


'''simple docstring'''







def _UpperCamelCase       (     __A    )							->		str:


      '''simple docstring'''



      UpperCamelCase__										=				""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups







def _UpperCamelCase       (     __A    )							->		dict[str, str]:


      '''simple docstring'''



      UpperCamelCase__										=				[chr(i + 65    ) for i in range(26    )]
      # Remove duplicate characters from key
      UpperCamelCase__										=				remove_duplicates(key.upper()    )
      UpperCamelCase__										=				len(__A    )
      # First fill cipher with key characters
      UpperCamelCase__										=				{alphabet[i]: char for i, char in enumerate(__A    )}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(__A    )      ,			26    ):
            UpperCamelCase__										=				alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  UpperCamelCase__										=				alphabet[i - offset]
            UpperCamelCase__										=				char
      return cipher_alphabet







def _UpperCamelCase       (     __A      ,			__A    )							->		str:


      '''simple docstring'''



      return "".join(cipher_map.get(__A      ,			__A    ) for ch in message.upper()    )







def _UpperCamelCase       (     __A      ,			__A    )							->		str:


      '''simple docstring'''



      UpperCamelCase__										=				{v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(__A      ,			__A    ) for ch in message.upper()    )







def _UpperCamelCase       (     )							->		None:


      '''simple docstring'''



      UpperCamelCase__										=				input("Enter message to encode or decode: "    ).strip()
      UpperCamelCase__										=				input("Enter keyword: "    ).strip()
      UpperCamelCase__										=				input("Encipher or decipher? E/D:"    ).strip()[0].lower()
      try:
            UpperCamelCase__										=				{"e": encipher, "d": decipher}[option]
      except KeyError:
            raise KeyError("invalid input option"    )
      UpperCamelCase__										=				create_cipher_map(__A    )
      print(func(__A      ,			__A    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()










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



@dataclass
class       SCREAMING_SNAKE_CASE__  (					datasets.BuilderConfig ):
   __lowerCAmelCase  :   Optional[datasets.Features]						=						None
   __lowerCAmelCase  :   str						=						"utf-8"
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   bool						=						True  # deprecated
   __lowerCAmelCase  :   Optional[int]						=						None  # deprecated
   __lowerCAmelCase  :   int						=						10 << 20  # 10MB
   __lowerCAmelCase  :   Optional[bool]						=						None



class       SCREAMING_SNAKE_CASE__  (					datasets.ArrowBasedBuilder ):
   __lowerCAmelCase  :   int						=						JsonConfig

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    if self.config.block_size is not None:
     logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""						)
     UpperCAmelCase	:					Any	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''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}"						)
    UpperCAmelCase	:					List[Any]	=	dl_manager.download_and_extract(self.config.data_files						)
    if isinstance(_SCREAMING_SNAKE_CASE      ,     (str, list, tuple)						):
     UpperCAmelCase	:					int	=	data_files
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Tuple	=	[files]
     UpperCAmelCase	:					List[str]	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,     gen_kwargs={"""files""": files}						)]
    UpperCAmelCase	:					List[str]	=	[]
    for split_name, files in data_files.items():
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Dict	=	[files]
     UpperCAmelCase	:					str	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      ,     gen_kwargs={"""files""": files}						)						)
    return splits

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      pa.Table:





    '''simple docstring'''




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




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE						)						):
     # 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(_SCREAMING_SNAKE_CASE      ,     encoding=self.config.encoding      ,     errors=self.config.encoding_errors						) as f:
       UpperCAmelCase	:					Union[str, Any]	=	json.load(_SCREAMING_SNAKE_CASE						)

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

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

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