






"""simple docstring"""

from __future__ import annotations

import pandas as pd






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






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

						__magic_name__            =     0
						__magic_name__            =     0
						__magic_name__            =     9_9_9_9_9_9_9_9_9
						__magic_name__            =     0
						__magic_name__            =     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:
																														__magic_name__            =     remaining_time[j]
																														__magic_name__            =     j
																														__magic_name__            =     True

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

												__magic_name__            =     remaining_time[short]
												if minm == 0:
																		__magic_name__            =     9_9_9_9_9_9_9_9_9

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

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

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

																		if waiting_time[short] < 0:
																								__magic_name__            =     0

        # Increment time
												increment_time += 1
						return waiting_time






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int ,						__lowerCamelCase:list[int]   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     0
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												__magic_name__            =     total_waiting_time + waiting_time[i]
												__magic_name__            =     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''')
			lowercase    			=       int(input())
			lowercase    			=       [0] * no_of_processes
			lowercase    			=       [0] * no_of_processes
			lowercase    			=       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))
						lowercase			,      lowercase    			=       map(int, input().split())

			lowercase    			=       calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

			calculate_average_times(waiting_time, turn_around_time, no_of_processes)

			lowercase    			=       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"""

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										if images is None and text is None:
																raise ValueError("You have to specify either images or text."	)

										# Get only text
										if images is None:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

										if text_encoding is not None:
																encoding_image_processor.update(__lowerCamelCase	)

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



