






'''simple docstring'''

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







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




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				1
            __lowerCamelCase						:       Tuple				   =				3
            __lowerCamelCase						:       List[str]				   =				(32, 32)

            __lowerCamelCase						:       Dict				   =				floats_tensor((batch_size, num_channels) + sizes	,    rng=random.Random(0					)					).to(A__					)
            return image




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       str				   =				UNetaDConditionModel(
                block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=4	,    out_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,    cross_attention_dim=32	,    )
            return model




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				AutoencoderKL(
                block_out_channels=[32, 64]	,    in_channels=3	,    out_channels=3	,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,    latent_channels=4	,    )
            return model




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       str				   =				CLIPTextConfig(
                bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    )
            return CLIPTextModel(A__					)




       @property
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            def extract(*A__      : Union[str, Any]	,    **A__      : List[str]					):







                 class 	SCREAMING_SNAKE_CASE	:




                        def __init__(					self      : List[str]					):


                             """simple docstring"""







                             __lowerCamelCase						:       Optional[Any]				   =				torch.ones([0]					)




                        def 					a_					(					self      : Tuple	,    A__      : str					):


                             """simple docstring"""







                             self.pixel_values.to(A__					)
                             return self

                 return Out()

            return extract




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
            __lowerCamelCase						:       Dict				   =				self.dummy_cond_unet
            __lowerCamelCase						:       Optional[Any]				   =				DDIMScheduler(
                beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule="""scaled_linear"""	,    clip_sample=A__	,    set_alpha_to_one=A__	,    )

            __lowerCamelCase						:       Union[str, Any]				   =				self.dummy_vae
            __lowerCamelCase						:       Dict				   =				self.dummy_text_encoder
            __lowerCamelCase						:       int				   =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

            # make sure here that pndm scheduler skips prk
            __lowerCamelCase						:       Union[str, Any]				   =				StableDiffusionPipeline(
                unet=A__	,    scheduler=A__	,    vae=A__	,    text_encoder=A__	,    tokenizer=A__	,    safety_checker=A__	,    feature_extractor=self.dummy_extractor	,    )
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				"""A painting of a squirrel eating a burger"""

            __lowerCamelCase						:       Optional[int]				   =				torch.Generator(device=A__					).manual_seed(0					)
            __lowerCamelCase						:       Optional[Any]				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""					)
            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Union[str, Any]				   =				torch.Generator(device=A__					).manual_seed(0					)
            __lowerCamelCase						:       Optional[Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""	,    return_dict=A__	,    )[0]

            __lowerCamelCase						:       Union[str, Any]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Any				   =				image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]					)

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




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
            __lowerCamelCase						:       int				   =				self.dummy_cond_unet
            __lowerCamelCase						:       Dict				   =				PNDMScheduler(skip_prk_steps=A__					)
            __lowerCamelCase						:       Tuple				   =				self.dummy_vae
            __lowerCamelCase						:       Dict				   =				self.dummy_text_encoder
            __lowerCamelCase						:       Optional[Any]				   =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

            # make sure here that pndm scheduler skips prk
            __lowerCamelCase						:       Dict				   =				StableDiffusionPipeline(
                unet=A__	,    scheduler=A__	,    vae=A__	,    text_encoder=A__	,    tokenizer=A__	,    safety_checker=A__	,    feature_extractor=self.dummy_extractor	,    )
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       List[str]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Optional[int]				   =				torch.Generator(device=A__					).manual_seed(0					)
            __lowerCamelCase						:       str				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""					)

            __lowerCamelCase						:       str				   =				output.images

            __lowerCamelCase						:       Any				   =				torch.Generator(device=A__					).manual_seed(0					)
            __lowerCamelCase						:       List[Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=6.0	,    num_inference_steps=2	,    output_type="""np"""	,    return_dict=A__	,    )[0]

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Union[str, Any]				   =				image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]					)

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				StableDiffusionPipeline.from_pretrained(
                """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""	,    safety_checker=A__					)
            assert isinstance(A__	,    A__					)
            assert isinstance(pipe.scheduler	,    A__					)
            assert pipe.safety_checker is None

            __lowerCamelCase						:       Any				   =				pipe("""example prompt"""	,    num_inference_steps=2					).images[0]
            assert image is not None

            # check that there's no error when saving a pipeline with one of the models being None
            with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(A__					)
                 __lowerCamelCase						:       Dict				   =				StableDiffusionPipeline.from_pretrained(A__					)

            # sanity check that the pipeline still works
            assert pipe.safety_checker is None
            __lowerCamelCase						:       Tuple				   =				pipe("""example prompt"""	,    num_inference_steps=2					).images[0]
            assert image is not None




       @unittest.skipIf(torch_device != """cuda"""	,    """This test requires a GPU"""					)
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.dummy_cond_unet
            __lowerCamelCase						:       int				   =				PNDMScheduler(skip_prk_steps=A__					)
            __lowerCamelCase						:       Dict				   =				self.dummy_vae
            __lowerCamelCase						:       int				   =				self.dummy_text_encoder
            __lowerCamelCase						:       Any				   =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

            # put models in fp16
            __lowerCamelCase						:       int				   =				unet.half()
            __lowerCamelCase						:       Tuple				   =				vae.half()
            __lowerCamelCase						:       Any				   =				bert.half()

            # make sure here that pndm scheduler skips prk
            __lowerCamelCase						:       Tuple				   =				StableDiffusionPipeline(
                unet=A__	,    scheduler=A__	,    vae=A__	,    text_encoder=A__	,    tokenizer=A__	,    safety_checker=A__	,    feature_extractor=self.dummy_extractor	,    )
            __lowerCamelCase						:       str				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[str]				   =				sd_pipe([prompt]	,    num_inference_steps=2	,    output_type="""np"""					).images

            assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




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


            """simple docstring"""







