



from __future__ import annotations







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					[0] * len(_a    )
    for i in range(1   ,		len(_a    )    ):
        UpperCAmelCase_     :       Any  		=					duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times







def       lowerCamelCase_  (    _a       :						list[int]   ,		_a       :						list[int]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    return sum(_a    ) / len(_a    )







def       lowerCamelCase_  (    _a       :						list[int]    ):



    '''simple docstring'''




    return sum(_a    ) / len(_a    )


if __name__ == "__main__":
      # process id's
      UpperCamelCase_									=   [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
      UpperCamelCase_									=   [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
      UpperCamelCase_									=   calculate_waiting_times(duration_times)
      UpperCamelCase_									=   calculate_turnaround_times(duration_times, waiting_times)

      # get the average times
      UpperCamelCase_									=   calculate_average_waiting_time(waiting_times)
      UpperCamelCase_									=   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'''







from typing import TYPE_CHECKING

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


snake_case_  :							Any            =	{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							str            =	[
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					snake_case_  :							List[str]            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





