
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam




class  lowerCamelCase__							(				datasets.BeamBasedBuilder):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:str						)		-> str:
								return datasets.DatasetInfo(
								    features=datasets.Features({"content": datasets.Value("string"						)}						)     ,   supervised_keys=a     ,   )







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:List[str]     ,   a		:Tuple						)		-> Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"examples": get_test_dummy_examples()}						)]







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:Optional[Any]     ,   a		:Tuple						)		-> str:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(a						)




class  lowerCamelCase__							(				datasets.BeamBasedBuilder):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[Any]:
								return datasets.DatasetInfo(
								    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"						)}						)}						)     ,   supervised_keys=a     ,   )







	def    _lowerCamelCase				(					self		:int     ,   a		:Optional[int]     ,   a		:Union[str, Any]						)		-> List[str]:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"examples": get_test_nested_examples()}						)
								]







	def    _lowerCamelCase				(					self		:int     ,   a		:List[Any]     ,   a		:Dict						)		-> Optional[Any]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(a						)




def 	_SCREAMING_SNAKE_CASE					(		)					->    Dict:






							'''simple docstring'''





							return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])]




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@require_beam
	def    _lowerCamelCase				(					self		:List[str]						)		-> Any:
								__UpperCamelCase	:   Union[str, Any]			   =					len(get_test_dummy_examples()						)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															__UpperCamelCase	:   Dict			   =					DummyBeamDataset(cache_dir=a     ,   beam_runner="DirectRunner"						)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   f'{builder.name}-train.arrow'						)						)						)
															self.assertDictEqual(builder.info.features     ,   datasets.Features({"content": datasets.Value("string"						)}						)						)
															__UpperCamelCase	:   List[Any]			   =					builder.as_dataset()
															self.assertEqual(dset["train"].num_rows     ,   a						)
															self.assertEqual(dset["train"].info.splits["train"].num_examples     ,   a						)
															self.assertDictEqual(dset["train"][0]     ,   get_test_dummy_examples()[0][1]						)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]     ,   get_test_dummy_examples()[expected_num_examples - 1][1]						)
															self.assertTrue(
															    os.path.exists(os.path.join(a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   "dataset_info.json"						)						)						)
															del dset







	@require_beam
	def    _lowerCamelCase				(					self		:Tuple						)		-> Optional[int]:
								import apache_beam as beam

								__UpperCamelCase	:   Optional[Any]			   =					beam.io.parquetio.WriteToParquet

								__UpperCamelCase	:   Optional[int]			   =					len(get_test_dummy_examples()						)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															__UpperCamelCase	:   Any			   =					DummyBeamDataset(cache_dir=a     ,   beam_runner="DirectRunner"						)
															with patch("apache_beam.io.parquetio.WriteToParquet"						) as write_parquet_mock:
																						__UpperCamelCase	:   Optional[Any]			   =					partial(a     ,   num_shards=2						)
																						builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   f'{builder.name}-train-00000-of-00002.arrow'						)						)						)
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   f'{builder.name}-train-00000-of-00002.arrow'						)						)						)
															self.assertDictEqual(builder.info.features     ,   datasets.Features({"content": datasets.Value("string"						)}						)						)
															__UpperCamelCase	:   Union[str, Any]			   =					builder.as_dataset()
															self.assertEqual(dset["train"].num_rows     ,   a						)
															self.assertEqual(dset["train"].info.splits["train"].num_examples     ,   a						)
															# Order is not preserved when sharding, so we just check that all the elements are there
															self.assertListEqual(sorted(dset["train"]["content"]						)     ,   sorted(["foo", "bar", "foobar"]						)						)
															self.assertTrue(
															    os.path.exists(os.path.join(a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   "dataset_info.json"						)						)						)
															del dset







	@require_beam
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															__UpperCamelCase	:   List[Any]			   =					DummyBeamDataset(cache_dir=a						)
															self.assertRaises(datasets.builder.MissingBeamOptions     ,   builder.download_and_prepare						)







	@require_beam
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					len(get_test_nested_examples()						)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
															__UpperCamelCase	:   Optional[int]			   =					NestedBeamDataset(cache_dir=a     ,   beam_runner="DirectRunner"						)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   f'{builder.name}-train.arrow'						)						)						)
															self.assertDictEqual(
															    builder.info.features     ,   datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"						)}						)}						)						)
															__UpperCamelCase	:   Optional[int]			   =					builder.as_dataset()
															self.assertEqual(dset["train"].num_rows     ,   a						)
															self.assertEqual(dset["train"].info.splits["train"].num_examples     ,   a						)
															self.assertDictEqual(dset["train"][0]     ,   get_test_nested_examples()[0][1]						)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]     ,   get_test_nested_examples()[expected_num_examples - 1][1]						)
															self.assertTrue(
															    os.path.exists(os.path.join(a     ,   builder.name     ,   "default"     ,   "0.0.0"     ,   "dataset_info.json"						)						)						)
															del dset

from PIL import Image




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Image						,  _lowerCamelCase :			int)					->    Image:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					(259 * (level + 255)) / (255 * (259 - level))

							def contrast(_lowerCamelCase :			int) -> int:
														return int(128 + factor * (c - 128))

							return img.point(_lowerCamelCase)


if __name__ == "__main__":
		# Load image
		with Image.open('image_data/lena.jpg') as img:
				# Change contrast to 170
				lowercase  :					Tuple													=       change_contrast(img, 170)
				cont_img.save('image_data/lena_high_contrast.png', format='png')