
'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 							A_  (    __SCREAMING_SNAKE_CASE    :	np.ndarray      ,  __SCREAMING_SNAKE_CASE    :	np.ndarray      ,  __SCREAMING_SNAKE_CASE    :	np.ndarray			)					->							np.ndarray:





  """simple docstring"""

  __A		:  Any       				=		np.stack((horizontal_flow, vertical_flow)      ,  2			)

  # Create a grid of all pixel coordinates and subtract the flow to get the
  # target pixels coordinates
  __A		:  Optional[Any]       				=		np.stack(
      np.meshgrid(np.arange(0      ,  image.shape[1]			)      ,  np.arange(0      ,  image.shape[0]			)			)      ,  2			)
  __A		:  str       				=		np.round(grid - flow			).astype(np.intaa			)

  # Find the locations outside of the original image
  __A		:  List[str]       				=		(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]			))
  __A		:  Optional[Any]       				=		0

  __A		:  Optional[int]       				=		image[grid[:, :, 1], grid[:, :, 0]]

  # Set pixels at invalid locations to 0
  __A		:  Tuple       				=		0

  return warped







def 							A_  (    __SCREAMING_SNAKE_CASE    :	np.ndarray      ,  __SCREAMING_SNAKE_CASE    :	np.ndarray      ,  __SCREAMING_SNAKE_CASE    :	SupportsIndex      ,  __SCREAMING_SNAKE_CASE    :	float | None = None      ,  )					->							tuple[np.ndarray, np.ndarray]:





  """simple docstring"""

  if alpha is None:
    __A		:  Optional[int]       				=		0.1

  # Initialize flow
  __A		:  Optional[int]       				=		np.zeros_like(__SCREAMING_SNAKE_CASE			)
  __A		:  str       				=		np.zeros_like(__SCREAMING_SNAKE_CASE			)

  # Prepare kernels for the calculation of the derivatives and the average velocity
  __A		:  Dict       				=		np.array([[-1, 1], [-1, 1]]			) * 0.25
  __A		:  Tuple       				=		np.array([[-1, -1], [1, 1]]			) * 0.25
  __A		:  List[str]       				=		np.array([[1, 1], [1, 1]]			) * 0.25
  __A		:  Optional[Any]       				=		np.array(
      [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]			)

  # Iteratively refine the flow
  for _ in range(__SCREAMING_SNAKE_CASE			):
    __A		:  List[str]       				=		warp(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) + convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) + convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    __A		:  str       				=		convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) + convolve(__SCREAMING_SNAKE_CASE      ,  -kernel_t			)

    __A		:  Optional[Any]       				=		convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
    __A		:  Any       				=		convolve(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)

    # This updates the flow as proposed in the paper (Step 12)
    __A		:  Optional[int]       				=		(
        derivative_x * avg_horizontal_velocity
        + derivative_y * avg_vertical_velocity
        + derivative_t
    )
    __A		:  List[Any]       				=		update / (alpha**2 + derivative_x**2 + derivative_y**2)

    __A		:  Tuple       				=		avg_horizontal_velocity - derivative_x * update
    __A		:  Dict       				=		avg_vertical_velocity - derivative_y * update

  return horizontal_flow, vertical_flow


if __name__ == "__main__":
     import doctest

     doctest.testmod()





'''simple docstring'''
import copy

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


A__ :		str					=logging.get_logger(__name__)

A__ :		Optional[int]					={
    '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 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Tuple					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Union[str, Any]=10_24					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     Optional[Any]="gelu"					, lowerCamelCase      :     Union[str, Any]=1e-6					, **lowerCamelCase      :     Union[str, Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[str]       				=		hidden_size
   __A		:  Any       				=		image_size
   __A		:  Dict       				=		patch_size
   __A		:  Union[str, Any]       				=		image_size // patch_size
   __A		:  List[Any]       				=		mask_input_channels
   __A		:  str       				=		num_point_embeddings
   __A		:  Tuple       				=		hidden_act
   __A		:  List[str]       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int=2_56					, lowerCamelCase      :     Tuple="relu"					, lowerCamelCase      :     Optional[Any]=20_48					, lowerCamelCase      :     int=2					, lowerCamelCase      :     Tuple=8					, lowerCamelCase      :     int=2					, lowerCamelCase      :     List[str]=3					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     Any=2_56					, lowerCamelCase      :     int=1e-6					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=7_68					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Optional[Any]=12					, lowerCamelCase      :     Tuple=12					, lowerCamelCase      :     int=3					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     int=16					, lowerCamelCase      :     Any="gelu"					, lowerCamelCase      :     Tuple=1e-0_6					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     List[str]=1e-1_0					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Union[str, Any]=4.0					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     str=14					, lowerCamelCase      :     str=[2, 5, 8, 11]					, lowerCamelCase      :     Dict=1_28					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     Optional[int]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''sam'''
 lowerCamelCase												=True
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     List[Any]=0.02					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  str       				=		vision_config if vision_config is not None else {}
   __A		:  int       				=		prompt_encoder_config if prompt_encoder_config is not None else {}
   __A		:  Dict       				=		mask_decoder_config if mask_decoder_config is not None else {}

   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  List[str]       				=		vision_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		prompt_encoder_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		mask_decoder_config.to_dict()

   __A		:  Any       				=		SamVisionConfig(**lowerCamelCase							)
   __A		:  List[Any]       				=		SamPromptEncoderConfig(**lowerCamelCase							)
   __A		:  Union[str, Any]       				=		SamMaskDecoderConfig(**lowerCamelCase							)
   __A		:  Any       				=		initializer_range






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		copy.deepcopy(self.__dict__							)
   __A		:  Dict       				=		self.vision_config.to_dict()
   __A		:  Any       				=		self.prompt_encoder_config.to_dict()
   __A		:  str       				=		self.mask_decoder_config.to_dict()
   __A		:  Optional[int]       				=		self.__class__.model_type
   return output



