





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

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




           '''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		=						field
           SCREAMING_SNAKE_CASE		=						path_or_paths if isinstance(lowerCamelCase__						,lowerCamelCase__	) else {self.split: path_or_paths}
           SCREAMING_SNAKE_CASE		=						Json(
               cache_dir=lowerCamelCase__						,data_files=lowerCamelCase__						,features=lowerCamelCase__						,field=lowerCamelCase__						,**lowerCamelCase__						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Union[str, 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 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   Dataset						,lowerCamelCase__						:   Union[PathLike, BinaryIO]						,lowerCamelCase__						:   Optional[int] = None						,lowerCamelCase__						:   Optional[int] = None						,**lowerCamelCase__						:   str						,)					->		Optional[int]:




           '''simple docstring'''


           if num_proc is not None and num_proc <= 0:
                 raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""	)

           SCREAMING_SNAKE_CASE		=						dataset
           SCREAMING_SNAKE_CASE		=						path_or_buf
           SCREAMING_SNAKE_CASE		=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           SCREAMING_SNAKE_CASE		=						num_proc
           SCREAMING_SNAKE_CASE		=						"""utf-8"""
           SCREAMING_SNAKE_CASE		=						to_json_kwargs






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.to_json_kwargs.pop("""path_or_buf"""						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.to_json_kwargs.pop("""orient"""						,"""records"""	)
           SCREAMING_SNAKE_CASE		=						self.to_json_kwargs.pop("""lines"""						,True if orient == """records""" else False	)
           SCREAMING_SNAKE_CASE		=						self.to_json_kwargs.pop("""index"""						,False if orient in ["""split""", """table"""] else True	)
           SCREAMING_SNAKE_CASE		=						self.to_json_kwargs.pop("""compression"""						,lowerCamelCase__	)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                 raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""	)

           if isinstance(self.path_or_buf						,(str, bytes, os.PathLike)	):
                 with fsspec.open(self.path_or_buf						,"""wb"""						,compression=lowerCamelCase__	) as buffer:
                       SCREAMING_SNAKE_CASE		=						self._write(file_obj=lowerCamelCase__						,orient=lowerCamelCase__						,lines=lowerCamelCase__						,index=lowerCamelCase__						,**self.to_json_kwargs	)
           else:
                 if compression:
                       raise NotImplementedError(
                           F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                           """ was passed. Please provide a local path instead."""	)
                 SCREAMING_SNAKE_CASE		=						self._write(
                     file_obj=self.path_or_buf						,orient=lowerCamelCase__						,lines=lowerCamelCase__						,index=lowerCamelCase__						,**self.to_json_kwargs	)
           return written






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Optional[int]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						args

           SCREAMING_SNAKE_CASE		=						query_table(
               table=self.dataset.data						,key=slice(lowerCamelCase__						,offset + self.batch_size	)						,indices=self.dataset._indices						,)
           SCREAMING_SNAKE_CASE		=						batch.to_pandas().to_json(
               path_or_buf=lowerCamelCase__						,orient=lowerCamelCase__						,lines=lowerCamelCase__						,index=lowerCamelCase__						,**lowerCamelCase__	)
           if not json_str.endswith("""\n"""	):
                 json_str += "\n"
           return json_str.encode(self.encoding	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   BinaryIO						,lowerCamelCase__						:   str						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Optional[Any]						,**lowerCamelCase__						:   Any						,)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						0

           if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0						,len(self.dataset	)						,self.batch_size	)						,unit="""ba"""						,disable=not logging.is_progress_bar_enabled()						,desc="""Creating json from Arrow format"""						,):
                       SCREAMING_SNAKE_CASE		=						self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                       written += file_obj.write(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						len(self.dataset	), self.batch_size
                 with multiprocessing.Pool(self.num_proc	) as pool:
                       for json_str in logging.tqdm(
                           pool.imap(
                               self._batch_json						,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,lowerCamelCase__						,lowerCamelCase__	)]						,)						,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,unit="""ba"""						,disable=not logging.is_progress_bar_enabled()						,desc="""Creating json from Arrow format"""						,):
                             written += file_obj.write(lowerCamelCase__	)

           return written









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
      print(F'''{solution() = }''')


