"""simple docstring"""







from __future__ import annotations




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Optional[Any]:







     '''simple docstring'''


     __A					:					Optional[int]								=		[0] * len(lowerCamelCase_						)
     for i in range(1  ,       len(lowerCamelCase_						)						):
          __A					:					Dict								=		duration_times[i - 1] + waiting_times[i - 1]
     return waiting_times




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				List[str]						)    ->     Union[str, Any]:







     '''simple docstring'''


     return [
         duration_time + waiting_times[i]
         for i, duration_time in enumerate(lowerCamelCase_						)
     ]




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any						)    ->     int:







     '''simple docstring'''


     return sum(lowerCamelCase_						) / len(lowerCamelCase_						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]						)    ->     Dict:







     '''simple docstring'''


     return sum(lowerCamelCase_						) / len(lowerCamelCase_						)


if __name__ == "__main__":
       # process id's
       lowerCamelCase      : List[str]       =[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
       lowerCamelCase      : int       =[19, 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
       lowerCamelCase      : Tuple       =calculate_waiting_times(duration_times)
       lowerCamelCase      : str       =calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       lowerCamelCase      : Optional[Any]       =calculate_average_waiting_time(waiting_times)
       lowerCamelCase      : Tuple       =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}')











"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Union[str, Any]=None					,		**__A:  Union[str, Any]     ):

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

												if isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.integer     ):
																	a__ 							=	{'''dtype''': tf.intaa}
												elif isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.floating     ):
																	a__ 							=	{'''dtype''': tf.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(__A					,		torch.Tensor     ):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
												if hasattr(__A					,		'''__array__'''     ) and not isinstance(__A					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

												return map_nested(self._recursive_tensorize					,		__A					,		map_list=__A     )




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch


