"""simple docstring"""





from __future__ import annotations

import pandas as pd





def        a_  (   _lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			[0] * no_of_processes
       lowercase__    :	str     					=			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(_lowerCAmelCase  ):
              lowercase__    :	Optional[int]     					=			burst_time[i]

       lowercase__    :	Any     					=			0
       lowercase__    :	int     					=			0
       lowercase__    :	Any     					=			9_9999_9999
       lowercase__    :	Optional[Any]     					=			0
       lowercase__    :	Tuple     					=			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:
                                   lowercase__    :	Dict     					=			remaining_time[j]
                                   lowercase__    :	List[Any]     					=			j
                                   lowercase__    :	Any     					=			True

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

              lowercase__    :	Optional[Any]     					=			remaining_time[short]
              if minm == 0:
                     lowercase__    :	Dict     					=			9_9999_9999

              if remaining_time[short] == 0:
                     complete += 1
                     lowercase__    :	Union[str, Any]     					=			False

                     # Find finish time of current process
                     lowercase__    :	int     					=			increment_time + 1

                     # Calculate waiting time
                     lowercase__    :	Dict     					=			finish_time - arrival_time[short]
                     lowercase__    :	str     					=			finar - burst_time[short]

                     if waiting_time[short] < 0:
                            lowercase__    :	Tuple     					=			0

        # Increment time
              increment_time += 1
       return waiting_time





def        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Any  ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			[0] * no_of_processes
       for i in range(_lowerCAmelCase  ):
              lowercase__    :	Union[str, Any]     					=			burst_time[i] + waiting_time[i]
       return turn_around_time





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			0
       lowercase__    :	Optional[Any]     					=			0
       for i in range(_lowerCAmelCase  ):
              lowercase__    :	Optional[Any]     					=			total_waiting_time + waiting_time[i]
              lowercase__    :	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")
       _UpperCamelCase     :		Tuple											=					int(input())
       _UpperCamelCase     :		Tuple											=					[0] * no_of_processes
       _UpperCamelCase     :		Optional[int]											=					[0] * no_of_processes
       _UpperCamelCase     :		int											=					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))
              _UpperCamelCase     :		Optional[int]											=					map(int, input().split())

       _UpperCamelCase     :		str											=					calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       _UpperCamelCase     :		Any											=					burst_time
       _UpperCamelCase     :		Dict											=					no_of_processes
       _UpperCamelCase     :		Tuple											=					waiting_time
       _UpperCamelCase     :		str											=					calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       _UpperCamelCase     :		Dict											=					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)







"""simple docstring"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

