



'''simple docstring'''


from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    @staticmethod
    @abstractmethod
    def  A       (  UpperCamelCase__      :					ArgumentParser ):


       """simple docstring"""







       raise NotImplementedError()






    @abstractmethod
    def  A       (  self      :					Any ):


       """simple docstring"""







       raise NotImplementedError()






'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

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


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

