





'''simple docstring'''







import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	Union[str, Any])			->						Union[str, Any]:

     '''simple docstring'''




     _lowercase							:      Union[str, Any]  						=							json.load(open(lowerCAmelCase__       ,  'r'))
     _lowercase							:      Optional[int]  						=							GPTNeoConfig(
         hidden_size=config_json['n_embd']       ,  num_layers=config_json['n_layer']       ,  num_heads=config_json['n_head']       ,  attention_types=config_json['attention_types']       ,  max_position_embeddings=config_json['n_positions']       ,  resid_dropout=config_json['res_dropout']       ,  embed_dropout=config_json['embed_dropout']       ,  attention_dropout=config_json['attn_dropout']       ,  )
     print(F'''Building PyTorch model from configuration: {config}''')
     _lowercase							:      Any  						=							GPTNeoForCausalLM(lowerCAmelCase__)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__)

     # Save pytorch-model
     print(F'''Save PyTorch model to {pytorch_dump_path}''')
     model.save_pretrained(lowerCAmelCase__)


if __name__ == "__main__":
    A						    =							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    A						    =							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A						    =							logging.get_logger(__name__)

A						    =							{
    '''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 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Union[str, Any]   ,UpperCamelCase    : Tuple=256   ,UpperCamelCase    : Union[str, Any]=1024   ,UpperCamelCase    : Any=16   ,UpperCamelCase    : List[str]=16   ,UpperCamelCase    : List[str]=4   ,UpperCamelCase    : Dict="gelu"   ,UpperCamelCase    : Any=1e-6   ,**UpperCamelCase    : List[Any]   ,)		->  str:
           super().__init__(**UpperCamelCase  )
           _lowercase							:      int  						=							hidden_size
           _lowercase							:      List[str]  						=							image_size
           _lowercase							:      Optional[Any]  						=							patch_size
           _lowercase							:      Any  						=							image_size // patch_size
           _lowercase							:      str  						=							mask_input_channels
           _lowercase							:      Optional[Any]  						=							num_point_embeddings
           _lowercase							:      Dict  						=							hidden_act
           _lowercase							:      int  						=							layer_norm_eps



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Any   ,UpperCamelCase    : Any=256   ,UpperCamelCase    : Any="relu"   ,UpperCamelCase    : Dict=2048   ,UpperCamelCase    : Dict=2   ,UpperCamelCase    : List[str]=8   ,UpperCamelCase    : int=2   ,UpperCamelCase    : List[Any]=3   ,UpperCamelCase    : Any=3   ,UpperCamelCase    : Any=256   ,UpperCamelCase    : Any=1e-6   ,**UpperCamelCase    : Union[str, Any]   ,)		->  Tuple:
           super().__init__(**UpperCamelCase  )
           _lowercase							:      str  						=							hidden_size
           _lowercase							:      Union[str, Any]  						=							hidden_act
           _lowercase							:      Optional[Any]  						=							mlp_dim
           _lowercase							:      Tuple  						=							num_hidden_layers
           _lowercase							:      Tuple  						=							num_attention_heads
           _lowercase							:      List[Any]  						=							attention_downsample_rate
           _lowercase							:      Optional[Any]  						=							num_multimask_outputs
           _lowercase							:      List[str]  						=							iou_head_depth
           _lowercase							:      List[str]  						=							iou_head_hidden_dim
           _lowercase							:      Union[str, Any]  						=							layer_norm_eps



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Optional[Any]   ,UpperCamelCase    : int=768   ,UpperCamelCase    : str=256   ,UpperCamelCase    : str=12   ,UpperCamelCase    : int=12   ,UpperCamelCase    : Optional[int]=3   ,UpperCamelCase    : str=1024   ,UpperCamelCase    : Dict=16   ,UpperCamelCase    : Tuple="gelu"   ,UpperCamelCase    : List[str]=1e-06   ,UpperCamelCase    : List[str]=0.0   ,UpperCamelCase    : int=1e-10   ,UpperCamelCase    : List[Any]=True   ,UpperCamelCase    : Optional[int]=4.0   ,UpperCamelCase    : List[Any]=True   ,UpperCamelCase    : Optional[int]=True   ,UpperCamelCase    : Tuple=14   ,UpperCamelCase    : Tuple=[2, 5, 8, 11]   ,UpperCamelCase    : List[str]=128   ,UpperCamelCase    : List[Any]=None   ,**UpperCamelCase    : List[str]   ,)		->  Optional[int]:
           super().__init__(**UpperCamelCase  )

           _lowercase							:      Optional[int]  						=							hidden_size
           _lowercase							:      List[Any]  						=							output_channels
           _lowercase							:      List[Any]  						=							num_hidden_layers
           _lowercase							:      Tuple  						=							num_attention_heads
           _lowercase							:      Union[str, Any]  						=							num_channels
           _lowercase							:      Optional[Any]  						=							image_size
           _lowercase							:      List[str]  						=							patch_size
           _lowercase							:      List[Any]  						=							hidden_act
           _lowercase							:      Dict  						=							layer_norm_eps
           _lowercase							:      List[str]  						=							attention_dropout
           _lowercase							:      Union[str, Any]  						=							initializer_range
           _lowercase							:      Any  						=							qkv_bias
           _lowercase							:      Dict  						=							mlp_ratio
           _lowercase							:      Optional[int]  						=							use_abs_pos
           _lowercase							:      int  						=							use_rel_pos
           _lowercase							:      Dict  						=							window_size
           _lowercase							:      Dict  						=							global_attn_indexes
           _lowercase							:      str  						=							num_pos_feats
           _lowercase							:      List[Any]  						=							int(hidden_size * mlp_ratio  ) if mlp_dim is None else mlp_dim



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Any 		=		"sam"
      lowerCAmelCase__       :      Optional[int] 		=		True

      def __init__(							self    : Optional[Any]   ,UpperCamelCase    : Optional[int]=None   ,UpperCamelCase    : List[str]=None   ,UpperCamelCase    : List[Any]=None   ,UpperCamelCase    : int=0.0_2   ,**UpperCamelCase    : Any   ,)		->  Tuple:
           super().__init__(**UpperCamelCase  )
           _lowercase							:      str  						=							vision_config if vision_config is not None else {}
           _lowercase							:      int  						=							prompt_encoder_config if prompt_encoder_config is not None else {}
           _lowercase							:      List[Any]  						=							mask_decoder_config if mask_decoder_config is not None else {}

           if isinstance(UpperCamelCase   ,UpperCamelCase  ):
                _lowercase							:      Optional[Any]  						=							vision_config.to_dict()
           if isinstance(UpperCamelCase   ,UpperCamelCase  ):
                _lowercase							:      Union[str, Any]  						=							prompt_encoder_config.to_dict()
           if isinstance(UpperCamelCase   ,UpperCamelCase  ):
                _lowercase							:      List[Any]  						=							mask_decoder_config.to_dict()

           _lowercase							:      Optional[int]  						=							SamVisionConfig(**UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							SamPromptEncoderConfig(**UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							SamMaskDecoderConfig(**UpperCamelCase  )
           _lowercase							:      Tuple  						=							initializer_range



      def   _lowerCamelCase		(							self    : List[str]  )		->  Optional[int]:
           _lowercase							:      List[Any]  						=							copy.deepcopy(self.__dict__  )
           _lowercase							:      Any  						=							self.vision_config.to_dict()
           _lowercase							:      Dict  						=							self.prompt_encoder_config.to_dict()
           _lowercase							:      Union[str, Any]  						=							self.mask_decoder_config.to_dict()
           _lowercase							:      List[Any]  						=							self.__class__.model_type
           return output