





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	 ={
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""DebertaV2TokenizerFast"""]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDebertaV2ForMaskedLM""",
						    """TFDebertaV2ForQuestionAnswering""",
						    """TFDebertaV2ForSequenceClassification""",
						    """TFDebertaV2ForTokenClassification""",
						    """TFDebertaV2Model""",
						    """TFDebertaV2PreTrainedModel""",
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DebertaV2ForMaskedLM""",
						    """DebertaV2ForMultipleChoice""",
						    """DebertaV2ForQuestionAnswering""",
						    """DebertaV2ForSequenceClassification""",
						    """DebertaV2ForTokenClassification""",
						    """DebertaV2Model""",
						    """DebertaV2PreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











'''simple docstring'''







from __future__ import annotations


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   [0] * len(snake_case__				)
      for i in range(1				,      len(snake_case__				)				):
            __A		 =   duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(snake_case__				)
      ]


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


if __name__ == "__main__":
       # process id's
       __A   :							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
       __A   :							List[Any]     	=						[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   :							Dict     	=						calculate_waiting_times(duration_times)
       __A   :							Optional[int]     	=						calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       __A   :							Dict     	=						calculate_average_waiting_time(waiting_times)
       __A   :							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}""")


