





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Any=13							,	__lowerCAmelCase   :    str=7							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    Tuple=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=99							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Union[str, Any]=4							,	__lowerCAmelCase   :    Optional[int]=37							,	__lowerCAmelCase   :    Tuple="gelu"							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Tuple=5_12							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Optional[int]=0.0_2							,	__lowerCAmelCase   :    Optional[int]=False							,	__lowerCAmelCase   :    Tuple=True							,	__lowerCAmelCase   :    Dict="None"							,	__lowerCAmelCase   :    Any=3							,	__lowerCAmelCase   :    Optional[int]=4							,	__lowerCAmelCase   :    Union[str, Any]=None							,	)	->	Optional[int]:







					"""simple docstring"""






					A__        =   parent
					A__        =   batch_size
					A__        =   seq_length
					A__        =   is_training
					A__        =   use_input_mask
					A__        =   use_token_type_ids
					A__        =   use_labels
					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   intermediate_size
					A__        =   hidden_act
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   type_sequence_label_size
					A__        =   initializer_range
					A__        =   num_labels
					A__        =   num_choices
					A__        =   relative_attention
					A__        =   position_biased_input
					A__        =   pos_att_type
					A__        =   scope






			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					A__        =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size   )

					A__        =   None
					if self.use_input_mask:
							A__        =   random_attention_mask([self.batch_size, self.seq_length]   )

					A__        =   None
					if self.use_token_type_ids:
							A__        =   ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size   )

					A__        =   None
					A__        =   None
					A__        =   None
					if self.use_labels:
							A__        =   ids_tensor([self.batch_size]							,	self.type_sequence_label_size   )
							A__        =   ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels   )

					A__        =   DebertaVaConfig(
					    vocab_size=self.vocab_size							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	max_position_embeddings=self.max_position_embeddings							,	type_vocab_size=self.type_vocab_size							,	relative_attention=self.relative_attention							,	position_biased_input=self.position_biased_input							,	initializer_range=self.initializer_range							,	return_dict=__lowerCAmelCase							,	)

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Optional[Any]   )	->	Tuple:







					"""simple docstring"""






					A__        =   TFDebertaVaModel(config=__lowerCAmelCase   )
					A__        =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

					A__        =   [input_ids, input_mask]
					A__        =   model(__lowerCAmelCase   )

					A__        =   model(__lowerCAmelCase   )

					self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)   )






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    List[str]   )	->	Tuple:







					"""simple docstring"""






					A__        =   TFDebertaVaForMaskedLM(config=__lowerCAmelCase   )
					A__        =   {
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}
					A__        =   model(__lowerCAmelCase   )
					self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size)   )






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Dict   )	->	Dict:







					"""simple docstring"""






					A__        =   self.num_labels
					A__        =   TFDebertaVaForSequenceClassification(config=__lowerCAmelCase   )
					A__        =   {
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}

					A__        =   model(__lowerCAmelCase   )
					self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)   )






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Dict   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.num_labels
					A__        =   TFDebertaVaForTokenClassification(config=__lowerCAmelCase   )
					A__        =   {
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}
					A__        =   model(__lowerCAmelCase   )
					self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.num_labels)   )






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    int   )	->	int:







					"""simple docstring"""






					A__        =   TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase   )
					A__        =   {
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}

					A__        =   model(__lowerCAmelCase   )
					self.parent.assertEqual(result.start_logits.shape							,	(self.batch_size, self.seq_length)   )
					self.parent.assertEqual(result.end_logits.shape							,	(self.batch_size, self.seq_length)   )







			def    a_       (			self   :    str   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.prepare_config_and_inputs()
					(
					    (
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			(
					    A__
					)      ,			
					)        =   config_and_inputs
					A__        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
					return config, inputs_dict


@require_tf
class   A			(SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    (
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			__lowerCamelCase	:       Any							=    (
			    {
			        '''feature-extraction''': TFDebertaVaModel,
			        '''fill-mask''': TFDebertaVaForMaskedLM,
			        '''question-answering''': TFDebertaVaForQuestionAnswering,
			        '''text-classification''': TFDebertaVaForSequenceClassification,
			        '''token-classification''': TFDebertaVaForTokenClassification,
			        '''zero-shot''': TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			__lowerCamelCase	:       Tuple							=    False
			__lowerCamelCase	:       List[Any]							=    False






			def    a_       (			self   :    List[str]   )	->	int:







					"""simple docstring"""






					A__        =   TFDebertaVaModelTester(self   )
					A__        =   ConfigTester(self							,	config_class=__lowerCAmelCase							,	hidden_size=37   )






			def    a_       (			self   :    Any   )	->	Dict:







					"""simple docstring"""






					self.config_tester.run_common_tests()






			def    a_       (			self   :    Tuple   )	->	Dict:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase   )






			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase   )






			def    a_       (			self   :    Tuple   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase   )






			def    a_       (			self   :    List[Any]   )	->	List[Any]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase   )






			def    a_       (			self   :    List[str]   )	->	List[str]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase   )







			@slow
			def    a_       (			self   :    List[Any]   )	->	str:







