
"""simple docstring"""




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     lowercase(_lowercase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Any:







         """simple docstring"""



         super().__init__(
             __SCREAMING_SNAKE_CASE     ,				split=__SCREAMING_SNAKE_CASE     ,				features=__SCREAMING_SNAKE_CASE     ,				cache_dir=__SCREAMING_SNAKE_CASE     ,				keep_in_memory=__SCREAMING_SNAKE_CASE     ,				streaming=__SCREAMING_SNAKE_CASE     ,				num_proc=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			field
         a__		=			path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
         a__		=			Json(
             cache_dir=__SCREAMING_SNAKE_CASE     ,				data_files=__SCREAMING_SNAKE_CASE     ,				features=__SCREAMING_SNAKE_CASE     ,				field=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         if self.streaming:
             a__		=			self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
             a__		=			None
             a__		=			None
             a__		=			None
             a__		=			None

             self.builder.download_and_prepare(
                 download_config=__SCREAMING_SNAKE_CASE     ,				download_mode=__SCREAMING_SNAKE_CASE     ,				verification_mode=__SCREAMING_SNAKE_CASE     ,				base_path=__SCREAMING_SNAKE_CASE     ,				num_proc=self.num_proc     ,				)
             a__		=			self.builder.as_dataset(
                 split=self.split     ,				verification_mode=__SCREAMING_SNAKE_CASE     ,				in_memory=self.keep_in_memory   )
         return dataset







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     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.'   )

         a__		=			dataset
         a__		=			path_or_buf
         a__		=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         a__		=			num_proc
         a__		=			'utf-8'
         a__		=			to_json_kwargs



     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			self.to_json_kwargs.pop('path_or_buf'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.to_json_kwargs.pop('orient'     ,				'records'   )
         a__		=			self.to_json_kwargs.pop('lines'     ,				True if orient == 'records' else False   )
         a__		=			self.to_json_kwargs.pop('index'     ,				False if orient in ['split', 'table'] else True   )
         a__		=			self.to_json_kwargs.pop('compression'     ,				__SCREAMING_SNAKE_CASE   )

         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=__SCREAMING_SNAKE_CASE   ) as buffer:
                 a__		=			self._write(file_obj=__SCREAMING_SNAKE_CASE     ,				orient=__SCREAMING_SNAKE_CASE     ,				lines=__SCREAMING_SNAKE_CASE     ,				index=__SCREAMING_SNAKE_CASE     ,				**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.'   )
             a__		=			self._write(
                 file_obj=self.path_or_buf     ,				orient=__SCREAMING_SNAKE_CASE     ,				lines=__SCREAMING_SNAKE_CASE     ,				index=__SCREAMING_SNAKE_CASE     ,				**self.to_json_kwargs   )
         return written



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__	,     a__	,     a__	,     a__	,     a__		=			args

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




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     int:







         """simple docstring"""



         a__		=			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'     ,				):
                 a__		=			self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
                 written += file_obj.write(__SCREAMING_SNAKE_CASE   )
         else:
             a__	,     a__		=			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     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )]     ,				)     ,				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(__SCREAMING_SNAKE_CASE   )

         return written




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


