code: 
  
  
  
  
  
  
  import json
  import os
  import tempfile
  
  import datasets
  from utils import generate_example_dataset, get_duration
  
  
  SCREAMING_SNAKE_CASE__		:       str   							=							50_00_00
  
  SCREAMING_SNAKE_CASE__							, SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							os.path.split(__file__)
  SCREAMING_SNAKE_CASE__		:       Dict   							=							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
  
  
  
  
  
  @get_duration
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> List[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Optional[Any]										= dataset.select(range(0   ,       len(__lowerCamelCase					)   ,       2					)					)
  
  
  
  
  
  @get_duration
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= dataset.sort("""numbers"""					)
  
  
  
  
  
  @get_duration
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> List[str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= dataset.shuffle()
  
  
  
  
  
  @get_duration
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= dataset.train_test_split(0.1					)
  
  
  
  
  
  @get_duration
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase=10					)   -> int:
  
       '''simple docstring'''
       for shard_id in range(__lowerCamelCase					):
            UpperCAmelCase__			:     str										= dataset.shard(__lowerCamelCase   ,       __lowerCamelCase					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= {"""num examples""": SPEED_TEST_N_EXAMPLES}
       UpperCAmelCase__			:     Dict										= (select, sort, shuffle, train_test_split, shard)
       with tempfile.TemporaryDirectory() as tmp_dir:
            print("""generating dataset"""					)
            UpperCAmelCase__			:     Dict										= datasets.Features({"""text""": datasets.Value("""string"""					), """numbers""": datasets.Value("""float32"""					)}					)
            UpperCAmelCase__			:     Dict										= generate_example_dataset(
                os.path.join(__lowerCamelCase   ,       """dataset.arrow"""					)   ,       __lowerCamelCase   ,       num_examples=__lowerCamelCase					)
            print("""Functions"""					)
            for func in functions:
                 print(func.__name__					)
                 UpperCAmelCase__			:     Dict										= func(__lowerCamelCase					)
  
       with open(__lowerCamelCase   ,       """wb"""					) as f:
            f.write(json.dumps(__lowerCamelCase					).encode("""utf-8"""					)					)
  
  
  if __name__ == "__main__":  # useful to run the profiler
        benchmark_indices_mapping()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import math
  import os
  
  import cva
  import numpy as np
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
       if mse == 0:
            return 100
  
       return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> None:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
       # Loading images (original image and compressed image)
       UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
       UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)
  
       UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
       UpperCAmelCase__			:     int										= cva.imread(
           os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)
  
       # Value expected: 29.73dB
       print("""-- First Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  