


"""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      _snake_case						(						_snake_case				:			Features			):
      lowerCAmelCase					:	int       =   np.inf

      def set_batch_size(_snake_case				:			FeatureType			) -> None:
            nonlocal batch_size
            if isinstance(_snake_case ,   _snake_case			):
                  lowerCAmelCase					:	int       =   min(_snake_case ,   config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
            elif isinstance(_snake_case ,   _snake_case			):
                  lowerCAmelCase					:	str       =   min(_snake_case ,   config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
            elif isinstance(_snake_case ,   _snake_case			) and feature.dtype == "binary":
                  lowerCAmelCase					:	Dict       =   min(_snake_case ,   config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS			)

      _visit(_snake_case ,   _snake_case			)

      return None if batch_size is np.inf else batch_size



class 		snake_case_(       a__			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					NestedDataStructureLike[PathLike]					,				UpperCamelCase_  :					Optional[NamedSplit] = None					,				UpperCamelCase_  :					Optional[Features] = None					,				UpperCamelCase_  :					str = None					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					Optional[int] = None					,				**UpperCamelCase_  :					List[str]					,				):
           super().__init__(
               UpperCamelCase_					,				split=UpperCamelCase_					,				features=UpperCamelCase_					,				cache_dir=UpperCamelCase_					,				keep_in_memory=UpperCamelCase_					,				streaming=UpperCamelCase_					,				num_proc=UpperCamelCase_					,				**UpperCamelCase_					,				)
           lowerCAmelCase					:	Optional[int]       =   path_or_paths if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else {self.split: path_or_paths}
           lowerCAmelCase					:	Optional[Any]       =   _PACKAGED_DATASETS_MODULES['''parquet'''][1]
           lowerCAmelCase					:	Optional[Any]       =   Parquet(
               cache_dir=UpperCamelCase_					,				data_files=UpperCamelCase_					,				features=UpperCamelCase_					,				hash=UpperCamelCase_					,				**UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           # Build iterable dataset
           if self.streaming:
                 lowerCAmelCase					:	Union[str, Any]       =   self.builder.as_streaming_dataset(split=self.split				)
           # Build regular (map-style) dataset
           else:
                 lowerCAmelCase					:	Optional[Any]       =   None
                 lowerCAmelCase					:	List[Any]       =   None
                 lowerCAmelCase					:	Union[str, Any]       =   None
                 lowerCAmelCase					:	str       =   None

                 self.builder.download_and_prepare(
                     download_config=UpperCamelCase_					,				download_mode=UpperCamelCase_					,				verification_mode=UpperCamelCase_					,				base_path=UpperCamelCase_					,				num_proc=self.num_proc					,				)
                 lowerCAmelCase					:	Dict       =   self.builder.as_dataset(
                     split=self.split					,				verification_mode=UpperCamelCase_					,				in_memory=self.keep_in_memory				)
           return dataset



class 		snake_case_:






     def __init__(	self  :					Optional[int]					,				UpperCamelCase_  :					Dataset					,				UpperCamelCase_  :					Union[PathLike, BinaryIO]					,				UpperCamelCase_  :					Optional[int] = None					,				**UpperCamelCase_  :					Union[str, Any]					,				):
           lowerCAmelCase					:	Optional[int]       =   dataset
           lowerCAmelCase					:	str       =   path_or_buf
           lowerCAmelCase					:	Optional[int]       =   batch_size or get_writer_batch_size(dataset.features				)
           lowerCAmelCase					:	Optional[Any]       =   parquet_writer_kwargs






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Optional[Any]       =   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					:	Any       =   self._write(file_obj=UpperCamelCase_					,				batch_size=UpperCamelCase_					,				**self.parquet_writer_kwargs				)
           else:
                 lowerCAmelCase					:	List[str]       =   self._write(file_obj=self.path_or_buf					,				batch_size=UpperCamelCase_					,				**self.parquet_writer_kwargs				)
           return written






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					BinaryIO					,				UpperCamelCase_  :					int					,				**UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	str       =   0
           lowerCAmelCase					:	int       =   parquet_writer_kwargs.pop('''path_or_buf'''					,				UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   self.dataset.features.arrow_schema

           lowerCAmelCase					:	Tuple       =   pq.ParquetWriter(UpperCamelCase_					,				schema=UpperCamelCase_					,				**UpperCamelCase_				)

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






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int = 4000000			):
      lowerCAmelCase					:	int       =   [0, 1]
      lowerCAmelCase					:	List[str]       =   0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]			)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCAmelCase					:	int       =   0
      for j in range(len(_snake_case			) - 1			):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
 print(f"""{solution() = }""")


