


"""simple docstring"""







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     from .features import FeatureType


lowerCAmelCase__    ,lowerCAmelCase__    ,lowerCAmelCase__				    =       False, False, False


@dataclass
class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       a  :    Optional[int]       =None
       a  :    bool       =True
       a  :    bool       =True
       a  :    Optional[str]       =None
       # Automatically constructed
       a  :    ClassVar[str]       ="dict"
       a  :    ClassVar[Any]       =pa.struct({"bytes": pa.binary(), "path": pa.string()}    )
       a  :    str       =field(default="Audio"       ,   init=lowercase       ,   repr=lowercase    )

       def __call__(						self						):






         """simple docstring"""





         return self.pa_type

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         try:
           import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
         except ImportError as err:
           raise ImportError("To support encoding audio data, please install 'soundfile'."						) from err
         if isinstance(snake_case__		,					snake_case__						):
           return {"bytes": None, "path": value}
         elif isinstance(snake_case__		,					snake_case__						):
           return {"bytes": value, "path": None}
         elif "array" in value:
           # convert the audio array to wav bytes
           lowerCAmelCase					:					List[str]								=							BytesIO()
           sf.write(snake_case__		,					value["array"]		,					value["sampling_rate"]		,					format="wav"						)
           return {"bytes": buffer.getvalue(), "path": None}
         elif value.get("path"						) is not None and os.path.isfile(value["path"]						):
           # we set "bytes": None to not duplicate the data if they're already available locally
           if value["path"].endswith("pcm"						):
             # "PCM" only has raw audio bytes
             if value.get("sampling_rate"						) is None:
               # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
               raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"						)
             if value.get("bytes"						):
               # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
               lowerCAmelCase					:					Any								=							np.frombuffer(value["bytes"]		,					dtype=np.intaa						).astype(np.floataa						) / 32_767
             else:
               lowerCAmelCase					:					List[str]								=							np.memmap(value["path"]		,					dtype="h"		,					mode="r"						).astype(np.floataa						) / 32_767

             lowerCAmelCase					:					Optional[int]								=							BytesIO(bytes()						)
             sf.write(snake_case__		,					snake_case__		,					value["sampling_rate"]		,					format="wav"						)
             return {"bytes": buffer.getvalue(), "path": None}
           else:
             return {"bytes": None, "path": value.get("path"						)}
         elif value.get("bytes"						) is not None or value.get("path"						) is not None:
           # store the audio bytes, and path is used to infer the audio format using the file extension
           return {"bytes": value.get("bytes"						), "path": value.get("path"						)}
         else:
           raise ValueError(
               f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         if not self.decode:
           raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."						)

         lowerCAmelCase					:					Dict								=							(value["path"], BytesIO(value["bytes"]						)) if value["bytes"] is not None else (value["path"], None)
         if path is None and file is None:
           raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""						)

         try:
           import librosa
           import soundfile as sf
         except ImportError as err:
           raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."						) from err

         lowerCAmelCase					:					Any								=							xsplitext(snake_case__						)[1][1:].lower() if path is not None else None
         if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
           raise RuntimeError(
               "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
               "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "						)
         elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
           raise RuntimeError(
               "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
               "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "						)

         if file is None:
           lowerCAmelCase					:					str								=							token_per_repo_id or {}
           lowerCAmelCase					:					str								=							path.split("::"						)[-1]
           try:
             lowerCAmelCase					:					Union[str, Any]								=							string_to_dict(snake_case__		,					config.HUB_DATASETS_URL						)["repo_id"]
             lowerCAmelCase					:					Union[str, Any]								=							token_per_repo_id[repo_id]
           except (ValueError, KeyError):
             lowerCAmelCase					:					Dict								=							None

           with xopen(snake_case__		,					"rb"		,					use_auth_token=snake_case__						) as f:
             lowerCAmelCase					:					Dict								=							sf.read(snake_case__						)

         else:
           lowerCAmelCase					:					Union[str, Any]								=							sf.read(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							array.T
         if self.mono:
           lowerCAmelCase					:					Dict								=							librosa.to_mono(snake_case__						)
         if self.sampling_rate and self.sampling_rate != sampling_rate:
           lowerCAmelCase					:					List[str]								=							librosa.resample(snake_case__		,					orig_sr=snake_case__		,					target_sr=self.sampling_rate						)
           lowerCAmelCase					:					Optional[Any]								=							self.sampling_rate

         return {"path": path, "array": array, "sampling_rate": sampling_rate}

       def 	lowercase__							(						self						):






