code: 
  
  
  import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_="data2vec."			)						->							Tuple:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			'''backbone.''' if is_semantic else ''''''
  
         _lowercase					:				int           =			[]
         for i in range(config.num_hidden_layers			):
                # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
                rename_keys.append(
                    (F'{prefix}blocks.{i}.norm1.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')			)
                rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')			)
                rename_keys.append(
                    (F'{prefix}blocks.{i}.attn.proj.weight', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')			)
                rename_keys.append(
                    (F'{prefix}blocks.{i}.attn.proj.bias', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')			)
                rename_keys.append(
                    (F'{prefix}blocks.{i}.norm2.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')			)
                rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')			)
                rename_keys.append(
                    (F'{prefix}blocks.{i}.mlp.fc1.weight', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')			)
                rename_keys.append(
                    (F'{prefix}blocks.{i}.mlp.fc1.bias', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')			)
                rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'{hf_prefix}encoder.layer.{i}.output.dense.weight')			)
                rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'{hf_prefix}encoder.layer.{i}.output.dense.bias')			)
  
         # projection layer + position embeddings
         rename_keys.extend(
             [
                 (F'{prefix}cls_token', F'{hf_prefix}embeddings.cls_token'),
                 (F'{prefix}patch_embed.proj.weight', F'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
                 (F'{prefix}patch_embed.proj.bias', F'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
             ]			)
  
         if has_lm_head:
                # mask token + shared relative position bias + layernorm
                rename_keys.extend(
                    [
                        ('''mask_token''', F'{hf_prefix}embeddings.mask_token'),
                        (
                            '''rel_pos_bias.relative_position_bias_table''',
                            F'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
                        ),
                        (
                            '''rel_pos_bias.relative_position_index''',
                            F'{hf_prefix}encoder.relative_position_bias.relative_position_index',
                        ),
                        ('''norm.weight''', '''layernorm.weight'''),
                        ('''norm.bias''', '''layernorm.bias'''),
                    ]			)
         elif is_semantic:
                # semantic segmentation classification heads
                rename_keys.extend(
                    [
                        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
                        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
                        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
                        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
                    ]			)
         else:
                # layernorm + classification head
                rename_keys.extend(
                    [
                        ('''fc_norm.weight''', F'{hf_prefix}pooler.layernorm.weight'),
                        ('''fc_norm.bias''', F'{hf_prefix}pooler.layernorm.bias'),
                        ('''head.weight''', '''classifier.weight'''),
                        ('''head.bias''', '''classifier.bias'''),
                    ]			)
  
         return rename_keys
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_="data2vec_vision."			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         for i in range(config.num_hidden_layers			):
                _lowercase					:				str           =			'''backbone.''' if is_semantic else ''''''
                # queries, keys and values
                _lowercase					:				Union[str, Any]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'			)
                _lowercase					:				Tuple           =			state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'			)
                _lowercase					:				Dict           =			state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'			)
  
                _lowercase					:				List[str]           =			in_proj_weight[
                    : config.hidden_size, :
                ]
                _lowercase					:				Union[str, Any]           =			q_bias
                _lowercase					:				Dict           =			in_proj_weight[
                    config.hidden_size : config.hidden_size * 2, :
                ]
                _lowercase					:				Union[str, Any]           =			in_proj_weight[
                    -config.hidden_size :, :
                ]
                _lowercase					:				Union[str, Any]           =			v_bias
  
                # gamma_1 and gamma_2
                # we call them lambda because otherwise they are renamed when using .from_pretrained
                _lowercase					:				List[Any]           =			state_dict.pop(F'{prefix}blocks.{i}.gamma_1'			)
                _lowercase					:				str           =			state_dict.pop(F'{prefix}blocks.{i}.gamma_2'			)
  
                _lowercase					:				Optional[Any]           =			gamma_a
                _lowercase					:				Optional[Any]           =			gamma_a
  
                # relative_position bias table + index
                if not has_lm_head:
                       # each layer has its own relative position bias
                       _lowercase					:				Optional[int]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
                       _lowercase					:				Optional[Any]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'			)
  
