





from __future__ import annotations

import pandas as pd



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     )				->				list[int]:







   """simple docstring"""
   lowerCamelCase__   :						Union[str, Any]					   =							[0] * no_of_processes
   lowerCamelCase__   :						Optional[int]					   =							[0] * no_of_processes
   # Copy the burst time into remaining_time[]
   for i in range(__lowerCamelCase     ):
      lowerCamelCase__   :						Any					   =							burst_time[i]

   lowerCamelCase__   :						str					   =							0
   lowerCamelCase__   :						List[str]					   =							0
   lowerCamelCase__   :						Optional[Any]					   =							999999999
   lowerCamelCase__   :						int					   =							0
   lowerCamelCase__   :						Optional[Any]					   =							False

   # Process until all processes are completed
   while complete != no_of_processes:
      for j in range(__lowerCamelCase     ):
         if arrival_time[j] <= increment_time and remaining_time[j] > 0:
            if remaining_time[j] < minm:
               lowerCamelCase__   :						Optional[int]					   =							remaining_time[j]
               lowerCamelCase__   :						Union[str, Any]					   =							j
               lowerCamelCase__   :						Dict					   =							True

      if not check:
         increment_time += 1
         continue
      remaining_time[short] -= 1

      lowerCamelCase__   :						List[str]					   =							remaining_time[short]
      if minm == 0:
         lowerCamelCase__   :						Union[str, Any]					   =							999999999

      if remaining_time[short] == 0:
         complete += 1
         lowerCamelCase__   :						Optional[Any]					   =							False

         # Find finish time of current process
         lowerCamelCase__   :						Tuple					   =							increment_time + 1

         # Calculate waiting time
         lowerCamelCase__   :						Any					   =							finish_time - arrival_time[short]
         lowerCamelCase__   :						str					   =							finar - burst_time[short]

         if waiting_time[short] < 0:
            lowerCamelCase__   :						Dict					   =							0

        # Increment time
      increment_time += 1
   return waiting_time



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     )				->				list[int]:







   """simple docstring"""
   lowerCamelCase__   :						Optional[int]					   =							[0] * no_of_processes
   for i in range(__lowerCamelCase     ):
      lowerCamelCase__   :						List[str]					   =							burst_time[i] + waiting_time[i]
   return turn_around_time



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     )				->				None:







   """simple docstring"""
   lowerCamelCase__   :						Optional[Any]					   =							0
   lowerCamelCase__   :						Dict					   =							0
   for i in range(__lowerCamelCase     ):
      lowerCamelCase__   :						Dict					   =							total_waiting_time + waiting_time[i]
      lowerCamelCase__   :						Tuple					   =							total_turn_around_time + turn_around_time[i]
   print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}"     )
   print('''Average turn around time ='''			,  total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
       print('Enter how many process you want to analyze')
       _A					:    List[Any]   =  int(input())
       _A					:    Optional[int]   =  [0] * no_of_processes
       _A					:    str   =  [0] * no_of_processes
       _A					:    Dict   =  list(range(1, no_of_processes + 1))

       for i in range(no_of_processes):
              print('Enter the arrival time and burst time for process:--' + str(i + 1))
              _A					:    str   =  map(int, input().split())

       _A					:    List[str]   =  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       _A					:    List[Any]   =  burst_time
       _A					:    int   =  no_of_processes
       _A					:    Optional[int]   =  waiting_time
       _A					:    Optional[int]   =  calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       _A					:    str   =  pd.DataFrame(
           list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
           columns=[
               'Process',
               'BurstTime',
               'ArrivalTime',
               'WaitingTime',
               'TurnAroundTime',
           ],
       )

       # Printing the dataFrame
       pd.set_option('display.max_rows', fcfs.shape[0] + 1)
       print(fcfs)









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	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							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		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """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,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """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,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

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

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

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

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

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

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







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







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

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      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(__magic_name__		,      __magic_name__				)

