


from __future__ import annotations







def   A   (		A__ ):
     UpperCAmelCase						:				Optional[int]		  =			[0] * len(A__ )
     for i in range(1		,		len(A__ ) ):
          UpperCAmelCase						:				List[str]		  =			duration_times[i - 1] + waiting_times[i - 1]
     return waiting_times







def   A   (		A__		,		A__ ):
     return [
         duration_time + waiting_times[i]
         for i, duration_time in enumerate(A__ )
     ]







def   A   (		A__ ):
     return sum(A__ ) / len(A__ )







def   A   (		A__ ):
     return sum(A__ ) / len(A__ )


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

      # get the average times
      _snake_case   = calculate_average_waiting_time(waiting_times)
      _snake_case   = 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}''')








from typing import TYPE_CHECKING

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


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

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



