'''simple docstring'''

import itertools
import string
from collections.abc import Generator, Iterable
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[Any]						= iter(UpperCAmelCase					)
 while True:
  lowercase__    :   Union[str, Any]						= tuple(itertools.islice(UpperCAmelCase		,					UpperCAmelCase					)					)
  if not chunk:
   return
  yield chunk
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   int						= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]					)
 lowercase__    :   str						= ''''''

 if len(UpperCAmelCase					) < 2:
  return dirty

 for i in range(len(UpperCAmelCase					) - 1					):
  clean += dirty[i]

  if dirty[i] == dirty[i + 1]:
   clean += "X"

 clean += dirty[-1]

 if len(UpperCAmelCase					) & 1:
  clean += "X"

 return clean
def       __UpperCamelCase	(       UpperCAmelCase					):
 # I and J are used interchangeably to allow
 # us to use a 5x5 table (25 letters)
 lowercase__    :   Union[str, Any]						= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
 # we're using a list instead of a '2d' array because it makes the math
 # for setting up the table and doing the actual encoding/decoding simpler
 lowercase__    :   Optional[Any]						= []

 # copy key chars into the table if they are in `alphabet` ignoring duplicates
 for char in key.upper():
  if char not in table and char in alphabet:
   table.append(UpperCAmelCase					)

    # fill the rest of the table in with the remaining alphabet chars
 for char in alphabet:
  if char not in table:
   table.append(UpperCAmelCase					)

 return table
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= generate_table(UpperCAmelCase					)
 lowercase__    :   Dict						= prepare_input(UpperCAmelCase					)
 lowercase__    :   Optional[int]						= ''''''

 # https://en.wikipedia.org/wiki/Playfair_cipher#Description
 for chara, chara in chunker(UpperCAmelCase		,					2					):
  lowercase__			,							lowercase__    :   Optional[int]						= divmod(table.index(UpperCAmelCase					)		,					5					)
  lowercase__			,							lowercase__    :   int						= divmod(table.index(UpperCAmelCase					)		,					5					)

  if rowa == rowa:
   ciphertext += table[rowa * 5 + (cola + 1) % 5]
   ciphertext += table[rowa * 5 + (cola + 1) % 5]
  elif cola == cola:
   ciphertext += table[((rowa + 1) % 5) * 5 + cola]
   ciphertext += table[((rowa + 1) % 5) * 5 + cola]
  else:  # rectangle
   ciphertext += table[rowa * 5 + cola]
   ciphertext += table[rowa * 5 + cola]

 return ciphertext





def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= generate_table(UpperCAmelCase					)
 lowercase__    :   Any						= ''''''

 # https://en.wikipedia.org/wiki/Playfair_cipher#Description
 for chara, chara in chunker(UpperCAmelCase		,					2					):
  lowercase__			,							lowercase__    :   Union[str, Any]						= divmod(table.index(UpperCAmelCase					)		,					5					)
  lowercase__			,							lowercase__    :   Any						= divmod(table.index(UpperCAmelCase					)		,					5					)

  if rowa == rowa:
   plaintext += table[rowa * 5 + (cola - 1) % 5]
   plaintext += table[rowa * 5 + (cola - 1) % 5]
  elif cola == cola:
   plaintext += table[((rowa - 1) % 5) * 5 + cola]
   plaintext += table[((rowa - 1) % 5) * 5 + cola]
  else:  # rectangle
   plaintext += table[rowa * 5 + cola]
   plaintext += table[rowa * 5 + cola]

 return plaintext


'''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


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        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}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  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			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, Any]						= pa_table.append_column(__lowerCAmelCase     ,       pa.array([None] * len(__lowerCAmelCase			)     ,       type=__lowerCAmelCase			)			)
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         # 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(__lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

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

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

         # If the file has one json object per line
         else:
          with open(__lowerCAmelCase     ,       '''rb'''			) as f:
           lowercase__    :   Optional[int]						= 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
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= f.read(self.config.chunksize			)
            if not batch:
             break
            # Finish current line
            try:
             batch += f.readline()
            except (AttributeError, io.UnsupportedOperation):
             batch += readline(__lowerCAmelCase			)
            # PyArrow only accepts utf-8 encoded bytes
            if self.config.encoding != "utf-8":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   str						= paj.read_json(
                   io.BytesIO(__lowerCAmelCase			)     ,       read_options=paj.ReadOptions(block_size=__lowerCAmelCase			)			)
               break
              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
               if (
                   isinstance(__lowerCAmelCase     ,       pa.ArrowInvalid			)
                   and "straddling" not in str(__lowerCAmelCase			)
                   or block_size > len(__lowerCAmelCase			)
               ):
                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(__lowerCAmelCase			)} 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(
                  __lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
               lowercase__    :   Optional[int]						= json.load(__lowerCAmelCase			)
             except json.JSONDecodeError:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise e
             # If possible, parse the file as a list of json objects and exit the loop
             if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):  # list is the only sequence type supported in JSON
              try:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
              except (pa.ArrowInvalid, AttributeError) as e:
               logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
               raise ValueError(F"""Not able to read records in the JSON file at {file}."""			) from None
              yield file_idx, self._cast_table(__lowerCAmelCase			)
              break
             else:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {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(__lowerCAmelCase			)
            batch_idx += 1

