import copy

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


_lowerCAmelCase        =       logging.get_logger(__name__)

_lowerCAmelCase        =       {
    '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(    snake_case_				):






 """simple docstring"""




 def __init__(    self ,       __magic_name__=256 ,       __magic_name__=1024 ,       __magic_name__=16 ,       __magic_name__=16 ,       __magic_name__=4 ,       __magic_name__="gelu" ,       __magic_name__=1e-6 ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(**_lowerCAmelCase      )
        A_							:       List[Any]	      =				hidden_size
        A_							:       Dict	      =				image_size
        A_							:       Optional[Any]	      =				patch_size
        A_							:       Tuple	      =				image_size // patch_size
        A_							:       List[Any]	      =				mask_input_channels
        A_							:       Optional[Any]	      =				num_point_embeddings
        A_							:       int	      =				hidden_act
        A_							:       Dict	      =				layer_norm_eps






class     __UpperCAmelCase(    snake_case_				):






 """simple docstring"""




 def __init__(    self ,       __magic_name__=256 ,       __magic_name__="relu" ,       __magic_name__=2048 ,       __magic_name__=2 ,       __magic_name__=8 ,       __magic_name__=2 ,       __magic_name__=3 ,       __magic_name__=3 ,       __magic_name__=256 ,       __magic_name__=1e-6 ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(**_lowerCAmelCase      )
        A_							:       Dict	      =				hidden_size
        A_							:       List[str]	      =				hidden_act
        A_							:       Any	      =				mlp_dim
        A_							:       Any	      =				num_hidden_layers
        A_							:       Union[str, Any]	      =				num_attention_heads
        A_							:       Optional[Any]	      =				attention_downsample_rate
        A_							:       Dict	      =				num_multimask_outputs
        A_							:       Optional[int]	      =				iou_head_depth
        A_							:       Optional[int]	      =				iou_head_hidden_dim
        A_							:       List[Any]	      =				layer_norm_eps






class     __UpperCAmelCase(    snake_case_				):






 """simple docstring"""




 def __init__(    self ,       __magic_name__=768 ,       __magic_name__=256 ,       __magic_name__=12 ,       __magic_name__=12 ,       __magic_name__=3 ,       __magic_name__=1024 ,       __magic_name__=16 ,       __magic_name__="gelu" ,       __magic_name__=1e-06 ,       __magic_name__=0.0 ,       __magic_name__=1e-10 ,       __magic_name__=True ,       __magic_name__=4.0 ,       __magic_name__=True ,       __magic_name__=True ,       __magic_name__=14 ,       __magic_name__=[2, 5, 8, 11] ,       __magic_name__=128 ,       __magic_name__=None ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(**_lowerCAmelCase      )

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






class     __UpperCAmelCase(    snake_case_				):






 """simple docstring"""




 __magic_name__          =   """sam"""
 __magic_name__          =   True
 def __init__(    self ,       __magic_name__=None ,       __magic_name__=None ,       __magic_name__=None ,       __magic_name__=0.02 ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(**_lowerCAmelCase      )
        A_							:       List[str]	      =				vision_config if vision_config is not None else {}
        A_							:       Tuple	      =				prompt_encoder_config if prompt_encoder_config is not None else {}
        A_							:       List[Any]	      =				mask_decoder_config if mask_decoder_config is not None else {}

        if isinstance(_lowerCAmelCase ,       _lowerCAmelCase      ):
               A_							:       str	      =				vision_config.to_dict()
        if isinstance(_lowerCAmelCase ,       _lowerCAmelCase      ):
               A_							:       int	      =				prompt_encoder_config.to_dict()
        if isinstance(_lowerCAmelCase ,       _lowerCAmelCase      ):
               A_							:       Optional[Any]	      =				mask_decoder_config.to_dict()

        A_							:       Union[str, Any]	      =				SamVisionConfig(**_lowerCAmelCase      )
        A_							:       Optional[int]	      =				SamPromptEncoderConfig(**_lowerCAmelCase      )
        A_							:       str	      =				SamMaskDecoderConfig(**_lowerCAmelCase      )
        A_							:       List[Any]	      =				initializer_range
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				copy.deepcopy(self.__dict__      )
        A_							:       Dict	      =				self.vision_config.to_dict()
        A_							:       int	      =				self.prompt_encoder_config.to_dict()
        A_							:       Any	      =				self.mask_decoder_config.to_dict()
        A_							:       int	      =				self.__class__.model_type
        return output







import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()