
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase  : str						   =							50000
lowercase  : str						   =							5000

lowercase  : List[str]						   =							os.path.split(__file__)
lowercase  : Any						   =							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       for i in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				dataset[i]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       for i in range(0 ,     len(SCREAMING_SNAKE_CASE__    ) ,     SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Any	     =				dataset[i : i + batch_size]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__    ):
              for i in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					int	     =				dataset[i]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__    ):
              for i in range(0 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					List[str]	     =				dataset[i : i + batch_size]







def 						_snake_case(  )			->   Any:
       lowercase							:					List[Any]	     =				{"""num examples""": SPEED_TEST_N_EXAMPLES}
       lowercase							:					Dict	     =				[
           (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}),
       ]

       lowercase							:					Any	     =				[
           (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"""    )
              lowercase							:					Optional[int]	     =				datasets.Features(
                  {"""list""": datasets.Sequence(datasets.Value("""float32"""    )    ), """numbers""": datasets.Value("""float32"""    )}    )
              lowercase							:					str	     =				generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE__ ,     """dataset.arrow"""    ) ,     SCREAMING_SNAKE_CASE__ ,     num_examples=SCREAMING_SNAKE_CASE__ ,     seq_shapes={"""list""": (100,)} ,     )
              print("""first set of iterations"""    )
              for func, kwargs in functions:
                     print(func.__name__ ,     str(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Dict	     =				func(SCREAMING_SNAKE_CASE__ ,     **SCREAMING_SNAKE_CASE__    )

              print("""shuffling dataset"""    )
              lowercase							:					str	     =				dataset.shuffle()
              print("""Second set of iterations (after shuffling"""    )
              for func, kwargs in functions_shuffled:
                     print("""shuffled """ ,     func.__name__ ,     str(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					str	     =				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_iterating()

print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