					"""simple docstring"""






					A__        =   TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""   )
					self.assertIsNotNone(__lowerCAmelCase   )


@require_tf
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@unittest.skip(reason="""Model not available yet"""   )
			def    a_       (			self   :    Any   )	->	Optional[int]:







					"""simple docstring"""






					pass







			@slow
			def    a_       (			self   :    Optional[Any]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""   )
					A__        =   tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
					A__        =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
					A__        =   model(__lowerCAmelCase							,	attention_mask=__lowerCAmelCase   )[0]

					A__        =   tf.constant(
					    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]   )
					tf.debugging.assert_near(output[:, 1:4, 1:4]							,	__lowerCAmelCase							,	atol=1e-4   )










from collections import deque


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					A__        =   process_name  # process name
					A__        =   arrival_time  # arrival time of the process
					# completion time of finished process or last interrupted time
					A__        =   arrival_time
					A__        =   burst_time  # remaining burst time
					A__        =   0  # total time of the process wait in ready queue
					A__        =   0  # time from arrival time to completion time


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    list[int]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					A__        =   number_of_queues
					# time slice of queues that round robin algorithm applied
					A__        =   time_slices
					# unfinished process is in this ready_queue
					A__        =   queue
					# current time
					A__        =   current_time
					# finished process is in this sequence queue
					A__        =   deque()






			def    a_       (			self   :    Dict   )	->	list[str]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(self.finish_queue   )   ):
							sequence.append(self.finish_queue[i].process_name   )
					return sequence






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							waiting_times.append(queue[i].waiting_time   )
					return waiting_times






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							turnaround_times.append(queue[i].turnaround_time   )
					return turnaround_times






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							completion_times.append(queue[i].stop_time   )
					return completion_times






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    deque[Process]   )	->	list[int]:







					"""simple docstring"""






					return [q.burst_time for q in queue]






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Process   )	->	int:







					"""simple docstring"""






					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    deque[Process]   )	->	deque[Process]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of finished process
					while len(__lowerCAmelCase   ) != 0:
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of current process
							self.update_waiting_time(__lowerCAmelCase   )
							# update current time
							self.current_time += cp.burst_time
							# finish the process and set the process's burst-time 0
							A__        =   0
							# set the process's turnaround time because it is finished
							A__        =   self.current_time - cp.arrival_time
							# set the completion time
							A__        =   self.current_time
							# add the process to queue that has finished queue
							finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# FCFS will finish all remaining processes
					return finished






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int   )	->	tuple[deque[Process], deque[Process]]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(__lowerCAmelCase   )   ):
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of unfinished processes
							self.update_waiting_time(__lowerCAmelCase   )
							# if the burst time of process is bigger than time-slice
							if cp.burst_time > time_slice:
									# use CPU for only time-slice
									self.current_time += time_slice
									# update remaining burst time
									cp.burst_time -= time_slice
									# update end point time
									A__        =   self.current_time
									# locate the process behind the queue because it is not finished
									ready_queue.append(__lowerCAmelCase   )
							else:
									# use CPU for remaining burst time
									self.current_time += cp.burst_time
									# set burst time 0 because the process is finished
									A__        =   0
									# set the finish time
									A__        =   self.current_time
									# update the process' turnaround time because it is finished
									A__        =   self.current_time - cp.arrival_time
									# add the process to queue that has finished queue
									finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# return finished processes queue and remaining processes queue
					return finished, ready_queue







			def    a_       (			self   :    List[Any]   )	->	deque[Process]:







					"""simple docstring"""






					for i in range(self.number_of_queues - 1   ):
							A__      ,			A__        =   self.round_robin(
							    self.ready_queue							,	self.time_slices[i]   )
					#  the last queue has first_come_first_served algorithm
					self.first_come_first_served(self.ready_queue   )

					return self.finish_queue


if __name__ == "__main__":
					import doctest

					A    :					Union[str, Any] =      Process('''P1''', 0, 5_3)
					A    :					Optional[Any] =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					int =      Process('''P4''', 0, 2_4)
					A    :					Any =      3
					A    :					List[Any] =      [1_7, 2_5]
					A    :					Optional[Any] =      deque([Pa, Pa, Pa, Pa])

					if len(time_slices) != number_of_queues - 1:
										raise SystemExit(0)

					doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

					A    :					Optional[Any] =      Process('''P1''', 0, 5_3)
					A    :					int =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					Tuple =      Process('''P4''', 0, 2_4)
					A    :					Union[str, Any] =      3
					A    :					Optional[Any] =      [1_7, 2_5]
					A    :					Tuple =      deque([Pa, Pa, Pa, Pa])
					A    :					Optional[int] =      MLFQ(number_of_queues, time_slices, queue, 0)
					A    :					Dict =      mlfq.multi_level_feedback_queue()

					# print total waiting times of processes(P1, P2, P3, P4)
					print(
					    F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print completion times of processes(P1, P2, P3, P4)
					print(
					    F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print total turnaround times of processes(P1, P2, P3, P4)
					print(
					    F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print sequence of finished processes
					print(
					    F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
					)