                       _lowercase					:				Union[str, Any]           =			table
                       _lowercase					:				List[str]           =			index
  
  def   UpperCamelCase__   (    )						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			argparse.ArgumentParser(
             '''Convert Data2VecVision to HF for image classification and pretraining'''    ,     add_help=UpperCAmelCase_			)
         parser.add_argument('''--hf_checkpoint_name'''    ,     type=UpperCAmelCase_			)
         parser.add_argument('''--input_size'''    ,     default=224    ,     type=UpperCAmelCase_    ,     help='''images input size'''			)
         parser.add_argument('''--beit_checkpoint'''    ,     default=''''''    ,     help='''beit checkpoint'''			)
  
         return parser.parse_args()
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         def load_state_dict(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=""    ,     UpperCAmelCase_="relative_position_index"			):
                _lowercase					:				List[str]           =			[]
                _lowercase					:				List[str]           =			[]
                _lowercase					:				int           =			[]
                # copy state_dict so _load_from_state_dict can modify it
                _lowercase					:				Union[str, Any]           =			getattr(UpperCAmelCase_    ,     '''_metadata'''    ,     UpperCAmelCase_			)
                _lowercase					:				List[str]           =			state_dict.copy()
                if metadata is not None:
                       _lowercase					:				Optional[Any]           =			metadata
  
                def load(UpperCAmelCase_    ,     UpperCAmelCase_=""			):
                       _lowercase					:				Union[str, Any]           =			{} if metadata is None else metadata.get(prefix[:-1]    ,     {}			)
                       module._load_from_state_dict(
                           UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
                       for name, child in module._modules.items():
                              if child is not None:
                                     load(UpperCAmelCase_    ,     prefix + name + '''.'''			)
  
                load(UpperCAmelCase_    ,     prefix=UpperCAmelCase_			)
  
                _lowercase					:				List[str]           =			[]
                _lowercase					:				Union[str, Any]           =			[]
                for key in missing_keys:
                       _lowercase					:				Union[str, Any]           =			True
                       for ignore_key in ignore_missing.split('''|'''			):
                              if ignore_key in key:
                                     _lowercase					:				int           =			False
                                     break
                       if keep_flag:
                              warn_missing_keys.append(UpperCAmelCase_			)
                       else:
                              ignore_missing_keys.append(UpperCAmelCase_			)
  
                _lowercase					:				List[Any]           =			warn_missing_keys
  
                if len(UpperCAmelCase_			) > 0:
                       print(
                           '''Weights of {} not initialized from pretrained model: {}'''.format(
                               model.__class__.__name__    ,     UpperCAmelCase_			)			)
                if len(UpperCAmelCase_			) > 0:
                       print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__    ,     UpperCAmelCase_			)			)
                if len(UpperCAmelCase_			) > 0:
                       print(
                           '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
                               model.__class__.__name__    ,     UpperCAmelCase_			)			)
                if len(UpperCAmelCase_			) > 0:
                       print('''\n'''.join(UpperCAmelCase_			)			)
  
         _lowercase					:				int           =			{
             '''pretrained''': False,
             '''use_shared_rel_pos_bias''': True,
             '''use_abs_pos_emb''': False,
             '''init_values''': 0.1,
         }
  
         if is_finetuned:
                model_kwargs.update(
                    {
                        '''num_classes''': 1000,
                        '''use_mean_pooling''': True,
                        '''init_scale''': 0.001,
                        '''use_rel_pos_bias''': True,
                    }			)
  
         _lowercase					:				List[Any]           =			create_model(
             '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''    ,     **UpperCAmelCase_    ,     )
         _lowercase					:				Dict           =			model.patch_embed.patch_size
         _lowercase					:				Optional[int]           =			(args.input_size // patch_size[0], args.input_size // patch_size[1])
         _lowercase					:				Union[str, Any]           =			torch.load(args.beit_checkpoint    ,     map_location='''cpu'''			)
  
         print(F'Load ckpt from {args.beit_checkpoint}'			)
         _lowercase					:				List[Any]           =			None
         for model_key in ("model", "module"):
                if model_key in checkpoint:
                       _lowercase					:				Union[str, Any]           =			checkpoint[model_key]
                       print(F'Load state_dict by model_key = {model_key}'			)
                       break
  
         _lowercase					:				int           =			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:
                       _lowercase					:				List[str]           =			checkpoint_model[key]
                       _lowercase       ,	_lowercase					:				Optional[Any]           =			rel_pos_bias.size()
                       _lowercase       ,	_lowercase					:				Union[str, Any]           =			model.state_dict()[key].size()
                       _lowercase					:				int           =			model.patch_embed.patch_shape
                       if dst_patch_shape[0] != dst_patch_shape[1]:
                              raise NotImplementedError()
  
         load_state_dict(UpperCAmelCase_    ,     UpperCAmelCase_    ,     prefix=''''''			)
  
         return model
  
  def   UpperCamelCase__   (    )						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			get_args()
  
         _lowercase					:				Optional[int]           =			'''ft1k''' in args.hf_checkpoint_name
         _lowercase					:				Any           =			'''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
         _lowercase					:				List[Any]           =			DataaVecVisionConfig()
         if is_finetuned:
                _lowercase					:				Optional[Any]           =			True
                _lowercase					:				Optional[int]           =			False
                _lowercase					:				List[str]           =			True
                _lowercase					:				str           =			1000
  
