

import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE				) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               _lowerCAmelCase   :					str        =    min(SCREAMING_SNAKE_CASE		,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
          elif isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               _lowerCAmelCase   :					Union[str, Any]        =    min(SCREAMING_SNAKE_CASE		,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
          elif isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				) and feature.dtype == "binary":
               _lowerCAmelCase   :					Optional[int]        =    min(SCREAMING_SNAKE_CASE		,							config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

     _visit(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     return None if batch_size is np.inf else batch_size





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	str     ,						A_ :	NestedDataStructureLike[PathLike]     ,						A_ :	Optional[NamedSplit] = None     ,						A_ :	Optional[Features] = None     ,						A_ :	str = None     ,						A_ :	bool = False     ,						A_ :	bool = False     ,						A_ :	Optional[int] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      super().__init__(
          A_     ,						split=A_     ,						features=A_     ,						cache_dir=A_     ,						keep_in_memory=A_     ,						streaming=A_     ,						num_proc=A_     ,						**A_     ,						)
      _lowerCAmelCase   :					Any        =    path_or_paths if isinstance(A_     ,						A_       ) else {self.split: path_or_paths}
      _lowerCAmelCase   :					Optional[int]        =    _PACKAGED_DATASETS_MODULES["parquet"][1]
      _lowerCAmelCase   :					Dict        =    Parquet(
          cache_dir=A_     ,						data_files=A_     ,						features=A_     ,						hash=A_     ,						**A_     ,						)


 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      if self.streaming:
           _lowerCAmelCase   :					int        =    self.builder.as_streaming_dataset(split=self.split       )
      # Build regular (map-style) dataset
      else:
           _lowerCAmelCase   :					int        =    None
           _lowerCAmelCase   :					List[Any]        =    None
           _lowerCAmelCase   :					Any        =    None
           _lowerCAmelCase   :					Tuple        =    None

           self.builder.download_and_prepare(
               download_config=A_     ,						download_mode=A_     ,						verification_mode=A_     ,						base_path=A_     ,						num_proc=self.num_proc     ,						)
           _lowerCAmelCase   :					Any        =    self.builder.as_dataset(
               split=self.split     ,						verification_mode=A_     ,						in_memory=self.keep_in_memory       )
      return dataset





class A__    :



 """simple docstring"""







 def __init__(		self :	Any     ,						A_ :	Dataset     ,						A_ :	Union[PathLike, BinaryIO]     ,						A_ :	Optional[int] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    dataset
      _lowerCAmelCase   :					str        =    path_or_buf
      _lowerCAmelCase   :					Dict        =    batch_size or get_writer_batch_size(dataset.features       )
      _lowerCAmelCase   :					Dict        =    parquet_writer_kwargs

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

      if isinstance(self.path_or_buf     ,						(str, bytes, os.PathLike)       ):
           with open(self.path_or_buf     ,						"wb+"       ) as buffer:
                _lowerCAmelCase   :					List[str]        =    self._write(file_obj=A_     ,						batch_size=A_     ,						**self.parquet_writer_kwargs       )
      else:
           _lowerCAmelCase   :					Optional[Any]        =    self._write(file_obj=self.path_or_buf     ,						batch_size=A_     ,						**self.parquet_writer_kwargs       )
      return written


 def    __magic_name__       (		self :	Any     ,						A_ :	BinaryIO     ,						A_ :	int     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    0
      _lowerCAmelCase   :					List[str]        =    parquet_writer_kwargs.pop("path_or_buf"     ,						A_       )
      _lowerCAmelCase   :					Tuple        =    self.dataset.features.arrow_schema

      _lowerCAmelCase   :					Any        =    pq.ParquetWriter(A_     ,						schema=A_     ,						**A_       )

      for offset in logging.tqdm(
          range(0     ,						len(self.dataset       )     ,						A_       )     ,						unit="ba"     ,						disable=not logging.is_progress_bar_enabled()     ,						desc="Creating parquet from Arrow format"     ,						):
           _lowerCAmelCase   :					List[Any]        =    query_table(
               table=self.dataset._data     ,						key=slice(A_     ,						offset + batch_size       )     ,						indices=self.dataset._indices if self.dataset._indices is not None else None     ,						)
           writer.write_table(A_       )
           written += batch.nbytes
      writer.close()
      return written









def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      "list[int]":

     """simple docstring"""







     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"				)

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2		,							upper_limit + 1				):
          for j in range(SCREAMING_SNAKE_CASE				):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
  print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
  print('\n*** Enter -1 at any time to quit ***')
  print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
  try:
    while True:
      __UpperCAmelCase    				=     int(input().strip())
      if N < 0:
        print('\n********* Goodbye!! ************')
        break
      else:
        print(f'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print('Try another upper limit for the sequence: ', end='')
  except (NameError, ValueError):
    print('\n********* Invalid input, goodbye! ************\n')

  import doctest

  doctest.testmod()






