"""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 				snake_case__     (   lowerCamelCase__   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(
            __A  ,						split=__A  ,						features=__A  ,						cache_dir=__A  ,						keep_in_memory=__A  ,						streaming=__A  ,						num_proc=__A  ,						**__A  ,						)
        __a     				=  field
        __a     				=  path_or_paths if isinstance(__A  ,						__A		) else {self.split: path_or_paths}
        __a     				=  Json(
            cache_dir=__A  ,						data_files=__A  ,						features=__A  ,						field=__A  ,						**__A  ,						)






 def 		a__ (				self		):
        # Build iterable dataset
        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=__A  ,						download_mode=__A  ,						verification_mode=__A  ,						base_path=__A  ,						num_proc=self.num_proc  ,						)
               __a     				=  self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__A  ,						in_memory=self.keep_in_memory		)
        return dataset




class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        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 		a__ (				self		):
        __a     				=  self.to_json_kwargs.pop("path_or_buf"  ,						__A		)
        __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"  ,						__A		)

        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=__A		) as buffer:
                      __a     				=  self._write(file_obj=__A  ,						orient=__A  ,						lines=__A  ,						index=__A  ,						**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=__A  ,						lines=__A  ,						index=__A  ,						**self.to_json_kwargs		)
        return written


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  args

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






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase  ,						):
        __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(__A		)
        else:
               __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  ,						__A  ,						__A		)]  ,						)  ,						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(__A		)

        return written

"""simple docstring"""



def 					_lowerCamelCase(					a							):
       __a     				=  len(a							)
       for i in range(1 ,  a							):
              __a     				=  collection[i]
              __a     				=  0
              __a     				=  i - 1

              while low <= high:
                     __a     				=  (low + high) // 2
                     if val < collection[mid]:
                            __a     				=  mid - 1
                     else:
                            __a     				=  mid + 1
              for j in range(a ,  a ,  -1							):
                     __a     				=  collection[j - 1]
              __a     				=  val
       return collection


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:str			=	input("""Enter numbers separated by a comma:\n""").strip()
   SCREAMING_SNAKE_CASE__:Any			=	[int(item) for item in user_input.split(""",""")]
   print(binary_insertion_sort(unsorted))
