

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


_a									=  (3_0, 4_8_7)
_a									=  (3_6, 1_0_2_4)
_a									=  (1_0_0, 1_0_0)
_a									=  1_0_0

_a									=  datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

_a			,      _a									=  os.path.split(__file__)
_a									=  os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[Any]       ,   SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       Any:

 """simple docstring"""






 with ArrowWriter(features=SCREAMING_SNAKE_CASE       ,   path=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)						) as writer:
  for key, record in dummy_data:
   __lowerCAmelCase:  List[str]        = my_features.encode_example(SCREAMING_SNAKE_CASE						)
   writer.write(SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[Any]        = writer.finalize()







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  int        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 for _ in dataset:
  pass







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  int        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 dataset.set_format('numpy'						)
 for _ in dataset:
  pass







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = 10
 __lowerCAmelCase:  Optional[Any]        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 for i in range(0       ,   len(SCREAMING_SNAKE_CASE						)       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Union[str, Any]        = dataset[i : i + batch_size]







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       Tuple:

 """simple docstring"""






 __lowerCAmelCase:  Any        = 10
 __lowerCAmelCase:  Union[str, Any]        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 dataset.set_format('numpy'						)
 for i in range(0       ,   len(SCREAMING_SNAKE_CASE						)       ,   SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  int        = dataset[i : i + batch_size]







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       List[Any]:

 """simple docstring"""






 __lowerCAmelCase:  int        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 for col in feats:
  __lowerCAmelCase:  Optional[int]        = dataset[col]







@get_duration
def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       Tuple:

 """simple docstring"""






 __lowerCAmelCase:  Optional[Any]        = datasets.Dataset.from_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE       ,   'beta.arrow'						)       ,   info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE						)						)
 dataset.set_format('numpy'						)
 for col in feats:
  __lowerCAmelCase:  Dict        = dataset[col]







def _a					(			)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = {}
 __lowerCAmelCase:  List[Any]        = (
     read_unformated,
     read_formatted_as_numpy,
     read_batch_unformated,
     read_batch_formatted_as_numpy,
     read_col_unformated,
     read_col_formatted_as_numpy,
 )
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCAmelCase:  List[Any]        = datasets.Features({'image': ArrayaD(SCREAMING_SNAKE_CASE       ,   dtype='float32'						)}						)
  __lowerCAmelCase:  Optional[Any]        = generate_examples(features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[str]        = write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  for read_func in read_functions:
   __lowerCAmelCase:  str        = read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 with tempfile.TemporaryDirectory() as tmp_dir:
  # don't use fixed length for fair comparison
  # feats = datasets.Features(
  #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
  # )
  __lowerCAmelCase:  Tuple        = datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'						)						)						)}						)
  __lowerCAmelCase:  Optional[Any]        = generate_examples(
      features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE       ,   seq_shapes={'image': SPEED_TEST_SHAPE}						)
  __lowerCAmelCase:  List[str]        = write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  for read_func in read_functions:
   __lowerCAmelCase:  Optional[Any]        = read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 with tempfile.TemporaryDirectory() as tmp_dir:
  # don't use fixed length for fair comparison
  # feats = datasets.Features(
  #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
  # )
  __lowerCAmelCase:  Any        = datasets.Features({'image': datasets.Sequence(datasets.Value('float32'						)						)}						)
  __lowerCAmelCase:  Any        = generate_examples(
      features=SCREAMING_SNAKE_CASE       ,   num_examples=SCREAMING_SNAKE_CASE       ,   seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}       ,   )
  __lowerCAmelCase:  Union[str, Any]        = write(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  for read_func in read_functions:
   __lowerCAmelCase:  Optional[int]        = read_func(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 with open(SCREAMING_SNAKE_CASE       ,   'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_array_xd()








from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       np.ndarray:

 """simple docstring"""






 __lowerCAmelCase:  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
 __lowerCAmelCase:  List[str]        = np.stack(
     np.meshgrid(np.arange(0       ,   image.shape[1]						)       ,   np.arange(0       ,   image.shape[0]						)						)       ,   2						)
 __lowerCAmelCase:  int        = np.round(grid - flow						).astype(np.intaa						)

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

 __lowerCAmelCase:  List[str]        = image[grid[:, :, 1], grid[:, :, 0]]

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

 return warped







def _a					(			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]:

 """simple docstring"""






 if alpha is None:
  __lowerCAmelCase:  Tuple        = 0.1

 # Initialize flow
 __lowerCAmelCase:  Optional[int]        = np.zeros_like(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = np.zeros_like(SCREAMING_SNAKE_CASE						)

 # Prepare kernels for the calculation of the derivatives and the average velocity
 __lowerCAmelCase:  List[Any]        = np.array([[-1, 1], [-1, 1]]						) * 0.2_5
 __lowerCAmelCase:  int        = np.array([[-1, -1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Tuple        = np.array([[1, 1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Dict        = 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						):
  __lowerCAmelCase:  Optional[int]        = warp(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[str]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[Any]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   -kernel_t						)

  __lowerCAmelCase:  Any        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Dict        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

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

  __lowerCAmelCase:  List[Any]        = avg_horizontal_velocity - derivative_x * update
  __lowerCAmelCase:  Union[str, Any]        = avg_vertical_velocity - derivative_y * update

 return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()





