






"""simple docstring"""
from __future__ import annotations

import pandas as pd

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[Any]     	=   [0] * no_of_processes
			UpperCAmelCase_	:					Optional[Any]     	=   [0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__		):
						UpperCAmelCase_	:					Optional[Any]     	=   burst_time[i]

			UpperCAmelCase_	:					Tuple     	=   0
			UpperCAmelCase_	:					Optional[int]     	=   0
			UpperCAmelCase_	:					List[Any]     	=   9_99_99_99_99
			UpperCAmelCase_	:					str     	=   0
			UpperCAmelCase_	:					List[str]     	=   False

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

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

						UpperCAmelCase_	:					Union[str, Any]     	=   remaining_time[short]
						if minm == 0:
									UpperCAmelCase_	:					List[str]     	=   9_99_99_99_99

						if remaining_time[short] == 0:
									complete += 1
									UpperCAmelCase_	:					Optional[Any]     	=   False

									# Find finish time of current process
									UpperCAmelCase_	:					Union[str, Any]     	=   increment_time + 1

									# Calculate waiting time
									UpperCAmelCase_	:					List[str]     	=   finish_time - arrival_time[short]
									UpperCAmelCase_	:					Tuple     	=   finar - burst_time[short]

									if waiting_time[short] < 0:
												UpperCAmelCase_	:					Any     	=   0

        # Increment time
						increment_time += 1
			return waiting_time

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					str     	=   [0] * no_of_processes
			for i in range(A__		):
						UpperCAmelCase_	:					List[Any]     	=   burst_time[i] + waiting_time[i]
			return turn_around_time

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Any     	=   0
			UpperCAmelCase_	:					Optional[Any]     	=   0
			for i in range(A__		):
						UpperCAmelCase_	:					Dict     	=   total_waiting_time + waiting_time[i]
						UpperCAmelCase_	:					Optional[Any]     	=   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)






"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


