
"""simple docstring"""




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__      :    str    							= 5_0_0_0_0
UpperCAmelCase__      :    List[str]    							= 5_0_0_0

UpperCAmelCase__							,				UpperCAmelCase__      :    Optional[int]    							= os.path.split(__file__)
UpperCAmelCase__      :    Optional[Any]    							= os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def lowercase_  (   _snake_case   ,_snake_case						):
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							for i in range(0   ,len(_snake_case						)   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   dataset[i : i + batch_size]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(0   ,_snake_case   ,_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       int           =   dataset[i : i + batch_size]

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
							SCREAMING_SNAKE_CASE__						:       Optional[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, {"""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}),
							]

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

														print("""shuffling dataset"""						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   dataset.shuffle()
														print("""Second set of iterations (after shuffling"""						)
														for func, kwargs in functions_shuffled:
																					print("""shuffled """   ,func.__name__   ,str(_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   func(
																					    _snake_case   ,**_snake_case						)

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


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











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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





