"""simple docstring"""



import copy

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


__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					{
    "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     (    a	):







 """simple docstring"""


 def __init__(   self		:      Optional[int]					,   _snake_case		:      List[Any]=256					,   _snake_case		:      Optional[int]=1024					,   _snake_case		:      List[str]=16					,   _snake_case		:      Any=16					,   _snake_case		:      Optional[int]=4					,   _snake_case		:      List[str]="gelu"					,   _snake_case		:      List[str]=1E-6					,   **_snake_case		:      Union[str, Any]					,   )				->			Tuple:




    '''simple docstring'''






    super().__init__(**_snake_case				)
    a__     				=		hidden_size
    a__     				=		image_size
    a__     				=		patch_size
    a__     				=		image_size // patch_size
    a__     				=		mask_input_channels
    a__     				=		num_point_embeddings
    a__     				=		hidden_act
    a__     				=		layer_norm_eps

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Tuple					,   _snake_case		:      Dict=256					,   _snake_case		:      Dict="relu"					,   _snake_case		:      List[Any]=2048					,   _snake_case		:      List[str]=2					,   _snake_case		:      str=8					,   _snake_case		:      Union[str, Any]=2					,   _snake_case		:      List[str]=3					,   _snake_case		:      Dict=3					,   _snake_case		:      Optional[Any]=256					,   _snake_case		:      Union[str, Any]=1E-6					,   **_snake_case		:      Optional[Any]					,   )				->			List[str]:




    '''simple docstring'''






    super().__init__(**_snake_case				)
    a__     				=		hidden_size
    a__     				=		hidden_act
    a__     				=		mlp_dim
    a__     				=		num_hidden_layers
    a__     				=		num_attention_heads
    a__     				=		attention_downsample_rate
    a__     				=		num_multimask_outputs
    a__     				=		iou_head_depth
    a__     				=		iou_head_hidden_dim
    a__     				=		layer_norm_eps

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


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




    '''simple docstring'''






    super().__init__(**_snake_case				)

    a__     				=		hidden_size
    a__     				=		output_channels
    a__     				=		num_hidden_layers
    a__     				=		num_attention_heads
    a__     				=		num_channels
    a__     				=		image_size
    a__     				=		patch_size
    a__     				=		hidden_act
    a__     				=		layer_norm_eps
    a__     				=		attention_dropout
    a__     				=		initializer_range
    a__     				=		qkv_bias
    a__     				=		mlp_ratio
    a__     				=		use_abs_pos
    a__     				=		use_rel_pos
    a__     				=		window_size
    a__     				=		global_attn_indexes
    a__     				=		num_pos_feats
    a__     				=		int(hidden_size * mlp_ratio				) if mlp_dim is None else mlp_dim

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Any  			="sam"
 a_       :	Any  			=True


 def __init__(   self		:      List[str]					,   _snake_case		:      List[str]=None					,   _snake_case		:      str=None					,   _snake_case		:      str=None					,   _snake_case		:      Union[str, Any]=0.02					,   **_snake_case		:      Tuple					,   )				->			Dict:




    '''simple docstring'''






    super().__init__(**_snake_case				)
    a__     				=		vision_config if vision_config is not None else {}
    a__     				=		prompt_encoder_config if prompt_encoder_config is not None else {}
    a__     				=		mask_decoder_config if mask_decoder_config is not None else {}

    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		vision_config.to_dict()
    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		prompt_encoder_config.to_dict()
    if isinstance(_snake_case					,   _snake_case				):
       a__     				=		mask_decoder_config.to_dict()

    a__     				=		SamVisionConfig(**_snake_case				)
    a__     				=		SamPromptEncoderConfig(**_snake_case				)
    a__     				=		SamMaskDecoderConfig(**_snake_case				)
    a__     				=		initializer_range



 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		copy.deepcopy(self.__dict__				)
    a__     				=		self.vision_config.to_dict()
    a__     				=		self.prompt_encoder_config.to_dict()
    a__     				=		self.mask_decoder_config.to_dict()
    a__     				=		self.__class__.model_type
    return output



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


