'''simple docstring'''





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


SCREAMING_SNAKE_CASE_:     Any  =datasets.utils.logging.get_logger(__name__)




@dataclass
class        __A      ( datasets.BuilderConfig	):
    a__       :						Any          =  None
    a__       :						Any          =  """utf-8"""
    a__       :						Union[str, Any]          =  None
    a__       :						Dict          =  None
    a__       :						str          =  True  # deprecated
    a__       :						Tuple          =  None  # deprecated
    a__       :						Any          =  10 << 20  # 10MB
    a__       :						Dict          =  None




class        __A      ( datasets.ArrowBasedBuilder	):
    a__       :						Optional[int]          =  JsonConfig



    def 					_lowercase						(self    :    Optional[int] ):




         if self.config.block_size is not None:
              logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
              UpperCAmelCase_           =   self.config.block_size
         if self.config.use_threads is not True:
              logger.warning(
                  "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
         if self.config.newlines_in_values is not None:
              raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
         return datasets.DatasetInfo(features=self.config.features )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict ):




         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}""" )
         UpperCAmelCase_           =   dl_manager.download_and_extract(self.config.data_files )
         if isinstance(__a   ,			(str, list, tuple) ):
              UpperCAmelCase_           =   data_files
              if isinstance(__a   ,			__a ):
                   UpperCAmelCase_           =   [files]
              UpperCAmelCase_           =   [dl_manager.iter_files(__a ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,			gen_kwargs={"files": files} )]
         UpperCAmelCase_           =   []
         for split_name, files in data_files.items():
              if isinstance(__a   ,			__a ):
                   UpperCAmelCase_           =   [files]
              UpperCAmelCase_           =   [dl_manager.iter_files(__a ) for file in files]
              splits.append(datasets.SplitGenerator(name=__a   ,			gen_kwargs={"files": files} ) )
         return splits



    def 					_lowercase						(self    :    List[str]   ,			__a    :    pa.Table ):




         if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features ) - set(pa_table.column_names ):
                   UpperCAmelCase_           =   self.config.features.arrow_schema.field(__a ).type
                   UpperCAmelCase_           =   pa_table.append_column(__a   ,			pa.array([None] * len(__a )   ,			type=__a ) )
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              UpperCAmelCase_           =   table_cast(__a   ,			self.config.features.arrow_schema )
         return pa_table






    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any] ):




         for file_idx, file in enumerate(itertools.chain.from_iterable(__a ) ):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                   with open(__a   ,			encoding=self.config.encoding   ,			errors=self.config.encoding_errors ) as f:
                        UpperCAmelCase_           =   json.load(__a )

                   # We keep only the field we are interested in
                   UpperCAmelCase_           =   dataset[self.config.field]

                   # We accept two format: a list of dicts or a dict of lists
                   if isinstance(__a   ,			(list, tuple) ):
                        UpperCAmelCase_           =   set().union(*[row.keys() for row in dataset] )
                        UpperCAmelCase_           =   {col: [row.get(__a ) for row in dataset] for col in keys}
                   else:
                        UpperCAmelCase_           =   dataset
                   UpperCAmelCase_           =   pa.Table.from_pydict(__a )
                   yield file_idx, self._cast_table(__a )

              # If the file has one json object per line
              else:
                   with open(__a   ,			"rb" ) as f:
                        UpperCAmelCase_           =   0
                        # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                        # Set a default minimum value of 16kB if the chunk size is really small
                        UpperCAmelCase_           =   max(self.config.chunksize // 32   ,			16 << 10 )
                        UpperCAmelCase_           =   (
                            self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                        )
                        while True:
                             UpperCAmelCase_           =   f.read(self.config.chunksize )
                             if not batch:
                                  break
                             # Finish current line
                             try:
                                  batch += f.readline()
                             except (AttributeError, io.UnsupportedOperation):
                                  batch += readline(__a )
                             # PyArrow only accepts utf-8 encoded bytes
                             if self.config.encoding != "utf-8":
                                  UpperCAmelCase_           =   batch.decode(self.config.encoding   ,			errors=__a ).encode("utf-8" )
                             try:
                                  while True:
                                       try:
                                            UpperCAmelCase_           =   paj.read_json(
                                                io.BytesIO(__a )   ,			read_options=paj.ReadOptions(block_size=__a ) )
                                            break
                                       except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                            if (
                                                isinstance(__a   ,			pa.ArrowInvalid )
                                                and "straddling" not in str(__a )
                                                or block_size > len(__a )
                                            ):
                                                 raise
                                            else:
                                                 # Increase the block size in case it was too small.
                                                 # The block size will be reset for the next file.
                                                 logger.debug(
                                                     f"""Batch of {len(__a )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""" )
                                                 block_size *= 2
                             except pa.ArrowInvalid as e:
                                  try:
                                       with open(
                                           __a   ,			encoding=self.config.encoding   ,			errors=self.config.encoding_errors ) as f:
                                            UpperCAmelCase_           =   json.load(__a )
                                  except json.JSONDecodeError:
                                       logger.error(f"""Failed to read file \'{file}\' with error {type(__a )}: {e}""" )
                                       raise e
                                  # If possible, parse the file as a list of json objects and exit the loop
                                  if isinstance(__a   ,			__a ):  # list is the only sequence type supported in JSON
                                       try:
                                            UpperCAmelCase_           =   set().union(*[row.keys() for row in dataset] )
                                            UpperCAmelCase_           =   {col: [row.get(__a ) for row in dataset] for col in keys}
                                            UpperCAmelCase_           =   pa.Table.from_pydict(__a )
                                       except (pa.ArrowInvalid, AttributeError) as e:
                                            logger.error(f"""Failed to read file \'{file}\' with error {type(__a )}: {e}""" )
                                            raise ValueError(f"""Not able to read records in the JSON file at {file}.""" ) from None
                                       yield file_idx, self._cast_table(__a )
                                       break
                                  else:
                                       logger.error(f"""Failed to read file \'{file}\' with error {type(__a )}: {e}""" )
                                       raise ValueError(
                                           f"""Not able to read records in the JSON file at {file}. """
                                           f"""You should probably indicate the field of the JSON file containing your records. """
                                           f"""This JSON file contain the following fields: {str(list(dataset.keys() ) )}. """
                                           f"""Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. """ ) from None
                                   # 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 (file_idx, batch_idx), self._cast_table(__a )
                             batch_idx += 1











'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					int ):
   return int((input_a, input_a).count(0 ) == 0 )
def 							UpperCAmelCase_  (						):
   assert and_gate(0	,0 ) == 0
   assert and_gate(0	,1 ) == 0
   assert and_gate(1	,0 ) == 0
   assert and_gate(1	,1 ) == 1


if __name__ == "__main__":
 test_and_gate()
 print(and_gate(1, 0))
 print(and_gate(0, 0))
 print(and_gate(0, 1))
 print(and_gate(1, 1))


