

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"data2vec-vision"


						def __init__(       self						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-12						,					A_=224						,					A_=16						,					A_=3						,					A_=False						,					A_=False						,					A_=False						,					A_=False						,					A_=0.1						,					A_=0.1						,					A_=True						,					A_=[3, 5, 7, 11]						,					A_=[1, 2, 3, 6]						,					A_=True						,					A_=0.4						,					A_=256						,					A_=1						,					A_=False						,					A_=255						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**_SCREAMING_SNAKE_CASE	)

										_UpperCAmelCase       :		Tuple 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Any 		=    num_attention_heads
										_UpperCAmelCase       :		Dict 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Any 		=    layer_norm_eps

										_UpperCAmelCase       :		Optional[Any] 		=    image_size
										_UpperCAmelCase       :		int 		=    patch_size
										_UpperCAmelCase       :		List[Any] 		=    num_channels
										_UpperCAmelCase       :		str 		=    use_mask_token
										_UpperCAmelCase       :		List[Any] 		=    use_absolute_position_embeddings
										_UpperCAmelCase       :		List[Any] 		=    use_relative_position_bias
										_UpperCAmelCase       :		List[Any] 		=    use_shared_relative_position_bias
										_UpperCAmelCase       :		Dict 		=    layer_scale_init_value
										_UpperCAmelCase       :		Tuple 		=    drop_path_rate
										_UpperCAmelCase       :		Union[str, Any] 		=    use_mean_pooling
										# decode head attributes (semantic segmentation)
										_UpperCAmelCase       :		int 		=    out_indices
										_UpperCAmelCase       :		List[Any] 		=    pool_scales
										# auxiliary head attributes (semantic segmentation)
										_UpperCAmelCase       :		Tuple 		=    use_auxiliary_head
										_UpperCAmelCase       :		Tuple 		=    auxiliary_loss_weight
										_UpperCAmelCase       :		List[str] 		=    auxiliary_channels
										_UpperCAmelCase       :		int 		=    auxiliary_num_convs
										_UpperCAmelCase       :		Tuple 		=    auxiliary_concat_input
										_UpperCAmelCase       :		str 		=    semantic_loss_ignore_index







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.11"				)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]	)





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-4





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		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:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

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

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

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

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

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

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    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')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      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))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      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)


