from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   np.stack((horizontal_flow, vertical_flow)				,						2	)

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

			# Find the locations outside of the original image
			_snake_case														=   (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]	))
			_snake_case														=   0

			_snake_case														=   image[grid[:, :, 1], grid[:, :, 0]]

			# Set pixels at invalid locations to 0
			_snake_case														=   0

			return warped






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_ = None				,						):



			"""simple docstring"""





			if alpha is None:
						_snake_case														=   0.1

			# Initialize flow
			_snake_case														=   np.zeros_like(__SCREAMING_SNAKE_CASE	)
			_snake_case														=   np.zeros_like(__SCREAMING_SNAKE_CASE	)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			_snake_case														=   np.array([[-1, 1], [-1, 1]]	) * 0.2_5
			_snake_case														=   np.array([[-1, -1], [1, 1]]	) * 0.2_5
			_snake_case														=   np.array([[1, 1], [1, 1]]	) * 0.2_5
			_snake_case														=   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	):
						_snake_case														=   warp(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	)
						_snake_case														=   convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	) + convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	)
						_snake_case														=   convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	) + convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	)
						_snake_case														=   convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	) + convolve(__SCREAMING_SNAKE_CASE				,						-kernel_t	)

						_snake_case														=   convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	)
						_snake_case														=   convolve(__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE	)

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

						_snake_case														=   avg_horizontal_velocity - derivative_x * update
						_snake_case														=   avg_vertical_velocity - derivative_y * update

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
			import doctest

			doctest.testmod()



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



