'''simple docstring'''




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[int]:
    __lowerCamelCase          =  np.inf

    def set_batch_size(UpperCamelCase__    ) -> None:
        nonlocal batch_size
        if isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
        elif isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
        elif isinstance(UpperCamelCase__    ,  UpperCamelCase__    ) and feature.dtype == "binary":
            __lowerCamelCase          =  min(UpperCamelCase__    ,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

    _visit(UpperCamelCase__    ,  UpperCamelCase__    )

    return None if batch_size is np.inf else batch_size




class    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		Any  ,    a		:		NestedDataStructureLike[PathLike]  ,    a		:		Optional[NamedSplit] = None  ,    a		:		Optional[Features] = None  ,    a		:		str = None  ,    a		:		bool = False  ,    a		:		bool = False  ,    a		:		Optional[int] = None  ,    **a		:		str  ,    ):





         """simple docstring"""



         super().__init__(
             a  ,    split=a  ,    features=a  ,    cache_dir=a  ,    keep_in_memory=a  ,    streaming=a  ,    num_proc=a  ,    **a  ,    )
         __lowerCamelCase          =  path_or_paths if isinstance(a  ,    a   ) else {self.split: path_or_paths}
         __lowerCamelCase          =  _PACKAGED_DATASETS_MODULES['''parquet'''][1]
         __lowerCamelCase          =  Parquet(
             cache_dir=a  ,    data_files=a  ,    features=a  ,    hash=a  ,    **a  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         if self.streaming:
             __lowerCamelCase          =  self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None
             __lowerCamelCase          =  None

             self.builder.download_and_prepare(
                 download_config=a  ,    download_mode=a  ,    verification_mode=a  ,    base_path=a  ,    num_proc=self.num_proc  ,    )
             __lowerCamelCase          =  self.builder.as_dataset(
                 split=self.split  ,    verification_mode=a  ,    in_memory=self.keep_in_memory   )
         return dataset




class    a__				:






     def __init__(			self		:		str  ,    a		:		Dataset  ,    a		:		Union[PathLike, BinaryIO]  ,    a		:		Optional[int] = None  ,    **a		:		Optional[int]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  dataset
         __lowerCamelCase          =  path_or_buf
         __lowerCamelCase          =  batch_size or get_writer_batch_size(dataset.features   )
         __lowerCamelCase          =  parquet_writer_kwargs






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf  ,    (str, bytes, os.PathLike)   ):
             with open(self.path_or_buf  ,    '''wb+'''   ) as buffer:
                 __lowerCamelCase          =  self._write(file_obj=a  ,    batch_size=a  ,    **self.parquet_writer_kwargs   )
         else:
             __lowerCamelCase          =  self._write(file_obj=self.path_or_buf  ,    batch_size=a  ,    **self.parquet_writer_kwargs   )
         return written






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		BinaryIO  ,    a		:		int  ,    **a		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  0
         __lowerCamelCase          =  parquet_writer_kwargs.pop('''path_or_buf'''  ,    a   )
         __lowerCamelCase          =  self.dataset.features.arrow_schema

         __lowerCamelCase          =  pq.ParquetWriter(a  ,    schema=a  ,    **a   )

         for offset in logging.tqdm(
             range(0  ,    len(self.dataset   )  ,    a   )  ,    unit='''ba'''  ,    disable=not logging.is_progress_bar_enabled()  ,    desc='''Creating parquet from Arrow format'''  ,    ):
             __lowerCamelCase          =  query_table(
                 table=self.dataset._data  ,    key=slice(a  ,    offset + batch_size   )  ,    indices=self.dataset._indices if self.dataset._indices is not None else None  ,    )
             writer.write_table(a   )
             written += batch.nbytes
         writer.close()
         return written

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    return "".join(chr(ord(UpperCamelCase__    ) - 32    ) if '''a''' <= char <= '''z''' else char for char in word    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()