                _lowercase					:				Union[str, Any]           =			'''huggingface/label-files'''
                _lowercase					:				Tuple           =			'''imagenet-1k-id2label.json'''
                _lowercase					:				List[Any]           =			json.load(open(hf_hub_download(UpperCAmelCase_    ,     UpperCAmelCase_    ,     repo_type='''dataset'''			)    ,     '''r'''			)			)
                _lowercase					:				List[Any]           =			{int(UpperCAmelCase_			): v for k, v in idalabel.items()}
                _lowercase					:				int           =			idalabel
                _lowercase					:				int           =			{v: k for k, v in idalabel.items()}
         else:
                _lowercase					:				Optional[int]           =			False
                _lowercase					:				Tuple           =			True
                _lowercase					:				Optional[Any]           =			False
  
         if is_large:
                _lowercase					:				Tuple           =			1024
                _lowercase					:				Optional[int]           =			4096
                _lowercase					:				Optional[int]           =			24
                _lowercase					:				Any           =			16
  
         # 2. Load Beit model
         _lowercase					:				Tuple           =			load_beit_model(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
         orig_model.eval()
  
         # 3. Forward Beit model
         _lowercase					:				Optional[int]           =			BeitImageProcessor(size=config.image_size    ,     do_center_crop=UpperCAmelCase_			)
         _lowercase					:				str           =			Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''			)
         _lowercase					:				Dict           =			image_processor(images=UpperCAmelCase_    ,     return_tensors='''pt'''			)
         _lowercase					:				Optional[Any]           =			encoding['''pixel_values''']
  
         _lowercase					:				Optional[Any]           =			(pixel_values,) if is_finetuned else (pixel_values, None)
         with torch.no_grad():
                _lowercase					:				Any           =			orig_model(*UpperCAmelCase_			)
  
         # 4. Load HF Data2VecVision model
         if is_finetuned:
                _lowercase					:				Dict           =			DataaVecVisionForImageClassification(UpperCAmelCase_			)
                hf_model.eval()
                _lowercase					:				List[str]           =			False
                _lowercase					:				int           =			'''data2vec_vision.'''
         else:
                _lowercase					:				List[str]           =			DataaVecVisionModel(UpperCAmelCase_			)
                hf_model.eval()
                _lowercase					:				Optional[int]           =			True
                _lowercase					:				int           =			''''''
  
         _lowercase					:				Optional[int]           =			create_rename_keys(UpperCAmelCase_    ,     hf_prefix=UpperCAmelCase_    ,     has_lm_head=UpperCAmelCase_			)
         _lowercase					:				Union[str, Any]           =			orig_model.state_dict()
         for src, dest in rename_keys:
                _lowercase					:				str           =			state_dict.pop(UpperCAmelCase_			)
                _lowercase					:				str           =			val
  
         read_in_q_k_v(UpperCAmelCase_    ,     UpperCAmelCase_    ,     hf_prefix=UpperCAmelCase_    ,     has_lm_head=UpperCAmelCase_			)
         _lowercase       ,	_lowercase					:				Dict           =			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():
                _lowercase					:				List[Any]           =			hf_model(UpperCAmelCase_			)
  
         _lowercase					:				List[Any]           =			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
         # 6. Compare
         _lowercase					:				Any           =			torch.max(torch.abs(hf_output - orig_model_output			)			).item()
  
         print(F'max_absolute_diff = {max_absolute_diff}'			)
         _lowercase					:				Optional[int]           =			torch.allclose(UpperCAmelCase_    ,     UpperCAmelCase_    ,     atol=1E-3			)
         print('''Do both models output the same tensors?'''    ,     '''🔥''' if success else '''💩'''			)
         if not success:
                raise Exception('''Something went wRoNg'''			)
  
         # 7. Save
         print(F'Saving to {args.hf_checkpoint_name}'			)
         hf_model.save_pretrained(args.hf_checkpoint_name			)
         image_processor.save_pretrained(args.hf_checkpoint_name			)
  
  
  if __name__ == "__main__":
     main()
     # Run the following to convert checkpoints
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
style_context: 
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)
  
