




"""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        UpperCAmelCase_  (	snake_case						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = False							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		**UpperCamelCase_							,		)		->   Optional[int]:
					super().__init__(
					    UpperCamelCase_							,		split=UpperCamelCase_							,		features=UpperCamelCase_							,		cache_dir=UpperCamelCase_							,		keep_in_memory=UpperCamelCase_							,		streaming=UpperCamelCase_							,		num_proc=UpperCamelCase_							,		**UpperCamelCase_							,		)
					__lowercase	:       Optional[Any]				=   path_or_paths if isinstance(UpperCamelCase_							,		UpperCamelCase_   ) else {self.split: path_or_paths}
					__lowercase	:       Any				=   Csv(
					    cache_dir=UpperCamelCase_							,		data_files=UpperCamelCase_							,		features=UpperCamelCase_							,		**UpperCamelCase_							,		)
		def 					_lowerCamelCase    (		self   )		->   Dict:
					# Build iterable dataset
					if self.streaming:
								__lowercase	:       Dict				=   self.builder.as_streaming_dataset(split=self.split   )
					# Build regular (map-style) dataset
					else:
								__lowercase	:       Union[str, Any]				=   None
								__lowercase	:       List[Any]				=   None
								__lowercase	:       Tuple				=   None
								__lowercase	:       List[Any]				=   None

								self.builder.download_and_prepare(
								    download_config=UpperCamelCase_							,		download_mode=UpperCamelCase_							,		verification_mode=UpperCamelCase_							,		base_path=UpperCamelCase_							,		num_proc=self.num_proc							,		)
								__lowercase	:       Tuple				=   self.builder.as_dataset(
								    split=self.split							,		verification_mode=UpperCamelCase_							,		in_memory=self.keep_in_memory   )
					return dataset


class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		**UpperCamelCase_							,		)		->   str:
					if num_proc is not None and num_proc <= 0:
								raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""   )

					__lowercase	:       Union[str, Any]				=   dataset
					__lowercase	:       str				=   path_or_buf
					__lowercase	:       List[str]				=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					__lowercase	:       Optional[int]				=   num_proc
					__lowercase	:       List[str]				=   '''utf-8'''
					__lowercase	:       Dict				=   to_csv_kwargs
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       int				=   self.to_csv_kwargs.pop('''path_or_buf'''							,		UpperCamelCase_   )
					__lowercase	:       Any				=   self.to_csv_kwargs.pop('''header'''							,		UpperCamelCase_   )
					__lowercase	:       Dict				=   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:
											__lowercase	:       Optional[int]				=   self._write(file_obj=UpperCamelCase_							,		header=UpperCamelCase_							,		index=UpperCamelCase_							,		**self.to_csv_kwargs   )
					else:
								__lowercase	:       List[Any]				=   self._write(file_obj=self.path_or_buf							,		header=UpperCamelCase_							,		index=UpperCamelCase_							,		**self.to_csv_kwargs   )
					return written
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					__lowercase	,__lowercase	,__lowercase	,__lowercase	:       str				=   args

					__lowercase	:       Union[str, Any]				=   query_table(
					    table=self.dataset.data							,		key=slice(UpperCamelCase_							,		offset + self.batch_size   )							,		indices=self.dataset._indices							,		)
					__lowercase	:       Tuple				=   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 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		**UpperCamelCase_   )		->   int:
					__lowercase	:       Union[str, Any]				=   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'''							,		):
											__lowercase	:       Dict				=   self._batch_csv((offset, header, index, to_csv_kwargs)   )
											written += file_obj.write(UpperCamelCase_   )

					else:
								__lowercase	,__lowercase	:       Optional[int]				=   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"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



