"""simple docstring"""






import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


lowerCAmelCase__  :  List[str]											=     datasets.utils.logging.get_logger(__name__)
@dataclass
class     snake_case	(			datasets.BuilderConfig    ):




					"""simple docstring"""

					snake_case__       =			None
					snake_case__       =			"utf-8"
					snake_case__       =			None
					snake_case__       =			10 << 20  # 10MB
					snake_case__       =			False
					snake_case__       =			"line"






class     snake_case	(			datasets.ArrowBasedBuilder    ):




					"""simple docstring"""

					snake_case__       =			TextConfig







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									return datasets.DatasetInfo(features=self.config.features      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]   ,lowerCamelCase__       :     Any      ):
									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(lowerCamelCase__   ,(str, list, tuple)      ):
													UpperCAmelCase__					=							data_files
													if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							[files]
													UpperCAmelCase__					=							[dl_manager.iter_files(lowerCamelCase__      ) 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(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							[files]
													UpperCAmelCase__					=							[dl_manager.iter_files(lowerCamelCase__      ) for file in files]
													splits.append(datasets.SplitGenerator(name=lowerCamelCase__   ,gen_kwargs={'files': files}      )      )
									return splits







					def 		__lowerCAmelCase						(    self       :     Optional[int]   ,lowerCamelCase__       :     pa.Table      ):
									if self.config.features is not None:
													UpperCAmelCase__					=							self.config.features.arrow_schema
													if all(not require_storage_cast(lowerCamelCase__      ) for feature in self.config.features.values()      ):
																	# cheaper cast
																	UpperCAmelCase__					=							pa_table.cast(lowerCamelCase__      )
													else:
																	# more expensive cast; allows str <-> int/float or str to Audio for example
																	UpperCAmelCase__					=							table_cast(lowerCamelCase__   ,lowerCamelCase__      )
													return pa_table
									else:
													return pa_table.cast(pa.schema({'text': pa.string()}      )      )







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     Any      ):
									UpperCAmelCase__					=							list(self.config.features      ) if self.config.features is not None else ['text']
									for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__      )      ):
													# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
													with open(lowerCamelCase__   ,encoding=self.config.encoding   ,errors=self.config.errors      ) as f:
																	if self.config.sample_by == "line":
																					UpperCAmelCase__					=							0
																					while True:
																									UpperCAmelCase__					=							f.read(self.config.chunksize      )
																									if not batch:
																													break
																									batch += f.readline()  # finish current line
																									# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																									UpperCAmelCase__					=							StringIO(lowerCamelCase__      ).readlines()
																									if not self.config.keep_linebreaks:
																													UpperCAmelCase__					=							[line.rstrip('\n'      ) for line in batch]
																									UpperCAmelCase__					=							pa.Table.from_arrays([pa.array(lowerCamelCase__      )]   ,names=lowerCamelCase__      )
																									# 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 (file_idx, batch_idx), self._cast_table(lowerCamelCase__      )
																									batch_idx += 1
																	elif self.config.sample_by == "paragraph":
																					UpperCAmelCase__					=							0
																					UpperCAmelCase__					=							''
																					while True:
																									UpperCAmelCase__					=							f.read(self.config.chunksize      )
																									if not new_batch:
																													break
																									batch += new_batch
																									batch += f.readline()  # finish current line
																									UpperCAmelCase__					=							batch.split('\n\n'      )
																									UpperCAmelCase__					=							pa.Table.from_arrays(
																									    [pa.array([example for example in batch[:-1] if example]      )]   ,names=lowerCamelCase__      )
																									# 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 (file_idx, batch_idx), self._cast_table(lowerCamelCase__      )
																									batch_idx += 1
																									UpperCAmelCase__					=							batch[-1]
																					if batch:
																									UpperCAmelCase__					=							pa.Table.from_arrays([pa.array([batch]      )]   ,names=lowerCamelCase__      )
																									yield (file_idx, batch_idx), self._cast_table(lowerCamelCase__      )
																	elif self.config.sample_by == "document":
																					UpperCAmelCase__					=							f.read()
																					UpperCAmelCase__					=							pa.Table.from_arrays([pa.array([text]      )]   ,names=lowerCamelCase__      )
																					yield file_idx, self._cast_table(lowerCamelCase__      )




"""simple docstring"""






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = None ,      ):
				if alpha is None:
								UpperCAmelCase__					=							0.1

				# Initialize flow
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)

				# 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 / 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(lowerCamelCase	):
								UpperCAmelCase__					=							warp(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      -kernel_t	)

								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)

								# 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()



