





'''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 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       np.inf

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





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

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

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

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







class __a :





   def __init__(					self				:			List[Any]				,lowerCamelCase				:			Dataset				,lowerCamelCase				:			Union[PathLike, BinaryIO]				,lowerCamelCase				:			Optional[int] = None				,**lowerCamelCase				:			List[Any]				,):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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:
            __SCREAMING_SNAKE_CASE      							=       self._write(file_obj=lowerCamelCase				,batch_size=lowerCamelCase				,**self.parquet_writer_kwargs    )
      else:
         __SCREAMING_SNAKE_CASE      							=       self._write(file_obj=self.path_or_buf				,batch_size=lowerCamelCase				,**self.parquet_writer_kwargs    )
      return written





   def      UpperCAmelCase__     (					self				:			Any				,lowerCamelCase				:			BinaryIO				,lowerCamelCase				:			int				,**lowerCamelCase				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       0
      __SCREAMING_SNAKE_CASE      							=       parquet_writer_kwargs.pop("""path_or_buf"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dataset.features.arrow_schema

      __SCREAMING_SNAKE_CASE      							=       pq.ParquetWriter(lowerCamelCase				,schema=lowerCamelCase				,**lowerCamelCase    )

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












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase							,	params=__UpperCAmelCase      ).content							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       soup.find("""div"""							,	attrs={"""class""": """gs_ri"""}      )
   __SCREAMING_SNAKE_CASE      							=       div.find("""div"""							,	attrs={"""class""": """gs_fl"""}      ).find_all("""a"""      )
   return anchors[2].get_text()


if __name__ == "__main__":
    a        =						{
        "title": (
            "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
            "capacitance, volumetric capacitance, and energy density"
        ),
        "journal": "Chem. Mater.",
        "volume": 30,
        "pages": "3979-3990",
        "year": 2018,
        "hl": "en",
    }
    print(get_citation("https://scholar.google.com/scholar_lookup", params=params))





