






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel

@require_tf
class _lowercase     :
							a    				=	BlenderbotConfig
							a    				=	{}
							a    				=	"""gelu"""




							def __init__(							self:    List[Any]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Union[str, Any]=13      ,							UpperCamelCase__:    List[Any]=7      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    int=False      ,							UpperCamelCase__:    Dict=99      ,							UpperCamelCase__:    Union[str, Any]=32      ,							UpperCamelCase__:    Dict=2      ,							UpperCamelCase__:    Union[str, Any]=4      ,							UpperCamelCase__:    int=37      ,							UpperCamelCase__:    Dict=0.1      ,							UpperCamelCase__:    Optional[Any]=0.1      ,							UpperCamelCase__:    Any=20      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Union[str, Any]=1      ,							UpperCamelCase__:    int=0      ,							):
														lowerCamelCase__ :     Any	  =	parent
														lowerCamelCase__ :     Optional[int]	  =	batch_size
														lowerCamelCase__ :     Union[str, Any]	  =	seq_length
														lowerCamelCase__ :     Union[str, Any]	  =	is_training
														lowerCamelCase__ :     List[str]	  =	use_labels
														lowerCamelCase__ :     Union[str, Any]	  =	vocab_size
														lowerCamelCase__ :     List[Any]	  =	hidden_size
														lowerCamelCase__ :     int	  =	num_hidden_layers
														lowerCamelCase__ :     int	  =	num_attention_heads
														lowerCamelCase__ :     int	  =	intermediate_size

														lowerCamelCase__ :     str	  =	hidden_dropout_prob
														lowerCamelCase__ :     List[Any]	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     List[Any]	  =	max_position_embeddings
														lowerCamelCase__ :     int	  =	eos_token_id
														lowerCamelCase__ :     Optional[int]	  =	pad_token_id
														lowerCamelCase__ :     Optional[int]	  =	bos_token_id




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     str	  =	ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size					)
														lowerCamelCase__ :     List[str]	  =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)      ,							1					)
														lowerCamelCase__ :     int	  =	tf.concat([input_ids, eos_tensor]      ,							axis=1					)

														lowerCamelCase__ :     Optional[Any]	  =	ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size					)

														lowerCamelCase__ :     Optional[int]	  =	self.config_cls(
														    vocab_size=self.vocab_size      ,							d_model=self.hidden_size      ,							encoder_layers=self.num_hidden_layers      ,							decoder_layers=self.num_hidden_layers      ,							encoder_attention_heads=self.num_attention_heads      ,							decoder_attention_heads=self.num_attention_heads      ,							encoder_ffn_dim=self.intermediate_size      ,							decoder_ffn_dim=self.intermediate_size      ,							dropout=self.hidden_dropout_prob      ,							attention_dropout=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							eos_token_ids=[2]      ,							bos_token_id=self.bos_token_id      ,							pad_token_id=self.pad_token_id      ,							decoder_start_token_id=self.pad_token_id      ,							**self.config_updates      ,							)
														lowerCamelCase__ :     Tuple	  =	prepare_blenderbot_inputs_dict(lowercase_      ,							lowercase_      ,							lowercase_					)
														return config, inputs_dict




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     Optional[Any]	  =	TFBlenderbotModel(config=lowercase_					).get_decoder()
														lowerCamelCase__ :     Optional[Any]	  =	inputs_dict["input_ids"]

														lowerCamelCase__ :     int	  =	input_ids[:1, :]
														lowerCamelCase__ :     Optional[Any]	  =	inputs_dict["attention_mask"][:1, :]
														lowerCamelCase__ :     Optional[Any]	  =	inputs_dict["head_mask"]
														lowerCamelCase__ :     Optional[int]	  =	1

														# first forward pass
														lowerCamelCase__ :     List[str]	  =	model(lowercase_      ,							attention_mask=lowercase_      ,							head_mask=lowercase_      ,							use_cache=lowercase_					)

														lowerCamelCase__ :     Optional[Any]	  =	outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														lowerCamelCase__ :     List[str]	  =	ids_tensor((self.batch_size, 3)      ,							config.vocab_size					)
														lowerCamelCase__ :     int	  =	tf.cast(ids_tensor((self.batch_size, 3)      ,							2					)      ,							tf.inta					)

