






"""simple docstring"""





import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_SCREAMING_SNAKE_CASE :    Tuple    =						logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_SCREAMING_SNAKE_CASE :    List[str]    =						direct_transformers_import(Path(__file__).parent)


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					int						=			["""feature_extractor""", """tokenizer"""]
    # Names need to be attr_class for attr in attributes
    SCREAMING_SNAKE_CASE						:					Optional[int]						=			None
    SCREAMING_SNAKE_CASE						:					Tuple						=			None
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			None






    def __init__(	self					:							int	,						*__SCREAMING_SNAKE_CASE					:							Optional[int]	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Dict:
      # Sanitize args and kwargs
      for key in kwargs:
        if key not in self.attributes:
          raise TypeError(F'''Unexpected keyword argument {key}.''' )
      for arg, attribute_name in zip(__SCREAMING_SNAKE_CASE	,						self.attributes ):
        if attribute_name in kwargs:
          raise TypeError(F'''Got multiple values for argument {attribute_name}.''' )
        else:
          lowerCamelCase_   					=						arg

      if len(__SCREAMING_SNAKE_CASE ) != len(self.attributes ):
        raise ValueError(
            F'''This processor requires {len(self.attributes )} arguments: {", ".join(self.attributes )}. Got '''
            F'''{len(__SCREAMING_SNAKE_CASE )} arguments instead.''' )

      # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
      for attribute_name, arg in kwargs.items():
        lowerCamelCase_   					=						getattr(self	,						F'''{attribute_name}_class''' )
        # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
        lowerCamelCase_   					=						AUTO_TO_BASE_CLASS_MAPPING.get(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
        if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
          lowerCamelCase_   					=						tuple(getattr(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ) for n in class_name if n is not None )
        else:
          lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        if not isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
          raise ValueError(
              F'''Received a {type(__SCREAMING_SNAKE_CASE ).__name__} for argument {attribute_name}, but a {class_name} was expected.''' )

        setattr(self	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )






    def __repr__(	self					:							Optional[int] )					->		List[str]:
      lowerCamelCase_   					=						[F'''- {name}: {repr(getattr(self	,						__SCREAMING_SNAKE_CASE ) )}''' for name in self.attributes]
      lowerCamelCase_   					=						'\n'.join(__SCREAMING_SNAKE_CASE )
      return F'''{self.__class__.__name__}:\n{attributes_repr}'''






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any] )					->		Union[str, Any]:
      os.makedirs(__SCREAMING_SNAKE_CASE	,						exist_ok=__SCREAMING_SNAKE_CASE )

      if push_to_hub:
        lowerCamelCase_   					=						kwargs.pop('commit_message'	,						__SCREAMING_SNAKE_CASE )
        lowerCamelCase_   					=						kwargs.pop('repo_id'	,						save_directory.split(os.path.sep )[-1] )
        lowerCamelCase_   					=						self._create_repo(__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )
        lowerCamelCase_   					=						self._get_files_timestamps(__SCREAMING_SNAKE_CASE )
      # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
      # loaded from the Hub.
      if self._auto_class is not None:
        lowerCamelCase_   					=						[getattr(self	,						__SCREAMING_SNAKE_CASE ) for attribute_name in self.attributes]
        lowerCamelCase_   					=						[(a.init_kwargs if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ) else a) for a in attrs]
        custom_object_save(self	,						__SCREAMING_SNAKE_CASE	,						config=__SCREAMING_SNAKE_CASE )

      for attribute_name in self.attributes:
        lowerCamelCase_   					=						getattr(self	,						__SCREAMING_SNAKE_CASE )
        # Include the processor class in the attribute config so this processor can then be reloaded with the
        # `AutoProcessor` API.
        if hasattr(__SCREAMING_SNAKE_CASE	,						'_set_processor_class' ):
          attribute._set_processor_class(self.__class__.__name__ )
        attribute.save_pretrained(__SCREAMING_SNAKE_CASE )

      if self._auto_class is not None:
        # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
        for attribute_name in self.attributes:
          lowerCamelCase_   					=						getattr(self	,						__SCREAMING_SNAKE_CASE )
          if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
            del attribute.init_kwargs["auto_map"]

      if push_to_hub:
        self._upload_modified_files(
            __SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						commit_message=__SCREAMING_SNAKE_CASE	,						token=kwargs.get('use_auth_token' )	,						)






    @classmethod
    def        UpperCamelCase	(	cls					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Union[str, os.PathLike]	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[str, os.PathLike]] = None	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[str, bool]] = None	,						__SCREAMING_SNAKE_CASE					:							str = "main"	,						**__SCREAMING_SNAKE_CASE					:							Optional[int]	,						)					->		Optional[int]:
      lowerCamelCase_   					=						cache_dir
      lowerCamelCase_   					=						force_download
      lowerCamelCase_   					=						local_files_only
      lowerCamelCase_   					=						revision

      lowerCamelCase_   					=						kwargs.pop('use_auth_token'	,						__SCREAMING_SNAKE_CASE )
      if use_auth_token is not None:
        warnings.warn(
            'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'	,						__SCREAMING_SNAKE_CASE )
        if token is not None:
          raise ValueError(
              '`token` and `use_auth_token` are both specified. Please set only the argument `token`.' )
        lowerCamelCase_   					=						use_auth_token

      if token is not None:
        # change to `token` in a follow-up PR
        lowerCamelCase_   					=						token

      lowerCamelCase_   					=						cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )
      return cls(*__SCREAMING_SNAKE_CASE )






    @classmethod
    def        UpperCamelCase	(	cls					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Dict="AutoProcessor" )					->		Dict:
      if not isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        lowerCamelCase_   					=						auto_class.__name__

      import transformers.models.auto as auto_module

      if not hasattr(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        raise ValueError(F'''{auto_class} is not a valid auto class.''' )

      lowerCamelCase_   					=						auto_class






    @classmethod
    def        UpperCamelCase	(	cls					:							Any	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						**__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Optional[int]:
      lowerCamelCase_   					=						[]
      for attribute_name in cls.attributes:
        lowerCamelCase_   					=						getattr(cls	,						F'''{attribute_name}_class''' )
        if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
          lowerCamelCase_   					=						tuple(getattr(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ) if n is not None else None for n in class_name )
          lowerCamelCase_   					=						kwargs.get('use_fast'	,						__SCREAMING_SNAKE_CASE )
          if use_fast and classes[1] is not None:
            lowerCamelCase_   					=						classes[1]
          else:
            lowerCamelCase_   					=						classes[0]
        else:
          lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )

        args.append(attribute_class.from_pretrained(__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE ) )
      return args






    @property
    def        UpperCamelCase	(	self					:							List[str] )					->		Optional[Any]:
      lowerCamelCase_   					=						getattr(self	,						self.attributes[0] )
      return getattr(__SCREAMING_SNAKE_CASE	,						'model_input_names'	,						__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE :    Any    =						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  _SCREAMING_SNAKE_CASE :    List[Any]    =						ProcessorMixin.push_to_hub.__doc__.format(
      object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
  )












"""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"




