


from collections import deque






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   None:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		process_name  # process name
           _lowercase       :							List[Any]      		=		arrival_time  # arrival time of the process
           # completion time of finished process or last interrupted time
           _lowercase       :							Tuple      		=		arrival_time
           _lowercase       :							Any      		=		burst_time  # remaining burst time
           _lowercase       :							Optional[int]      		=		0  # total time of the process wait in ready queue
           _lowercase       :							Union[str, Any]      		=		0  # time from arrival time to completion time






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   None:




           """simple docstring"""

           _lowercase       :							List[str]      		=		number_of_queues
           # time slice of queues that round robin algorithm applied
           _lowercase       :							str      		=		time_slices
           # unfinished process is in this ready_queue
           _lowercase       :							Optional[Any]      		=		queue
           # current time
           _lowercase       :							Union[str, Any]      		=		current_time
           # finished process is in this sequence queue
           _lowercase       :							deque[Process]      		=		deque()
       def 			UpperCamelCase     (					self)       ->   list[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		[]
           for i in range(len(self.finish_queue)):
               sequence.append(self.finish_queue[i].process_name)
           return sequence
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		[]
           for i in range(len(lowerCamelCase)):
               waiting_times.append(queue[i].waiting_time)
           return waiting_times
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		[]
           for i in range(len(lowerCamelCase)):
               turnaround_times.append(queue[i].turnaround_time)
           return turnaround_times
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		[]
           for i in range(len(lowerCamelCase)):
               completion_times.append(queue[i].stop_time)
           return completion_times
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           return [q.burst_time for q in queue]
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




           """simple docstring"""

           process.waiting_time += self.current_time - process.stop_time
           return process.waiting_time
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   deque[Process]:




           """simple docstring"""

           _lowercase       :							deque[Process]      		=		deque()  # sequence deque of finished process
           while len(lowerCamelCase) != 0:
               _lowercase       :							List[Any]      		=		ready_queue.popleft()  # current process

               # if process's arrival time is later than current time, update current time
               if self.current_time < cp.arrival_time:
                   self.current_time += cp.arrival_time

               # update waiting time of current process
               self.update_waiting_time(lowerCamelCase)
               # update current time
               self.current_time += cp.burst_time
               # finish the process and set the process's burst-time 0
               _lowercase       :							Optional[int]      		=		0
               # set the process's turnaround time because it is finished
               _lowercase       :							str      		=		self.current_time - cp.arrival_time
               # set the completion time
               _lowercase       :							Any      		=		self.current_time
               # add the process to queue that has finished queue
               finished.append(lowerCamelCase)

           self.finish_queue.extend(lowerCamelCase)  # add finished process to finish queue
           # FCFS will finish all remaining processes
           return finished
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   tuple[deque[Process], deque[Process]]:




           """simple docstring"""

           _lowercase       :							deque[Process]      		=		deque()  # sequence deque of terminated process
           # just for 1 cycle and unfinished processes will go back to queue
           for _ in range(len(lowerCamelCase)):
               _lowercase       :							int      		=		ready_queue.popleft()  # current process

               # if process's arrival time is later than current time, update current time
               if self.current_time < cp.arrival_time:
                   self.current_time += cp.arrival_time

               # update waiting time of unfinished processes
               self.update_waiting_time(lowerCamelCase)
               # if the burst time of process is bigger than time-slice
               if cp.burst_time > time_slice:
                   # use CPU for only time-slice
                   self.current_time += time_slice
                   # update remaining burst time
                   cp.burst_time -= time_slice
                   # update end point time
                   _lowercase       :							str      		=		self.current_time
                   # locate the process behind the queue because it is not finished
                   ready_queue.append(lowerCamelCase)
               else:
                   # use CPU for remaining burst time
                   self.current_time += cp.burst_time
                   # set burst time 0 because the process is finished
                   _lowercase       :							str      		=		0
                   # set the finish time
                   _lowercase       :							Union[str, Any]      		=		self.current_time
                   # update the process' turnaround time because it is finished
                   _lowercase       :							List[str]      		=		self.current_time - cp.arrival_time
                   # add the process to queue that has finished queue
                   finished.append(lowerCamelCase)

           self.finish_queue.extend(lowerCamelCase)  # add finished process to finish queue
           # return finished processes queue and remaining processes queue
           return finished, ready_queue




       def 			UpperCamelCase     (					self)       ->   deque[Process]:




           """simple docstring"""

           for i in range(self.number_of_queues - 1):
               _lowercase		,				_lowercase       :							str      		=		self.round_robin(
                   self.ready_queue,      self.time_slices[i])
           #  the last queue has first_come_first_served algorithm
           self.first_come_first_served(self.ready_queue)

           return self.finish_queue


if __name__ == "__main__":
 import doctest

 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= Process("P1", 0, 53)
 SCREAMING_SNAKE_CASE       :      str												= Process("P2", 0, 17)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P3", 0, 68)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P4", 0, 24)
 SCREAMING_SNAKE_CASE       :      Optional[int]												= 3
 SCREAMING_SNAKE_CASE       :      List[str]												= [17, 25]
 SCREAMING_SNAKE_CASE       :      List[Any]												= deque([Pa, Pa, Pa, Pa])

 if len(time_slices) != number_of_queues - 1:
  raise SystemExit(0)

 doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})

