






'''simple docstring'''







from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve






def  lowerCAmelCase__		(					a_  :     np.ndarray			,						a_  :     np.ndarray			,						a_  :     np.ndarray			)   -> np.ndarray:
       UpperCAmelCase__	:   List[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
       UpperCAmelCase__	:   List[Any]												=	np.stack(
           np.meshgrid(np.arange(0			,						image.shape[1]			)			,						np.arange(0			,						image.shape[0]			)			)			,						2			)
       UpperCAmelCase__	:   Any												=	np.round(grid - flow			).astype(np.intaa			)

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

       UpperCAmelCase__	:   Tuple												=	image[grid[:, :, 1], grid[:, :, 0]]

       # Set pixels at invalid locations to 0
       UpperCAmelCase__	:   Optional[Any]												=	0

       return warped






def  lowerCAmelCase__		(					a_  :     np.ndarray			,						a_  :     np.ndarray			,						a_  :     SupportsIndex			,						a_  :     float | None = None			,						)   -> tuple[np.ndarray, np.ndarray]:
       if alpha is None:
              UpperCAmelCase__	:   Dict												=	0.1

       # Initialize flow
       UpperCAmelCase__	:   List[str]												=	np.zeros_like(a_			)
       UpperCAmelCase__	:   List[str]												=	np.zeros_like(a_			)

       # Prepare kernels for the calculation of the derivatives and the average velocity
       UpperCAmelCase__	:   Optional[int]												=	np.array([[-1, 1], [-1, 1]]			) * 0.25
       UpperCAmelCase__	:   List[Any]												=	np.array([[-1, -1], [1, 1]]			) * 0.25
       UpperCAmelCase__	:   List[str]												=	np.array([[1, 1], [1, 1]]			) * 0.25
       UpperCAmelCase__	:   Optional[int]												=	np.array(
           [[1 / 1_2, 1 / 6, 1 / 1_2], [1 / 6, 0, 1 / 6], [1 / 1_2, 1 / 6, 1 / 1_2]]			)

       # Iteratively refine the flow
       for _ in range(a_			):
              UpperCAmelCase__	:   int												=	warp(a_			,						a_			,						a_			)
              UpperCAmelCase__	:   Optional[int]												=	convolve(a_			,						a_			) + convolve(a_			,						a_			)
              UpperCAmelCase__	:   Dict												=	convolve(a_			,						a_			) + convolve(a_			,						a_			)
              UpperCAmelCase__	:   int												=	convolve(a_			,						a_			) + convolve(a_			,						-kernel_t			)

              UpperCAmelCase__	:   Optional[Any]												=	convolve(a_			,						a_			)
              UpperCAmelCase__	:   List[Any]												=	convolve(a_			,						a_			)

              # This updates the flow as proposed in the paper (Step 12)
              UpperCAmelCase__	:   str												=	(
                  derivative_x * avg_horizontal_velocity
                  + derivative_y * avg_vertical_velocity
                  + derivative_t
              )
              UpperCAmelCase__	:   Any												=	update / (alpha**2 + derivative_x**2 + derivative_y**2)

              UpperCAmelCase__	:   Union[str, Any]												=	avg_horizontal_velocity - derivative_x * update
              UpperCAmelCase__	:   Any												=	avg_vertical_velocity - derivative_y * update

       return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor