






"""simple docstring"""





import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE :    List[str]    =						logging.get_logger(__name__)






@dataclass
class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			4_2
    SCREAMING_SNAKE_CASE						:					int						=			field(default_factory=_a		)
    SCREAMING_SNAKE_CASE						:					List[str]						=			field(default_factory=_a		)






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Tensor	,						__SCREAMING_SNAKE_CASE					:							Tensor )					->		Optional[Any]:
      lowerCamelCase_   					=						len(list(m.modules() ) ) == 1 or isinstance(__SCREAMING_SNAKE_CASE	,						nn.Convad ) or isinstance(__SCREAMING_SNAKE_CASE	,						nn.BatchNormad )
      if has_not_submodules:
        if not isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
          self.traced.append(__SCREAMING_SNAKE_CASE )






    def __call__(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tensor )					->		str:
      for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook ) )
      self.module(__SCREAMING_SNAKE_CASE )
      [x.remove() for x in self.handles]
      return self






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Optional[Any]:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __SCREAMING_SNAKE_CASE : len(list(x.state_dict().keys() ) ) > 0	,						self.traced ) )






@dataclass
class 					a						:
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			4_2
    SCREAMING_SNAKE_CASE						:					Any						=			4_2
    SCREAMING_SNAKE_CASE						:					List[str]						=			0
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			field(default_factory=_a		)
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			field(default_factory=_a		)






    def __call__(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Tensor )					->		List[Any]:
      lowerCamelCase_   					=						Tracker(self.dest )(__SCREAMING_SNAKE_CASE ).parametrized
      lowerCamelCase_   					=						Tracker(self.src )(__SCREAMING_SNAKE_CASE ).parametrized

      lowerCamelCase_   					=						list(filter(lambda __SCREAMING_SNAKE_CASE : type(__SCREAMING_SNAKE_CASE ) not in self.src_skip	,						__SCREAMING_SNAKE_CASE ) )
      lowerCamelCase_   					=						list(filter(lambda __SCREAMING_SNAKE_CASE : type(__SCREAMING_SNAKE_CASE ) not in self.dest_skip	,						__SCREAMING_SNAKE_CASE ) )

      if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
        raise Exception(
            F'''Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE )} operations while'''
            F''' destination module has {len(__SCREAMING_SNAKE_CASE )}.''' )

      for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        dest_m.load_state_dict(src_m.state_dict() )
        if self.verbose == 1:
          print(F'''Transfered from={src_m} to={dest_m}''' )







def 					lowerCamelCase__	(							_lowerCamelCase	:		nn.Module		,    _lowerCamelCase	:		nn.Module			)				->    nn.Module:
  lowerCamelCase_   					=						from_model.state_dict()
  lowerCamelCase_   					=						our_model.state_dict()
  lowerCamelCase_   					=						our_model.config
  lowerCamelCase_   					=						[]
  for stage_idx in range(len(config.hidden_sizes			)			):
    for block_id in range(config.depths[stage_idx]			):
      lowerCamelCase_   					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
      lowerCamelCase_   					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

      all_keys.append((from_key, to_key)			)
      lowerCamelCase_   					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
      lowerCamelCase_   					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

      all_keys.append((from_key, to_key)			)

  for from_key, to_key in all_keys:
    lowerCamelCase_   					=						from_state_dict.pop(snake_case__			)

  our_model.load_state_dict(snake_case__			)
  return our_model







def 					lowerCamelCase__	(							_lowerCamelCase	:		str		,    _lowerCamelCase	:		VanConfig		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		nn.Module		,    _lowerCamelCase	:		Path		,    _lowerCamelCase	:		bool = True		,    )				->    str:
  print(F'''Downloading weights for {name}...'''			)
  lowerCamelCase_   					=						cached_download(snake_case__			)
  print(F'''Converting {name}...'''			)
  lowerCamelCase_   					=						torch.load(snake_case__			)['state_dict']
  from_model.load_state_dict(snake_case__			)
  from_model.eval()
  with torch.no_grad():
    lowerCamelCase_   					=						VanForImageClassification(snake_case__			).eval()
    lowerCamelCase_   					=						ModuleTransfer(src=snake_case__		,    dest=snake_case__			)
    lowerCamelCase_   					=						torch.randn((1, 3, 224, 224)			)
    module_transfer(snake_case__			)
    lowerCamelCase_   					=						copy_parameters(snake_case__		,    snake_case__			)

  if not torch.allclose(from_model(snake_case__			)		,    our_model(snake_case__			).logits			):
    raise ValueError('The model logits don\'t match the original one.'			)

  lowerCamelCase_   					=						name
  print(snake_case__			)