 SCREAMING_SNAKE_CASE       :      List[str]												= Process("P1", 0, 53)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P2", 0, 17)
 SCREAMING_SNAKE_CASE       :      List[str]												= Process("P3", 0, 68)
 SCREAMING_SNAKE_CASE       :      Tuple												= Process("P4", 0, 24)
 SCREAMING_SNAKE_CASE       :      List[str]												= 3
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= [17, 25]
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= deque([Pa, Pa, Pa, Pa])
 SCREAMING_SNAKE_CASE       :      str												= MLFQ(number_of_queues, time_slices, queue, 0)
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= mlfq.multi_level_feedback_queue()

 # print total waiting times of processes(P1, P2, P3, P4)
 print(
     F"waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
 )
 # print completion times of processes(P1, P2, P3, P4)
 print(
     F"completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
 )
 # print total turnaround times of processes(P1, P2, P3, P4)
 print(
     F"turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
 )
 # print sequence of finished processes
 print(
     F"sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"
 )






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							ShapEImgaImgPipeline
      lowerCAmelCase__					:     List[str]  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							[
          """num_images_per_prompt""",
          """num_inference_steps""",
          """generator""",
          """latents""",
          """guidance_scale""",
          """frame_size""",
          """output_type""",
          """return_dict""",
      ]
      lowerCAmelCase__					:     Tuple  					=							False



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return self.time_input_dim * 4



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return 8



      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size   ,						image_size=64   ,						projection_dim=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_channels=3   ,						num_hidden_layers=5   ,						patch_size=1   ,						)

             lowercase__      			= CLIPVisionModel(UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= CLIPImageProcessor(
                 crop_size=224   ,						do_center_crop=UpperCamelCase   ,						do_normalize=UpperCamelCase   ,						do_resize=UpperCamelCase   ,						image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,						image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,						resample=3   ,						size=224   ,						)

             return image_processor



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''num_attention_heads''': 2,
                 '''attention_head_dim''': 16,
                 '''embedding_dim''': self.time_input_dim,
                 '''num_embeddings''': 32,
                 '''embedding_proj_dim''': self.text_embedder_hidden_size,
                 '''time_embed_dim''': self.time_embed_dim,
                 '''num_layers''': 1,
                 '''clip_embed_dim''': self.time_input_dim * 2,
                 '''additional_embeddings''': 0,
                 '''time_embed_act_fn''': '''gelu''',
                 '''norm_in_type''': '''layer''',
                 '''embedding_proj_norm_type''': '''layer''',
                 '''encoder_hid_proj_type''': None,
                 '''added_emb_type''': None,
             }

             lowercase__      			= PriorTransformer(**UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''param_shapes''': (
                     (self.renderer_dim, 93),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                 ),
                 '''d_latent''': self.time_input_dim,
                 '''d_hidden''': self.renderer_dim,
                 '''n_output''': 12,
                 '''background''': (
                     0.1,
                     0.1,
                     0.1,
                 ),
             }
             lowercase__      			= ShapERenderer(**UpperCamelCase )
             return model



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.dummy_prior
             lowercase__      			= self.dummy_image_encoder
             lowercase__      			= self.dummy_image_processor
             lowercase__      			= self.dummy_renderer

             lowercase__      			= HeunDiscreteScheduler(
                 beta_schedule='''exp'''   ,						num_train_timesteps=1024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase   ,						clip_sample=UpperCamelCase   ,						clip_sample_range=1.0   ,						)
             lowercase__      			= {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''image_processor''': image_processor,
                 '''renderer''': renderer,
                 '''scheduler''': scheduler,
             }

             return components



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str=0 ):

             '''simple docstring'''







             lowercase__      			= floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )

             if str(UpperCamelCase ).startswith('''mps''' ):
                    lowercase__      			= torch.manual_seed(UpperCamelCase )
             else:
                    lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(UpperCamelCase )
             lowercase__      			= {
                 '''image''': input_image,
                 '''generator''': generator,
                 '''num_inference_steps''': 1,
                 '''frame_size''': 32,
                 '''output_type''': '''np''',
             }
             return inputs



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= '''cpu'''

             lowercase__      			= self.get_dummy_components()

             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )

             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= pipe(**self.get_dummy_inputs(UpperCamelCase ) )
             lowercase__      			= output.images[0]
             lowercase__      			= image[0, -3:, -3:, -1]

             assert image.shape == (20, 32, 32, 3)

             lowercase__      			= np.array(
                 [
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                 ] )

             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             self._test_inference_batch_consistent(batch_sizes=[1, 2] )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= torch_device == '''cpu'''
             lowercase__      			= True
             self._test_inference_batch_single_identical(
                 batch_size=2   ,						test_max_difference=UpperCamelCase   ,						relax_max_difference=UpperCamelCase   ,						)






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.get_dummy_components()
             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= 1
             lowercase__      			= 2

             lowercase__      			= self.get_dummy_inputs(UpperCamelCase )

             for key in inputs.keys():
                    if key in self.batch_params:
                           lowercase__      			= batch_size * [inputs[key]]

             lowercase__      			= pipe(**UpperCamelCase   ,						num_images_per_prompt=UpperCamelCase )[0]

             assert images.shape[0] == batch_size * num_images_per_prompt






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





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
             lowercase__      			= load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/shap_e/test_shap_e_img2img_out.npy''' )
             lowercase__      			= ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(0 )

             lowercase__      			= pipe(
                 UpperCamelCase   ,						generator=UpperCamelCase   ,						guidance_scale=3.0   ,						num_inference_steps=64   ,						frame_size=64   ,						output_type='''np'''   ,						).images[0]

             assert images.shape == (20, 64, 64, 3)

             assert_mean_pixel_difference(UpperCamelCase   ,						UpperCamelCase )




