


'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowerCAmelCase     :  Optional[Any]											=    datasets.utils.logging.get_logger(__name__)
@dataclass
class 						UpperCamelCase__       (			datasets.BuilderConfig						):



     """simple docstring"""






     __magic_name__           =     1_0_0_0_0
     __magic_name__           =     None
     __magic_name__           =     None
class 						UpperCamelCase__       (			datasets.ArrowBasedBuilder						):



     """simple docstring"""






     __magic_name__           =     ParquetConfig







     def a						(			self ):

            '''simple docstring'''

            return datasets.DatasetInfo(features=self.config.features )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if not self.config.data_files:
                   raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
            _lowerCAmelCase     :				int       =							dl_manager.download_and_extract(self.config.data_files )
            if isinstance(__lowerCamelCase   ,						(str, list, tuple) ):
                   _lowerCAmelCase     :				List[str]       =							data_files
                   if isinstance(__lowerCamelCase   ,						__lowerCamelCase ):
                          _lowerCAmelCase     :				str       =							[files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   _lowerCAmelCase     :				Dict       =							[dl_manager.iter_files(__lowerCamelCase ) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs={'files': files} )]
            _lowerCAmelCase     :				Dict       =							[]
            for split_name, files in data_files.items():
                   if isinstance(__lowerCamelCase   ,						__lowerCamelCase ):
                          _lowerCAmelCase     :				Dict       =							[files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   _lowerCAmelCase     :				Optional[Any]       =							[dl_manager.iter_files(__lowerCamelCase ) for file in files]
                   # Infer features is they are stoed in the arrow schema
                   if self.info.features is None:
                          for file in itertools.chain.from_iterable(__lowerCamelCase ):
                                 with open(__lowerCamelCase   ,						'rb' ) as f:
                                        _lowerCAmelCase     :				Tuple       =							datasets.Features.from_arrow_schema(pq.read_schema(__lowerCamelCase ) )
                                 break
                   splits.append(datasets.SplitGenerator(name=__lowerCamelCase   ,						gen_kwargs={'files': files} ) )
            return splits







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.info.features is not None:
                   # more expensive cast to support nested features with keys in a different order
                   # allows str <-> int/float or str to Audio for example
                   _lowerCAmelCase     :				List[str]       =							table_cast(__lowerCamelCase   ,						self.info.features.arrow_schema )
            return pa_table







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.info.features.arrow_schema if self.info.features is not None else None
            if self.info.features is not None and self.config.columns is not None:
                   if sorted(field.name for field in schema ) != sorted(self.config.columns ):
                          raise ValueError(
                              F'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
            for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase ) ):
                   with open(__lowerCamelCase   ,						'rb' ) as f:
                          _lowerCAmelCase     :				Tuple       =							pq.ParquetFile(__lowerCamelCase )
                          try:
                                 for batch_idx, record_batch in enumerate(
                                     parquet_file.iter_batches(batch_size=self.config.batch_size   ,						columns=self.config.columns ) ):
                                        _lowerCAmelCase     :				Any       =							pa.Table.from_batches([record_batch] )
                                        # Uncomment for debugging (will print the Arrow table size and elements)
                                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                        yield F'{file_idx}_{batch_idx}', self._cast_table(__lowerCamelCase )
                          except ValueError as e:
                                 logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCamelCase )}: {e}' )
                                 raise







import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
      __magic_name__  :  List[Any]					=       """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

      print("""Googling.....""")

      __magic_name__  :  List[Any]					=       F"https://www.google.com/search?q={query}&num=100"

      __magic_name__  :  Union[str, Any]					=       requests.get(
          url,
          headers={"""User-Agent""": str(UserAgent().random)},
      )

      try:
            __magic_name__  :  Optional[Any]					=       (
                BeautifulSoup(res.text, """html.parser""")
                .find("""div""", attrs={"""class""": """yuRUbf"""})
                .find("""a""")
                .get("""href""")
            )

      except AttributeError:
            __magic_name__  :  List[Any]					=       parse_qs(
                BeautifulSoup(res.text, """html.parser""")
                .find("""div""", attrs={"""class""": """kCrYT"""})
                .find("""a""")
                .get("""href""")
            )["""url"""][0]

      webbrowser.open(link)

