





'''simple docstring'''







from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     np.ndarray,     SCREAMING_SNAKE_CASE__ :     np.ndarray,     SCREAMING_SNAKE_CASE__ :     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_     : Any         =					np.stack(
						    np.meshgrid(np.arange(0,     image.shape[1]				),     np.arange(0,     image.shape[0]				)				),     2				)
						UpperCAmelCase_     : str         =					np.round(grid - flow				).astype(np.intaa				)

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

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

						# Set pixels at invalid locations to 0
						UpperCAmelCase_     : List[str]         =					0

						return warped





def     lowerCamelCase_ (					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]:
						if alpha is None:
												UpperCAmelCase_     : Dict         =					0.1

						# Initialize flow
						UpperCAmelCase_     : str         =					np.zeros_like(SCREAMING_SNAKE_CASE__				)
						UpperCAmelCase_     : str         =					np.zeros_like(SCREAMING_SNAKE_CASE__				)

						# Prepare kernels for the calculation of the derivatives and the average velocity
						UpperCAmelCase_     : Dict         =					np.array([[-1, 1], [-1, 1]]				) * 0.25
						UpperCAmelCase_     : List[str]         =					np.array([[-1, -1], [1, 1]]				) * 0.25
						UpperCAmelCase_     : str         =					np.array([[1, 1], [1, 1]]				) * 0.25
						UpperCAmelCase_     : 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__				):
												UpperCAmelCase_     : Union[str, Any]         =					warp(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : Dict         =					convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				) + convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : Optional[Any]         =					convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				) + convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : List[str]         =					convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				) + convolve(SCREAMING_SNAKE_CASE__,     -kernel_t				)

												UpperCAmelCase_     : Union[str, Any]         =					convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : Dict         =					convolve(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)

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

												UpperCAmelCase_     : str         =					avg_horizontal_velocity - derivative_x * update
												UpperCAmelCase_     : Optional[int]         =					avg_vertical_velocity - derivative_y * update

						return horizontal_flow, vertical_flow


if __name__ == "__main__":
					import doctest

					doctest.testmod()












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