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,    commit_message='Add model'		,    use_temp_dir=snake_case__		,    )

    # we can use the convnext one
    lowerCamelCase_   					=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'			)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,    commit_message='Add image processor'		,    use_temp_dir=snake_case__		,    )

    print(F'''Pushed {checkpoint_name}'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Path		,    _lowerCamelCase	:		str = None		,    _lowerCamelCase	:		bool = True			)				->    Dict:
  lowerCamelCase_   					=						'imagenet-1k-id2label.json'
  lowerCamelCase_   					=						1000

  lowerCamelCase_   					=						'huggingface/label-files'
  lowerCamelCase_   					=						num_labels
  lowerCamelCase_   					=						json.load(open(hf_hub_download(snake_case__		,    snake_case__		,    repo_type='dataset'			)		,    'r'			)			)
  lowerCamelCase_   					=						{int(snake_case__			): v for k, v in idalabel.items()}

  lowerCamelCase_   					=						idalabel
  lowerCamelCase_   					=						{v: k for k, v in idalabel.items()}

  lowerCamelCase_   					=						partial(snake_case__		,    num_labels=snake_case__		,    idalabel=snake_case__		,    labelaid=snake_case__			)

  lowerCamelCase_   					=						{
      'van-tiny': ImageNetPreTrainedConfig(
          hidden_sizes=[32, 64, 160, 256]		,    depths=[3, 3, 5, 2]		,    mlp_ratios=[8, 8, 4, 4]		,    ),
      'van-small': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,    depths=[2, 2, 4, 2]		,    mlp_ratios=[8, 8, 4, 4]		,    ),
      'van-base': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,    depths=[3, 3, 12, 3]		,    mlp_ratios=[8, 8, 4, 4]		,    ),
      'van-large': ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]		,    depths=[3, 5, 27, 3]		,    mlp_ratios=[8, 8, 4, 4]		,    ),
  }

  lowerCamelCase_   					=						{
      'van-tiny': van_tiny,
      'van-small': van_small,
      'van-base': van_base,
      'van-large': van_large,
  }

  lowerCamelCase_   					=						{
      'van-tiny': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
      ),
      'van-small': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
      ),
      'van-base': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
      ),
      'van-large': (
          'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
      ),
  }

  if model_name:
    convert_weight_and_push(
        snake_case__		,    names_to_config[model_name]		,    checkpoint=names_to_original_checkpoints[model_name]		,    from_model=names_to_original_models[model_name]()		,    save_directory=snake_case__		,    push_to_hub=snake_case__		,    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          snake_case__		,    snake_case__		,    checkpoint=names_to_original_checkpoints[model_name]		,    from_model=names_to_original_models[model_name]()		,    save_directory=snake_case__		,    push_to_hub=snake_case__		,    )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--model-name''',
      default=None,
      type=str,
      help=(
          '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
          ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=Path,
      required=True,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument(
      '''--van_dir''',
      required=True,
      type=Path,
      help=(
          '''A path to VAN\'s original implementation directory. You can download from here:'''
          ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
      ),
  )
  parser.add_argument(
      '''--push_to_hub''',
      default=True,
      type=bool,
      required=False,
      help='''If True, push model and image processor to the hub.''',
  )

  _SCREAMING_SNAKE_CASE :    List[str]    =						parser.parse_args()
  _SCREAMING_SNAKE_CASE :    List[str]    =						args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  _SCREAMING_SNAKE_CASE :    Any    =						args.van_dir
  # append the path to the parents to maskformer dir
  sys.path.append(str(van_dir.parent))
  from van.models.van import van_base, van_large, van_small, van_tiny

  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












"""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"