														# append to next input_ids and
														lowerCamelCase__ :     Optional[Any]	  =	tf.concat([input_ids, next_tokens]      ,							axis=-1					)
														lowerCamelCase__ :     Optional[int]	  =	tf.concat([attention_mask, next_attn_mask]      ,							axis=-1					)

														lowerCamelCase__ :     Dict	  =	model(lowercase_      ,							attention_mask=lowercase_					)[0]
														lowerCamelCase__ :     Optional[int]	  =	model(lowercase_      ,							attention_mask=lowercase_      ,							past_key_values=lowercase_					)[0]

														self.parent.assertEqual(next_tokens.shape[1]      ,							output_from_past.shape[1]					)

														# select random slice
														lowerCamelCase__ :     int	  =	int(ids_tensor((1,)      ,							output_from_past.shape[-1]					)					)
														lowerCamelCase__ :     Tuple	  =	output_from_no_past[:, -3:, random_slice_idx]
														lowerCamelCase__ :     Any	  =	output_from_past[:, :, random_slice_idx]

														# test that outputs are equal for slice
														tf.debugging.assert_near(lowercase_      ,							lowercase_      ,							rtol=1e-3					)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None    ,      UpperCamelCase=None    ,      UpperCamelCase=None    ,      UpperCamelCase=None    ,      UpperCamelCase=None    ,      ):
							if attention_mask is None:
														lowerCamelCase__ :     int	  =	tf.cast(tf.math.not_equal(__lowerCamelCase    ,      config.pad_token_id				)    ,      tf.inta				)
							if decoder_attention_mask is None:
														lowerCamelCase__ :     int	  =	tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape    ,      dtype=tf.inta				),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,      config.pad_token_id				)    ,      tf.inta				),
														    ]    ,      axis=-1    ,      )
							if head_mask is None:
														lowerCamelCase__ :     Optional[int]	  =	tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														lowerCamelCase__ :     Optional[int]	  =	tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														lowerCamelCase__ :     Union[str, Any]	  =	tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class _lowercase     (    lowercase__    ,				lowercase__    ,				unittest.TestCase		):
							a    				=	(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							a    				=	(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							a    				=	(
							    {
							        """conversational""": TFBlenderbotForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotModel,
							        """summarization""": TFBlenderbotForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotForConditionalGeneration,
							        """translation""": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							a    				=	True
							a    				=	False
							a    				=	False




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Dict	  =	TFBlenderbotModelTester(self					)
														lowerCamelCase__ :     str	  =	ConfigTester(self      ,							config_class=lowercase_					)




							def 		lowerCamelCase_    (							self:    Any					):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)



@require_tokenizers
@require_tf
class _lowercase     (    unittest.TestCase		):
							a    				=	["""My friends are cool but they eat too many carbs."""]
							a    				=	"""facebook/blenderbot-400M-distill"""




							@cached_property
							def 		lowerCamelCase_    (							self:    str					):
														return BlenderbotTokenizer.from_pretrained(self.model_name					)




							@cached_property
							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
														return model




							@slow
							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Any	  =	self.tokenizer(self.src_text      ,							return_tensors="""tf"""					)
														lowerCamelCase__ :     Union[str, Any]	  =	self.model.generate(
														    model_inputs.input_ids      ,							)
														lowerCamelCase__ :     Dict	  =	self.tokenizer.batch_decode(generated_ids.numpy()      ,							skip_special_tokens=lowercase_					)[0]
														assert (
														    generated_words
														    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
														)