  UpperCamelCase__				       =			{
      'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
      'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
      'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
  }
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[Any]	,      UpperCamelCase       :       Optional[Any]=2_56	,      UpperCamelCase       :       str=10_24	,      UpperCamelCase       :       Any=16	,      UpperCamelCase       :       Optional[Any]=16	,      UpperCamelCase       :       Union[str, Any]=4	,      UpperCamelCase       :       str="gelu"	,      UpperCamelCase       :       Dict=1E-6	,      **UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			hidden_size
              _lowercase					:				Optional[Any]           =			image_size
              _lowercase					:				Tuple           =			patch_size
              _lowercase					:				List[str]           =			image_size // patch_size
              _lowercase					:				Optional[Any]           =			mask_input_channels
              _lowercase					:				str           =			num_point_embeddings
              _lowercase					:				List[Any]           =			hidden_act
              _lowercase					:				Union[str, Any]           =			layer_norm_eps
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[int]	,      UpperCamelCase       :       str=2_56	,      UpperCamelCase       :       int="relu"	,      UpperCamelCase       :       Any=20_48	,      UpperCamelCase       :       List[Any]=2	,      UpperCamelCase       :       Union[str, Any]=8	,      UpperCamelCase       :       List[str]=2	,      UpperCamelCase       :       Any=3	,      UpperCamelCase       :       List[Any]=3	,      UpperCamelCase       :       int=2_56	,      UpperCamelCase       :       List[str]=1E-6	,      **UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				List[Any]           =			hidden_size
              _lowercase					:				Optional[int]           =			hidden_act
              _lowercase					:				Union[str, Any]           =			mlp_dim
              _lowercase					:				Optional[int]           =			num_hidden_layers
              _lowercase					:				List[str]           =			num_attention_heads
              _lowercase					:				Dict           =			attention_downsample_rate
              _lowercase					:				Optional[Any]           =			num_multimask_outputs
              _lowercase					:				Optional[Any]           =			iou_head_depth
              _lowercase					:				Dict           =			iou_head_hidden_dim
              _lowercase					:				List[str]           =			layer_norm_eps
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]=7_68	,      UpperCamelCase       :       Optional[Any]=2_56	,      UpperCamelCase       :       Optional[Any]=12	,      UpperCamelCase       :       Dict=12	,      UpperCamelCase       :       Dict=3	,      UpperCamelCase       :       Optional[int]=10_24	,      UpperCamelCase       :       Any=16	,      UpperCamelCase       :       Union[str, Any]="gelu"	,      UpperCamelCase       :       int=1E-06	,      UpperCamelCase       :       Tuple=0.0	,      UpperCamelCase       :       List[str]=1E-10	,      UpperCamelCase       :       List[Any]=True	,      UpperCamelCase       :       Any=4.0	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       Optional[int]=True	,      UpperCamelCase       :       Optional[int]=14	,      UpperCamelCase       :       Any=[2, 5, 8, 11]	,      UpperCamelCase       :       Tuple=1_28	,      UpperCamelCase       :       str=None	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
  
              _lowercase					:				Union[str, Any]           =			hidden_size
              _lowercase					:				Optional[int]           =			output_channels
              _lowercase					:				Dict           =			num_hidden_layers
              _lowercase					:				str           =			num_attention_heads
              _lowercase					:				Dict           =			num_channels
              _lowercase					:				List[Any]           =			image_size
              _lowercase					:				int           =			patch_size
              _lowercase					:				Optional[Any]           =			hidden_act
              _lowercase					:				Any           =			layer_norm_eps
              _lowercase					:				Any           =			attention_dropout
              _lowercase					:				Optional[int]           =			initializer_range
              _lowercase					:				List[str]           =			qkv_bias
              _lowercase					:				Optional[Any]           =			mlp_ratio
              _lowercase					:				Optional[Any]           =			use_abs_pos
              _lowercase					:				List[Any]           =			use_rel_pos
              _lowercase					:				str           =			window_size
              _lowercase					:				Tuple           =			global_attn_indexes
              _lowercase					:				List[Any]           =			num_pos_feats
              _lowercase					:				int           =			int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  '''sam'''
       UpperCAmelCase_         =  True
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       Any=None	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       Dict=0.02	,      **UpperCamelCase       :       Union[str, Any]	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				Optional[int]           =			vision_config if vision_config is not None else {}
              _lowercase					:				Any           =			prompt_encoder_config if prompt_encoder_config is not None else {}
              _lowercase					:				Optional[Any]           =			mask_decoder_config if mask_decoder_config is not None else {}
  
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Dict           =			vision_config.to_dict()
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				List[str]           =			prompt_encoder_config.to_dict()
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Optional[int]           =			mask_decoder_config.to_dict()
  
              _lowercase					:				Union[str, Any]           =			SamVisionConfig(**UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			SamPromptEncoderConfig(**UpperCamelCase		)
              _lowercase					:				List[Any]           =			SamMaskDecoderConfig(**UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			initializer_range
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			copy.deepcopy(self.__dict__		)
              _lowercase					:				int           =			self.vision_config.to_dict()
              _lowercase					:				Optional[int]           =			self.prompt_encoder_config.to_dict()
              _lowercase					:				List[Any]           =			self.mask_decoder_config.to_dict()
              _lowercase					:				List[Any]           =			self.__class__.model_type
              return output