

"""simple docstring"""




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

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





class 				lowerCamelCase  (lowercase_     ):

			def __init__(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  NestedDataStructureLike[PathLike]      ,			__UpperCAmelCase			:  Optional[NamedSplit] = None      ,			__UpperCAmelCase			:  Optional[Features] = None      ,			__UpperCAmelCase			:  str = None      ,			__UpperCAmelCase			:  bool = False      ,			__UpperCAmelCase			:  bool = False      ,			__UpperCAmelCase			:  Optional[int] = None      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      List[Any]:
						super().__init__(
						    __UpperCamelCase      ,			split=__UpperCamelCase      ,			features=__UpperCamelCase      ,			cache_dir=__UpperCamelCase      ,			keep_in_memory=__UpperCamelCase      ,			streaming=__UpperCamelCase      ,			num_proc=__UpperCamelCase      ,			**__UpperCamelCase      ,			)
						SCREAMING_SNAKE_CASE__									= path_or_paths if isinstance(__UpperCamelCase      ,			__UpperCamelCase    ) else {self.split: path_or_paths}
						SCREAMING_SNAKE_CASE__									= Csv(
						    cache_dir=__UpperCamelCase      ,			data_files=__UpperCamelCase      ,			features=__UpperCamelCase      ,			**__UpperCamelCase      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      str:
						# Build iterable dataset
						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=__UpperCamelCase      ,			download_mode=__UpperCamelCase      ,			verification_mode=__UpperCamelCase      ,			base_path=__UpperCamelCase      ,			num_proc=self.num_proc      ,			)
									SCREAMING_SNAKE_CASE__									= self.builder.as_dataset(
									    split=self.split      ,			verification_mode=__UpperCamelCase      ,			in_memory=self.keep_in_memory    )
						return dataset





class 				lowerCamelCase  :

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.to_csv_kwargs.pop("""path_or_buf"""      ,			__UpperCamelCase    )
						SCREAMING_SNAKE_CASE__									= self.to_csv_kwargs.pop("""header"""      ,			__UpperCamelCase    )
						SCREAMING_SNAKE_CASE__									= self.to_csv_kwargs.pop("""index"""      ,			__UpperCamelCase    )

						if isinstance(self.path_or_buf      ,			(str, bytes, os.PathLike)    ):
									with open(self.path_or_buf      ,			"""wb+"""    ) as buffer:
												SCREAMING_SNAKE_CASE__									= self._write(file_obj=__UpperCamelCase      ,			header=__UpperCamelCase      ,			index=__UpperCamelCase      ,			**self.to_csv_kwargs    )
						else:
									SCREAMING_SNAKE_CASE__									= self._write(file_obj=self.path_or_buf      ,			header=__UpperCamelCase      ,			index=__UpperCamelCase      ,			**self.to_csv_kwargs    )
						return written

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  str    )				->      List[str]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= args

						SCREAMING_SNAKE_CASE__									= query_table(
						    table=self.dataset.data      ,			key=slice(__UpperCamelCase      ,			offset + self.batch_size    )      ,			indices=self.dataset._indices      ,			)
						SCREAMING_SNAKE_CASE__									= batch.to_pandas().to_csv(
						    path_or_buf=__UpperCamelCase      ,			header=header if (offset == 0) else False      ,			index=__UpperCamelCase      ,			**__UpperCamelCase    )
						return csv_str.encode(self.encoding    )





			def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  BinaryIO      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Dict      ,			**__UpperCAmelCase			:  Optional[int]    )				->      Any:
						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 CSV from Arrow format"""      ,			):
												SCREAMING_SNAKE_CASE__									= self._batch_csv((offset, header, index, to_csv_kwargs)    )
												written += file_obj.write(__UpperCamelCase    )

						else:
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= len(self.dataset    ), self.batch_size
									with multiprocessing.Pool(self.num_proc    ) as pool:
												for csv_str in logging.tqdm(
												    pool.imap(
												        self._batch_csv      ,			[(offset, header, index, to_csv_kwargs) for offset in range(0      ,			__UpperCamelCase      ,			__UpperCamelCase    )]      ,			)      ,			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 CSV from Arrow format"""      ,			):
															written += file_obj.write(__UpperCamelCase    )

						return written




"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if start == end:
						return start if lst[start] > item else start + 1
			if start > end:
						return start

			SCREAMING_SNAKE_CASE__									= (start + end) // 2
			if lst[mid] < item:
						return binary_search(snake_case__						,			snake_case__						,			mid + 1						,			snake_case__      )
			elif lst[mid] > item:
						return binary_search(snake_case__						,			snake_case__						,			snake_case__						,			mid - 1      )
			else:
						return mid

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

			for index in range(1						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= lst[index]
						SCREAMING_SNAKE_CASE__									= binary_search(snake_case__						,			snake_case__						,			0						,			index - 1      )
						SCREAMING_SNAKE_CASE__									= lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

			return lst

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if not left:
						return right

			if not right:
						return left

			if left[0] < right[0]:
						return [left[0], *merge(left[1:]						,			snake_case__      )]

			return [right[0], *merge(snake_case__						,			right[1:]      )]

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= [], []
			SCREAMING_SNAKE_CASE__									= [lst[0]]
			SCREAMING_SNAKE_CASE__									= []
			SCREAMING_SNAKE_CASE__									= 1
			while i < length:
						if lst[i] < lst[i - 1]:
									runs.append(snake_case__      )
									SCREAMING_SNAKE_CASE__									= [lst[i]]
						else:
									new_run.append(lst[i]      )
						i += 1
			runs.append(snake_case__      )

			for run in runs:
						sorted_runs.append(insertion_sort(snake_case__      )      )
			for run in sorted_runs:
						SCREAMING_SNAKE_CASE__									= merge(snake_case__						,			snake_case__      )

			return sorted_array

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
			SCREAMING_SNAKE_CASE__									= tim_sort(snake_case__      )
			print(snake_case__      )


if __name__ == "__main__":
						main()


