





import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	def wrapper(*__snake_case ,**__snake_case							):
		lowerCamelCase__              =	timeit.default_timer()
		lowerCamelCase__              =	func(*__snake_case ,**__snake_case							)
		lowerCamelCase__              =	timeit.default_timer() - starttime
		return delta

	lowerCamelCase__              =	func.__name__

	return wrapper





def lowerCAmelCase__(__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	[]
	lowerCamelCase__              =	seq_shapes or {}
	for i in range(__snake_case							):
		lowerCamelCase__              =	{}
		for col_id, (k, v) in enumerate(features.items()							):
			if isinstance(__snake_case ,_ArrayXD							):
				lowerCamelCase__              =	np.random.rand(*v.shape							).astype(v.dtype							)
			elif isinstance(__snake_case ,datasets.Value							):
				if v.dtype == "string":
					lowerCamelCase__              =	'''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCamelCase__              =	np.random.randint(10 ,size=1							).astype(v.dtype							).item()
			elif isinstance(__snake_case ,datasets.Sequence							):
				while isinstance(__snake_case ,datasets.Sequence							):
					lowerCamelCase__              =	v.feature
				lowerCamelCase__              =	seq_shapes[k]
				lowerCamelCase__              =	np.random.rand(*__snake_case							).astype(v.dtype							)
			lowerCamelCase__              =	data

		dummy_data.append((i, example)							)

	return dummy_data





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	generate_examples(__snake_case ,num_examples=__snake_case ,seq_shapes=__snake_case							)

	with ArrowWriter(features=__snake_case ,path=__snake_case							) as writer:
		for key, record in dummy_data:
			lowerCamelCase__              =	features.encode_example(__snake_case							)
			writer.write(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'							)

	lowerCamelCase__              =	datasets.Dataset.from_file(filename=__snake_case ,info=datasets.DatasetInfo(features=__snake_case							)							)

	return dataset










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


_a						=   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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	return (abs(source - target							) / target) < 0.0_1





@pytest.mark.integration
def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	_TestCommandArgs(dataset=__snake_case ,all_configs=__snake_case ,save_infos=__snake_case							)
	lowerCamelCase__              =	TestCommand(*__snake_case							)
	test_command.run()
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	assert os.path.exists(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict.from_directory(__snake_case							)
	lowerCamelCase__              =	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''': 2351563,
	                    '''num_examples''': 10000,
	                },
	                {
	                    '''name''': '''validation''',
	                    '''num_bytes''': 238418,
	                    '''num_examples''': 1000,
	                },
	            ] ,download_size=3940680 ,dataset_size=2589981 ,)
	    }							)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		lowerCamelCase__       ,			lowerCamelCase__              =	getattr(dataset_infos['''default'''] ,__snake_case							), getattr(expected_dataset_infos['''default'''] ,__snake_case							)
		if key == "num_bytes":
			assert is_apercent_close(__snake_case ,__snake_case							)
		elif key == "splits":
			assert list(__snake_case							) == list(__snake_case							)
			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



