code: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import SupportsIndex
  
  import numpy as np
  from scipy.ndimage import convolve
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''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 _lowercase   (  __A							,__A							,__A							,__A = None							,):
  
  
  
   '''simple docstring'''
  
  
  
   if alpha is None:
    __UpperCamelCase				      =  0.1
  
   # Initialize flow
   __UpperCamelCase				      =  np.zeros_like(__A       )
   __UpperCamelCase				      =  np.zeros_like(__A       )
  
   # 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(__A       ):
    __UpperCamelCase				      =  warp(__A							,__A							,__A       )
    __UpperCamelCase				      =  convolve(__A							,__A       ) + convolve(__A							,__A       )
    __UpperCamelCase				      =  convolve(__A							,__A       ) + convolve(__A							,__A       )
    __UpperCamelCase				      =  convolve(__A							,__A       ) + convolve(__A							,-kernel_t       )
  
    __UpperCamelCase				      =  convolve(__A							,__A       )
    __UpperCamelCase				      =  convolve(__A							,__A       )
  
    # 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()
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  