



'''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 re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
				from PIL import Image

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		"naver-clova-ix/donut-base-finetuned-docvqa"
						UpperCAmelCase							:   int									=		(
						    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
						    "should be the document containing the information, as well as a `question` that is the question about the "
						    "document. It returns a text that contains the answer to the question."
						)
						UpperCAmelCase							:   Union[str, Any]									=		"document_qa"
						UpperCAmelCase							:   List[Any]									=		AutoProcessor
						UpperCAmelCase							:   Any									=		VisionEncoderDecoderModel

						UpperCAmelCase							:   Optional[int]									=		["image", "text"]
						UpperCAmelCase							:   str									=		["text"]


						def __init__( self     ,	*A_     ,	**A_				)     ->     Any:
							if not is_vision_available():
								raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""				)

							super().__init__(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
							lowerCAmelCase   =     task_prompt.replace("""{user_input}"""     ,	A_				)
							lowerCAmelCase   =     self.pre_processor.tokenizer(
							    A_     ,	add_special_tokens=A_     ,	return_tensors="""pt"""				).input_ids
							lowerCAmelCase   =     self.pre_processor(A_     ,	return_tensors="""pt"""				).pixel_values

							return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}


						def     __snake_case   ( self     ,	A_				)     ->     Optional[int]:
							return self.model.generate(
							    inputs["""pixel_values"""].to(self.device				)     ,	decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device				)     ,	max_length=self.model.decoder.config.max_position_embeddings     ,	early_stopping=A_     ,	pad_token_id=self.pre_processor.tokenizer.pad_token_id     ,	eos_token_id=self.pre_processor.tokenizer.eos_token_id     ,	use_cache=A_     ,	num_beams=1     ,	bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]     ,	return_dict_in_generate=A_     ,	).sequences




						def     __snake_case   ( self     ,	A_				)     ->     Any:
							lowerCAmelCase   =     self.pre_processor.batch_decode(A_				)[0]
							lowerCAmelCase   =     sequence.replace(self.pre_processor.tokenizer.eos_token     ,	""""""				)
							lowerCAmelCase   =     sequence.replace(self.pre_processor.tokenizer.pad_token     ,	""""""				)
							lowerCAmelCase   =     re.sub(r"""<.*?>"""     ,	""""""     ,	A_     ,	count=1				).strip()  # remove first task start token
							lowerCAmelCase   =     self.pre_processor.tokenajson(A_				)

							return sequence["answer"]




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

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

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

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

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

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

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

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     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     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   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		,       UpperCAmelCase     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   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)