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 			lowercase_				(			_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       List[Any]):
    assert isinstance(_lowerCamelCase		,	_lowerCamelCase)
    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 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       List[Any]):
    lowercase__ :   Tuple    					=				tmp_path / "cache"
    lowercase__ :   str    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        lowercase__ :   Optional[int]    					=				ParquetDatasetReader(_lowerCamelCase		,	cache_dir=_lowerCamelCase		,	keep_in_memory=_lowerCamelCase).read()
    _check_parquet_dataset(_lowerCamelCase		,	_lowerCamelCase)

@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 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[int]    					=				tmp_path / "cache"
    lowercase__ :   int    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    lowercase__ :   Optional[int]    					=				features.copy() if features else default_expected_features
    lowercase__ :   Any    					=				(
        Features({feature: Value(_lowerCamelCase) for feature, dtype in features.items()}) if features is not None else None
    )
    lowercase__ :   Dict    					=				ParquetDatasetReader(_lowerCamelCase		,	features=_lowerCamelCase		,	cache_dir=_lowerCamelCase).read()
    _check_parquet_dataset(_lowerCamelCase		,	_lowerCamelCase)

@pytest.mark.parametrize("split"		,	[None, NamedSplit("train"), "train", "test"])
def 			lowercase_				(			_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       str):
    lowercase__ :   Any    					=				tmp_path / "cache"
    lowercase__ :   str    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    lowercase__ :   str    					=				ParquetDatasetReader(_lowerCamelCase		,	cache_dir=_lowerCamelCase		,	split=_lowerCamelCase).read()
    _check_parquet_dataset(_lowerCamelCase		,	_lowerCamelCase)
    assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"		,	[str, list])
def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       List[Any]):
    if issubclass(_lowerCamelCase		,	_lowerCamelCase):
        lowercase__ :   int    					=				parquet_path
    elif issubclass(_lowerCamelCase		,	_lowerCamelCase):
        lowercase__ :   Optional[Any]    					=				[parquet_path]
    lowercase__ :   Any    					=				tmp_path / "cache"
    lowercase__ :   Union[str, Any]    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    lowercase__ :   List[str]    					=				ParquetDatasetReader(_lowerCamelCase		,	cache_dir=_lowerCamelCase).read()
    _check_parquet_dataset(_lowerCamelCase		,	_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       List[str]=("train",)):
    assert isinstance(_lowerCamelCase		,	_lowerCamelCase)
    for split in splits:
        lowercase__ :   int    					=				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 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       str):
    lowercase__ :   Any    					=				tmp_path / "cache"
    lowercase__ :   List[Any]    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        lowercase__ :   int    					=				ParquetDatasetReader(
            {"train": parquet_path}		,	cache_dir=_lowerCamelCase		,	keep_in_memory=_lowerCamelCase).read()
    _check_parquet_datasetdict(_lowerCamelCase		,	_lowerCamelCase)

@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 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Optional[Any]):
    lowercase__ :   Optional[Any]    					=				tmp_path / "cache"
    lowercase__ :   List[str]    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    lowercase__ :   int    					=				features.copy() if features else default_expected_features
    lowercase__ :   List[Any]    					=				(
        Features({feature: Value(_lowerCamelCase) for feature, dtype in features.items()}) if features is not None else None
    )
    lowercase__ :   List[Any]    					=				ParquetDatasetReader({"train": parquet_path}		,	features=_lowerCamelCase		,	cache_dir=_lowerCamelCase).read()
    _check_parquet_datasetdict(_lowerCamelCase		,	_lowerCamelCase)

@pytest.mark.parametrize("split"		,	[None, NamedSplit("train"), "train", "test"])
def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Dict):
    if split:
        lowercase__ :   List[Any]    					=				{split: parquet_path}
    else:
        lowercase__ :   Dict    					=				"train"
        lowercase__ :   List[str]    					=				{"train": parquet_path, "test": parquet_path}
    lowercase__ :   Optional[int]    					=				tmp_path / "cache"
    lowercase__ :   Any    					=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    lowercase__ :   List[str]    					=				ParquetDatasetReader(_lowerCamelCase		,	cache_dir=_lowerCamelCase).read()
    _check_parquet_datasetdict(_lowerCamelCase		,	_lowerCamelCase		,	splits=list(path.keys()))
    assert all(dataset[split].split == split for split in path.keys())

def 			lowercase_				(			_lowerCamelCase							:       Dict		,	_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Dict    					=				ParquetDatasetWriter(_lowerCamelCase		,	tmp_path / "foo.parquet")
    assert writer.write() > 0
    lowercase__ :   Tuple    					=				pq.ParquetFile(tmp_path / "foo.parquet")
    lowercase__ :   List[str]    					=				pf.read()
    assert dataset.data.table == output_table

def 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       List[Any]):
    lowercase__ :   Any    					=				str(shared_datadir / "test_image_rgb.jpg")
    lowercase__ :   Any    					=				{"image": [image_path]}
    lowercase__ :   Dict    					=				Features({"image": Image()})
    lowercase__ :   Tuple    					=				Dataset.from_dict(_lowerCamelCase		,	features=_lowerCamelCase)
    lowercase__ :   Tuple    					=				ParquetDatasetWriter(_lowerCamelCase		,	tmp_path / "foo.parquet")
    assert writer.write() > 0

    lowercase__ :   List[Any]    					=				Dataset.from_parquet(str(tmp_path / "foo.parquet"))
    assert dataset.features == reloaded_dataset.features

    lowercase__ :   int    					=				ParquetDatasetReader(str(tmp_path / "foo.parquet")		,	streaming=_lowerCamelCase).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 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       List[Any]):
    assert get_writer_batch_size(_lowerCamelCase) == expected







import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  ""
      try:
            with open(_A    ,					"rb"						) as binary_file:
                  lowerCamelCase__		:				Union[str, Any]    =  binary_file.read()
            for dat in data:
                  lowerCamelCase__		:				Optional[Any]    =  F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

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

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

      return "0" * (length_length - 1) + file_length_binary + compressed

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      to_write[i : i + byte_length]
                      for i in range(0    ,					len(_A						)    ,					_A						)
                  ]

                  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:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


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


