"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
				from PIL import Image
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						List[Any]													=		(
						    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
						    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
						    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
						)
						lowerCAmelCase__  :						List[str]													=		'CIDAS/clipseg-rd64-refined'
						lowerCAmelCase__  :						Optional[Any]													=		'image_segmenter'
						lowerCAmelCase__  :						List[str]													=		CLIPSegForImageSegmentation

						lowerCAmelCase__  :						Union[str, Any]													=		['image', 'text']
						lowerCAmelCase__  :						Optional[Any]													=		['image']





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				str:
													requires_backends(self		,['vision']     )
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
													return self.pre_processor(text=[label]		,images=[image]		,padding=__UpperCAmelCase		,return_tensors='pt'     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Union[str, Any]:
													with torch.no_grad():
																				A__	     =							self.model(**__UpperCAmelCase     ).logits
													return logits





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													A__	     =							outputs.cpu().detach().numpy()
													A__	     =							0
													A__	     =							1
													return Image.fromarray((array * 2_55).astype(np.uinta     )     )






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[0] * no_of_processes
							A__	     =							[0] * no_of_processes
							# Copy the burst time into remaining_time[]
							for i in range(UpperCamelCase__				):
														A__	     =							burst_time[i]

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							False

							# Process until all processes are completed
							while complete != no_of_processes:
														for j in range(UpperCamelCase__				):
																					if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																												if remaining_time[j] < minm:
																																			A__	     =							remaining_time[j]
																																			A__	     =							j
																																			A__	     =							True

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

														A__	     =							remaining_time[short]
														if minm == 0:
																					A__	     =							999_999_999

														if remaining_time[short] == 0:
																					complete += 1
																					A__	     =							False

																					# Find finish time of current process
																					A__	     =							increment_time + 1

																					# Calculate waiting time
																					A__	     =							finish_time - arrival_time[short]
																					A__	     =							finar - burst_time[short]

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[0] * no_of_processes
							for i in range(UpperCamelCase__				):
														A__	     =							burst_time[i] + waiting_time[i]
							return turn_around_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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")
				__lowerCamelCase											=  int(input())
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  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))
								__lowerCamelCase				,   __lowerCamelCase											=  map(int, input().split())

				__lowerCamelCase											=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				__lowerCamelCase											=  burst_time
				__lowerCamelCase											=  no_of_processes
				__lowerCamelCase											=  waiting_time
				__lowerCamelCase											=  calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				__lowerCamelCase											=  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)