'''simple docstring'''







from collections import deque

class _lowercase     :




							def __init__(							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     int	  =	process_name  # process name
														lowerCamelCase__ :     int	  =	arrival_time  # arrival time of the process
														# completion time of finished process or last interrupted time
														lowerCamelCase__ :     List[str]	  =	arrival_time
														lowerCamelCase__ :     Tuple	  =	burst_time  # remaining burst time
														lowerCamelCase__ :     str	  =	0  # total time of the process wait in ready queue
														lowerCamelCase__ :     Optional[Any]	  =	0  # time from arrival time to completion time



class _lowercase     :




							def __init__(							self:    Any      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    list[int]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int      ,							):
														# total number of mlfq's queues
														lowerCamelCase__ :     Tuple	  =	number_of_queues
														# time slice of queues that round robin algorithm applied
														lowerCamelCase__ :     List[Any]	  =	time_slices
														# unfinished process is in this ready_queue
														lowerCamelCase__ :     int	  =	queue
														# current time
														lowerCamelCase__ :     Optional[int]	  =	current_time
														# finished process is in this sequence queue
														lowerCamelCase__ :     deque[Process]	  =	deque()




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	[]
														for i in range(len(self.finish_queue					)					):
																					sequence.append(self.finish_queue[i].process_name					)
														return sequence




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     int	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					waiting_times.append(queue[i].waiting_time					)
														return waiting_times




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     Optional[int]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					turnaround_times.append(queue[i].turnaround_time					)
														return turnaround_times




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     List[Any]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					completion_times.append(queue[i].stop_time					)
														return completion_times




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    deque[Process]					):
														return [q.burst_time for q in queue]




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Process					):
														process.waiting_time += self.current_time - process.stop_time
														return process.waiting_time




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    deque[Process]					):
														lowerCamelCase__ :     deque[Process]	  =	deque()  # sequence deque of finished process
														while len(UpperCamelCase__					) != 0:
																					lowerCamelCase__ :     List[Any]	  =	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(UpperCamelCase__					)
																					# update current time
																					self.current_time += cp.burst_time
																					# finish the process and set the process's burst-time 0
																					lowerCamelCase__ :     Dict	  =	0
																					# set the process's turnaround time because it is finished
																					lowerCamelCase__ :     List[str]	  =	self.current_time - cp.arrival_time
																					# set the completion time
																					lowerCamelCase__ :     int	  =	self.current_time
																					# add the process to queue that has finished queue
																					finished.append(UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     deque[Process]	  =	deque()  # sequence deque of terminated process
														# just for 1 cycle and unfinished processes will go back to queue
														for _ in range(len(UpperCamelCase__					)					):
																					lowerCamelCase__ :     Optional[int]	  =	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(UpperCamelCase__					)
																					# 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
																												lowerCamelCase__ :     Dict	  =	self.current_time
																												# locate the process behind the queue because it is not finished
																												ready_queue.append(UpperCamelCase__					)
																					else:
																												# use CPU for remaining burst time
																												self.current_time += cp.burst_time
																												# set burst time 0 because the process is finished
																												lowerCamelCase__ :     Any	  =	0
																												# set the finish time
																												lowerCamelCase__ :     List[Any]	  =	self.current_time
																												# update the process' turnaround time because it is finished
																												lowerCamelCase__ :     Any	  =	self.current_time - cp.arrival_time
																												# add the process to queue that has finished queue
																												finished.append(UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Tuple					):

														#  all queues except last one have round_robin algorithm
														for i in range(self.number_of_queues - 1					):
																					lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	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     :      Optional[Any]   					=Process('''P1''', 0, 53)
			_A     :      List[Any]   					=Process('''P2''', 0, 17)
			_A     :      Any   					=Process('''P3''', 0, 68)
			_A     :      Tuple   					=Process('''P4''', 0, 24)
			_A     :      int   					=3
			_A     :      Tuple   					=[17, 25]
			_A     :      List[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     :      Dict   					=Process('''P1''', 0, 53)
			_A     :      Union[str, Any]   					=Process('''P2''', 0, 17)
			_A     :      int   					=Process('''P3''', 0, 68)
			_A     :      Dict   					=Process('''P4''', 0, 24)
			_A     :      List[str]   					=3
			_A     :      List[Any]   					=[17, 25]
			_A     :      Any   					=deque([Pa, Pa, Pa, Pa])
			_A     :      List[str]   					=MLFQ(number_of_queues, time_slices, queue, 0)
			_A     :      Union[str, Any]   					=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()}'
			)

