAmelCase													=	use_cache
    UpperCAmelCase													=	classifier_dropout
    UpperCAmelCase													=	add_lm_hidden_dense_layer
    UpperCAmelCase													=	1  # not used but required by Hugging Face




class        lowercase_	(		a ):

  '''simple docstring'''





  @property
  def 						snake_case_			(     self     )							->				Mapping[str, Mapping[int, str]]:


    """simple docstring"""
    if self.task == "multiple-choice":
      UpperCAmelCase													=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      UpperCAmelCase													=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]     )


