




from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					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													=					np.stack(
						    np.meshgrid(np.arange(0     ,   image.shape[1]						)     ,   np.arange(0     ,   image.shape[0]						)						)     ,   2						)
						_UpperCAmelCase													=					np.round(grid - flow						).astype(np.intaa						)

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

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

						# Set pixels at invalid locations to 0
						_UpperCAmelCase													=					0

						return warped


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_ = None     ,   ):

						'''simple docstring'''


						if alpha is None:
												_UpperCAmelCase													=					0.1

						# Initialize flow
						_UpperCAmelCase													=					np.zeros_like(snake_case_						)
						_UpperCAmelCase													=					np.zeros_like(snake_case_						)

						# Prepare kernels for the calculation of the derivatives and the average velocity
						_UpperCAmelCase													=					np.array([[-1, 1], [-1, 1]]						) * 0.25
						_UpperCAmelCase													=					np.array([[-1, -1], [1, 1]]						) * 0.25
						_UpperCAmelCase													=					np.array([[1, 1], [1, 1]]						) * 0.25
						_UpperCAmelCase													=					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(snake_case_						):
												_UpperCAmelCase													=					warp(snake_case_     ,   snake_case_     ,   snake_case_						)
												_UpperCAmelCase													=					convolve(snake_case_     ,   snake_case_						) + convolve(snake_case_     ,   snake_case_						)
												_UpperCAmelCase													=					convolve(snake_case_     ,   snake_case_						) + convolve(snake_case_     ,   snake_case_						)
												_UpperCAmelCase													=					convolve(snake_case_     ,   snake_case_						) + convolve(snake_case_     ,   -kernel_t						)

												_UpperCAmelCase													=					convolve(snake_case_     ,   snake_case_						)
												_UpperCAmelCase													=					convolve(snake_case_     ,   snake_case_						)

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

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

						return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()










import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase_     :    Union[str, Any]      =			datasets.utils.logging.get_logger(__name__)






@dataclass
class    __lowerCAmelCase			(	datasets.BuilderConfig			):
			snake_case_				:			Optional[datasets.Features]								=					None






class    __lowerCAmelCase			(	datasets.ArrowBasedBuilder			):
			snake_case_				:			str								=					ArrowConfig







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


									return datasets.DatasetInfo(features=self.config.features				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Optional[int]				):




									"""simple docstring"""


									if not self.config.data_files:
															raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""				)
									_UpperCAmelCase													=					dl_manager.download_and_extract(self.config.data_files				)
									if isinstance(snake_case__   ,   (str, list, tuple)				):
															_UpperCAmelCase													=					data_files
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,   gen_kwargs={"files": files}				)]
									_UpperCAmelCase													=					[]
									for split_name, files in data_files.items():
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(snake_case__				):
																											with open(snake_case__   ,   "rb"				) as f:
																																	_UpperCAmelCase													=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(snake_case__				).schema				)
																											break
															splits.append(datasets.SplitGenerator(name=snake_case__   ,   gen_kwargs={"files": files}				)				)
									return splits







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      pa.Table				):




									"""simple docstring"""


									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_UpperCAmelCase													=					table_cast(snake_case__   ,   self.info.features.arrow_schema				)
									return pa_table







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
															with open(snake_case__   ,   "rb"				) as f:
																					try:
																											for batch_idx, record_batch in enumerate(pa.ipc.open_stream(snake_case__				)				):
																																	_UpperCAmelCase													=					pa.Table.from_batches([record_batch]				)
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__				)
																					except ValueError as e:
																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case__				)}: {e}"""				)
																											raise




