
import math
import sys


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		try:
				with open(lowercase					, 'rb'				) as binary_file:
						UpperCamelCase						=  binary_file.read()
				for dat in data:
						UpperCamelCase						=  f'''{dat:08b}'''
						result += curr_byte
				return result
		except OSError:
				print('File not accessible'				)
				sys.exit()


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  {'0': '0', '1': '1'}
		UpperCamelCase			,						UpperCamelCase						=  '', ''
		UpperCamelCase						=  len(lowercase				)

		for i in range(len(lowercase				)				):
				curr_string += data_bits[i]
				if curr_string not in lexicon:
						continue

				UpperCamelCase						=  lexicon[curr_string]
				result += last_match_id
				UpperCamelCase						=  last_match_id + '0'

				if math.loga(lowercase				).is_integer():
						UpperCamelCase						=  {}
						for curr_key in list(lowercase				):
								UpperCamelCase						=  lexicon.pop(lowercase				)
						UpperCamelCase						=  new_lex

				UpperCamelCase						=  last_match_id + '1'
				index += 1
				UpperCamelCase						=  ''
		return result


def    A (      lowercase					, lowercase				)     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  8
		try:
				with open(lowercase					, 'wb'				) as opened_file:
						UpperCamelCase						=  [
						    to_write[i : i + byte_length]
						    for i in range(0					, len(lowercase				)					, lowercase				)
						]

						if len(result_byte_array[-1]				) % byte_length == 0:
								result_byte_array.append('10000000'				)
						else:
								result_byte_array[-1] += "1" + "0" * (
								    byte_length - len(result_byte_array[-1]				) - 1
								)

						for elem in result_byte_array[:-1]:
								opened_file.write(int(lowercase					, 2				).to_bytes(1					, byteorder='big'				)				)
		except OSError:
				print('File not accessible'				)
				sys.exit()


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  0
		for letter in data_bits:
				if letter == "1":
						break
				counter += 1

		UpperCamelCase						=  data_bits[counter:]
		UpperCamelCase						=  data_bits[counter + 1 :]
		return data_bits


def    A (      lowercase					, lowercase				)     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  read_file_binary(lowercase				)
		UpperCamelCase						=  remove_prefix(lowercase				)
		UpperCamelCase						=  decompress_data(lowercase				)
		write_file_binary(lowercase					, lowercase				)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'					, [False, True]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase					, keep_in_memory=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


@pytest.mark.parametrize(
    'features'					, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					, )
def    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, features=lowercase					, cache_dir=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase					, split=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)
		assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type'					, [str, list]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if issubclass(lowercase					, lowercase				):
				UpperCamelCase						=  parquet_path
		elif issubclass(lowercase					, lowercase				):
				UpperCamelCase						=  [parquet_path]
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


def    A (      lowercase					, lowercase					, lowercase=("train",)				)     ->						Tuple:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		for split in splits:
				UpperCamelCase						=  dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'					, [False, True]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  ParquetDatasetReader(
				    {'train': parquet_path}					, cache_dir=lowercase					, keep_in_memory=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase				)


@pytest.mark.parametrize(
    'features'					, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					, )
def    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  ParquetDatasetReader({'train': parquet_path}					, features=lowercase					, cache_dir=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase				)


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if split:
				UpperCamelCase						=  {split: parquet_path}
		else:
				UpperCamelCase						=  'train'
				UpperCamelCase						=  {'train': parquet_path, 'test': parquet_path}
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase					, splits=list(path.keys()				)				)
		assert all(dataset[split].split == split for split in path.keys()				)


def    A (      lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  ParquetDatasetWriter(lowercase					, tmp_path / 'foo.parquet'				)
		assert writer.write() > 0
		UpperCamelCase						=  pq.ParquetFile(tmp_path / 'foo.parquet'				)
		UpperCamelCase						=  pf.read()
		assert dataset.data.table == output_table


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  str(shared_datadir / 'test_image_rgb.jpg'				)
		UpperCamelCase						=  {'image': [image_path]}
		UpperCamelCase						=  Features({'image': Image()}				)
		UpperCamelCase						=  Dataset.from_dict(lowercase					, features=lowercase				)
		UpperCamelCase						=  ParquetDatasetWriter(lowercase					, tmp_path / 'foo.parquet'				)
		assert writer.write() > 0

		UpperCamelCase						=  Dataset.from_parquet(str(tmp_path / 'foo.parquet'				)				)
		assert dataset.features == reloaded_dataset.features

		UpperCamelCase						=  ParquetDatasetReader(str(tmp_path / 'foo.parquet'				)					, streaming=lowercase				).read()
		assert dataset.features == reloaded_iterable_dataset.features


@pytest.mark.parametrize(
    'feature, expected'					, [
        (Features({'foo': Value('int32'				)}				), None),
        (Features({'image': Image(), 'foo': Value('int32'				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]					, )
def    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		assert get_writer_batch_size(lowercase				) == expected
