



'''simple docstring'''


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowercase_      :				List[str]   					=    namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      str						):
						return (abs(source - target						) / target) < 0.01







@pytest.mark.integration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							_TestCommandArgs(dataset=lowercase_ ,  all_configs=lowercase_ ,  save_infos=lowercase_						)
						lowercase        =							TestCommand(*lowercase_						)
						test_command.run()
						lowercase        =							os.path.join(lowercase_ ,  """README.md"""						)
						assert os.path.exists(lowercase_						)
						lowercase        =							DatasetInfosDict.from_directory(lowercase_						)
						lowercase        =							DatasetInfosDict(
						    {
						        """default""": DatasetInfo(
						            features=Features(
						                {
						                    """tokens""": Sequence(Value("""string"""						)						),
						                    """ner_tags""": Sequence(
						                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]						)						),
						                    """langs""": Sequence(Value("""string"""						)						),
						                    """spans""": Sequence(Value("""string"""						)						),
						                }						) ,  splits=[
						                {
						                    """name""": """train""",
						                    """num_bytes""": 235_1563,
						                    """num_examples""": 1_0000,
						                },
						                {
						                    """name""": """validation""",
						                    """num_bytes""": 23_8418,
						                    """num_examples""": 1000,
						                },
						            ] ,  download_size=394_0680 ,  dataset_size=258_9981 ,  )
						    }						)
						assert dataset_infos.keys() == expected_dataset_infos.keys()
						for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
												lowercase     ,   lowercase        =							getattr(dataset_infos["""default"""] ,  lowercase_						), getattr(expected_dataset_infos["""default"""] ,  lowercase_						)
												if key == "num_bytes":
																		assert is_apercent_close(lowercase_ ,  lowercase_						)
												elif key == "splits":
																		assert list(lowercase_						) == list(lowercase_						)
																		for split in result:
																								assert result[split].name == expected[split].name
																								assert result[split].num_examples == expected[split].num_examples
																								assert is_apercent_close(result[split].num_bytes ,  expected[split].num_bytes						)
												else:
																		result == expected







'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_      :				int   					=    50_0000

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







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Dict						):
						lowercase        =							dataset.map(**lowercase_						)







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Optional[int]						):
						lowercase        =							dataset.filter(**lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							{"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase        =							datasets.Features({"""text""": datasets.Value("""string"""						), """numbers""": datasets.Value("""float32"""						)}						)
												lowercase        =							generate_example_dataset(
												    os.path.join(lowercase_ ,  """dataset.arrow"""						) ,  lowercase_ ,  num_examples=lowercase_						)

												lowercase        =							transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,  use_fast=lowercase_						)

												def tokenize(lowercase_    :      Dict						):
																		return tokenizer(examples["""text"""]						)

												lowercase        =							map(lowercase_						)

												lowercase        =							map(lowercase_ ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""numpy"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""pandas"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""torch""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""tensorflow""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lowercase_ ,  batched=lowercase_						)

												lowercase        =							filter(lowercase_						)

												# 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(lowercase_ ,  """wb"""						) as f:
												f.write(json.dumps(lowercase_						).encode("""utf-8"""						)						)


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