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




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,    safety_checker=A__					)
            __lowerCamelCase						:       Optional[Any]				   =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       Any				   =				(
                """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
                """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
                """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
                """ children from bahnhof zoo, detailed """
            )
            __lowerCamelCase						:       Optional[int]				   =				4003660346
            __lowerCamelCase						:       Dict				   =				7

            # without safety guidance (sld_guidance_scale = 0)
            __lowerCamelCase						:       Any				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       int				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

            __lowerCamelCase						:       Optional[int]				   =				output.images
            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       int				   =				[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

            assert image.shape == (1, 512, 512, 3)

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

            # without safety guidance (strong configuration)
            __lowerCamelCase						:       int				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       str				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

            __lowerCamelCase						:       List[str]				   =				output.images
            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Tuple				   =				[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

            assert image.shape == (1, 512, 512, 3)

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




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,    safety_checker=A__					)
            __lowerCamelCase						:       Dict				   =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
            __lowerCamelCase						:       Optional[Any]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       Optional[int]				   =				"""padme amidala taking a bath artwork, safe for work, no nudity"""
            __lowerCamelCase						:       Union[str, Any]				   =				2734971755
            __lowerCamelCase						:       int				   =				7

            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       Optional[int]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

            __lowerCamelCase						:       Optional[int]				   =				output.images
            __lowerCamelCase						:       Optional[Any]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Optional[int]				   =				[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

            assert image.shape == (1, 512, 512, 3)

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

            __lowerCamelCase						:       Dict				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       int				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

            __lowerCamelCase						:       str				   =				output.images
            __lowerCamelCase						:       List[str]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Any				   =				[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

            assert image.shape == (1, 512, 512, 3)

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




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
            __lowerCamelCase						:       str				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            __lowerCamelCase						:       List[Any]				   =				(
                """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
                """ leyendecker"""
            )
            __lowerCamelCase						:       Union[str, Any]				   =				1044355234
            __lowerCamelCase						:       Union[str, Any]				   =				12

            __lowerCamelCase						:       Any				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       str				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=0	,    )

            __lowerCamelCase						:       List[Any]				   =				output.images
            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Union[str, Any]				   =				np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]					)

            assert image.shape == (1, 512, 512, 3)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-7

            __lowerCamelCase						:       Any				   =				torch.manual_seed(A__					)
            __lowerCamelCase						:       int				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=A__	,    num_inference_steps=50	,    output_type="""np"""	,    width=512	,    height=512	,    sld_guidance_scale=2000	,    sld_warmup_steps=7	,    sld_threshold=0.025	,    sld_momentum_scale=0.5	,    sld_mom_beta=0.7	,    )

            __lowerCamelCase						:       Dict				   =				output.images
            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       str				   =				np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]					)
            assert image.shape == (1, 512, 512, 3)

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











'''simple docstring'''

from statistics import mean

import numpy as np
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				0
     # Number of processes finished
     __lowerCamelCase						:       List[Any]				   =				0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __lowerCamelCase						:       int				   =				[0] * no_of_process
     # List to include calculation results
     __lowerCamelCase						:       Optional[int]				   =				[0] * no_of_process

     # Sort by arrival time.
     __lowerCamelCase						:       int				   =				[burst_time[i] for i in np.argsort(_lowercase     )]
     __lowerCamelCase						:       Union[str, Any]				   =				[process_name[i] for i in np.argsort(_lowercase     )]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __lowerCamelCase						:       Tuple				   =				0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __lowerCamelCase						:       List[str]				   =				arrival_time[i]

          __lowerCamelCase						:       Dict				   =				0
          # Index showing the location of the process being performed
          __lowerCamelCase						:       Optional[Any]				   =				0
          # Saves the current response ratio.
          __lowerCamelCase						:       List[str]				   =				0
          for i in range(0,						_lowercase     ):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    __lowerCamelCase						:       Optional[int]				   =				(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    __lowerCamelCase						:       List[Any]				   =				temp
                    __lowerCamelCase						:       str				   =				i

        # Calculate the turn around time
          __lowerCamelCase						:       Optional[Any]				   =				current_time + burst_time[loc] - arrival_time[loc]
          current_time += burst_time[loc]
          # Indicates that the process has been performed.
          __lowerCamelCase						:       Optional[Any]				   =				1
          # Increase finished_process_count by 1
          finished_process_count += 1

     return turn_around_time
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				[0] * no_of_process
     for i in range(0,						_lowercase     ):
          __lowerCamelCase						:       Tuple				   =				turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
   UpperCAmelCase__						:List[Any]          =			5
   UpperCAmelCase__						:Tuple          =			["""A""", """B""", """C""", """D""", """E"""]
   UpperCAmelCase__						:Tuple          =			[1, 2, 3, 4, 5]
   UpperCAmelCase__						:List[Any]          =			[1, 2, 3, 4, 5]

   UpperCAmelCase__						:Union[str, Any]          =			calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   UpperCAmelCase__						:Optional[int]          =			calculate_waiting_time(
       process_name, turn_around_time, burst_time, no_of_process
   )

   print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
   for i in range(0, no_of_process):
      print(
          f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
          f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
      )

   print(f'''average waiting time : {mean(waiting_time):.5f}''')
   print(f'''average turn around time : {mean(turn_around_time):.5f}''')



