






"""simple docstring"""





from __future__ import annotations






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     [0] * len(snake_case_						)
       for i in range(1		,  len(snake_case_						)						):
              lowercase__:							Any         =     duration_times[i - 1] + waiting_times[i - 1]
       return waiting_times






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_						):
       """simple docstring"""





       return sum(snake_case_						) / len(snake_case_						)






def 							A(    snake_case_						):
       """simple docstring"""





       return sum(snake_case_						) / len(snake_case_						)


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_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)