



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}")

import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """runwayml/stable-diffusion-v1-5"""
     SCREAMING_SNAKE_CASE__     				= TextToVideoZeroPipeline.from_pretrained(_lowercase			,      torch_dtype=torch.floataa ).to("""cuda""" )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__     				= torch.Generator(device="""cuda""" ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__     				= """A bear is playing a guitar on Times Square"""
     SCREAMING_SNAKE_CASE__     				= pipe(prompt=_lowercase			,      generator=_lowercase ).images

     SCREAMING_SNAKE_CASE__     				= load_pt(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt""" )

     assert_mean_pixel_difference(_lowercase			,      _lowercase )