         """simple docstring"""





         from .features import Value

         if self.decode:
           raise ValueError("Cannot flatten a decoded Audio feature."						)
         return {
             "bytes": Value("binary"						),
             "path": Value("string"						),
         }

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         if pa.types.is_string(storage.type						):
           lowerCAmelCase					:					Any								=							pa.array([None] * len(snake_case__						)		,					type=pa.binary()						)
           lowerCAmelCase					:					Tuple								=							pa.StructArray.from_arrays([bytes_array, storage]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         elif pa.types.is_binary(storage.type						):
           lowerCAmelCase					:					str								=							pa.array([None] * len(snake_case__						)		,					type=pa.string()						)
           lowerCAmelCase					:					List[Any]								=							pa.StructArray.from_arrays([storage, path_array]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         elif pa.types.is_struct(storage.type						) and storage.type.get_all_field_indices("array"						):
           lowerCAmelCase					:					Dict								=							pa.array([Audio().encode_example(snake_case__						) if x is not None else None for x in storage.to_pylist()]						)
         elif pa.types.is_struct(storage.type						):
           if storage.type.get_field_index("bytes"						) >= 0:
             lowerCAmelCase					:					Tuple								=							storage.field("bytes"						)
           else:
             lowerCAmelCase					:					Tuple								=							pa.array([None] * len(snake_case__						)		,					type=pa.binary()						)
           if storage.type.get_field_index("path"						) >= 0:
             lowerCAmelCase					:					Dict								=							storage.field("path"						)
           else:
             lowerCAmelCase					:					List[str]								=							pa.array([None] * len(snake_case__						)		,					type=pa.string()						)
           lowerCAmelCase					:					int								=							pa.StructArray.from_arrays([bytes_array, path_array]		,					["bytes", "path"]		,					mask=storage.is_null()						)
         return array_cast(snake_case__		,					self.pa_type						)







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         @no_op_if_value_is_null
         def path_to_bytes(snake_case__						):
           with xopen(snake_case__		,					"rb"						) as f:
             lowerCAmelCase					:					Tuple								=							f.read()
           return bytes_

         lowerCAmelCase					:					str								=							pa.array(
             [
                 (path_to_bytes(x["path"]						) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]		,					type=pa.binary()		,					)
         lowerCAmelCase					:					List[str]								=							pa.array(
             [os.path.basename(snake_case__						) if path is not None else None for path in storage.field("path"						).to_pylist()]		,					type=pa.string()		,					)
         lowerCAmelCase					:					str								=							pa.StructArray.from_arrays([bytes_array, path_array]		,					["bytes", "path"]		,					mask=bytes_array.is_null()						)
         return array_cast(snake_case__		,					self.pa_type						)









"""simple docstring"""







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__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  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__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







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


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[str]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					int								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				split=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"    ,				[str, list]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Optional[Any]								=							parquet_path
  elif issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Dict								=							[parquet_path]
  lowerCAmelCase					:					Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Any								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Tuple=("train",)    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for split in splits:
    lowerCAmelCase					:					List[Any]								=							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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							tmp_path / "cache"
  lowerCAmelCase					:					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():
    lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(
        {"train": parquet_path}    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							tmp_path / "cache"
  lowerCAmelCase					:					Union[str, Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader({"train": parquet_path}    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if split:
    lowerCAmelCase					:					List[str]								=							{split: parquet_path}
  else:
    lowerCAmelCase					:					List[str]								=							"train"
    lowerCAmelCase					:					str								=							{"train": parquet_path, "test": parquet_path}
  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0
  lowerCAmelCase					:					List[str]								=							pq.ParquetFile(tmp_path / "foo.parquet"    )
  lowerCAmelCase					:					Union[str, Any]								=							pf.read()
  assert dataset.data.table == output_table
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							str(shared_datadir / "test_image_rgb.jpg"    )
  lowerCAmelCase					:					List[str]								=							{"image": [image_path]}
  lowerCAmelCase					:					str								=							Features({"image": Image()}    )
  lowerCAmelCase					:					Optional[Any]								=							Dataset.from_dict(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0

  lowerCAmelCase					:					Any								=							Dataset.from_parquet(str(tmp_path / "foo.parquet"    )    )
  assert dataset.features == reloaded_dataset.features

  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader(str(tmp_path / "foo.parquet"    )    ,				streaming=SCREAMING_SNAKE_CASE    ).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__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert get_writer_batch_size(SCREAMING_SNAKE_CASE    ) == expected





