"""simple docstring"""
from collections import deque




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					Optional[Any]      							=     process_name  # process name
        a__:					List[str]      							=     arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        a__:					List[str]      							=     arrival_time
        a__:					Any      							=     burst_time  # remaining burst time
        a__:					Optional[int]      							=     0  # total time of the process wait in ready queue
        a__:					Optional[Any]      							=     0  # time from arrival time to completion time







class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      )     ->      None:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     number_of_queues
        # time slice of queues that round robin algorithm applied
        a__:					int      							=     time_slices
        # unfinished process is in this ready_queue
        a__:					Optional[int]      							=     queue
        # current time
        a__:					Dict      							=     current_time
        # finished process is in this sequence queue
        a__:					deque[Process]      							=     deque()



      def   lowerCamelCase_       (						self)     ->      list[str]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      list[int]:





        '''simple docstring'''





        a__:					List[str]      							=     []
        for i in range(len(lowercase)):
          waiting_times.append(queue[i].waiting_time)
        return waiting_times



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      list[int]:





        '''simple docstring'''





        a__:					Any      							=     []
        for i in range(len(lowercase)):
          turnaround_times.append(queue[i].turnaround_time)
        return turnaround_times



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      list[int]:





        '''simple docstring'''





        a__:					Optional[int]      							=     []
        for i in range(len(lowercase)):
          completion_times.append(queue[i].stop_time)
        return completion_times



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      list[int]:





        '''simple docstring'''





        return [q.burst_time for q in queue]



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      deque[Process]:





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      tuple[deque[Process], deque[Process]]:





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self)     ->      deque[Process]:





        '''simple docstring'''





        for i in range(self.number_of_queues - 1):
          a__							,   a__:					Optional[int]      							=     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

 lowercase__ 	=     Process('P1', 0, 53)
 lowercase__ 	=     Process('P2', 0, 17)
 lowercase__ 	=     Process('P3', 0, 68)
 lowercase__ 	=     Process('P4', 0, 24)
 lowercase__ 	=     3
 lowercase__ 	=     [17, 25]
 lowercase__ 	=     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])})

 lowercase__ 	=     Process('P1', 0, 53)
 lowercase__ 	=     Process('P2', 0, 17)
 lowercase__ 	=     Process('P3', 0, 68)
 lowercase__ 	=     Process('P4', 0, 24)
 lowercase__ 	=     3
 lowercase__ 	=     [17, 25]
 lowercase__ 	=     deque([Pa, Pa, Pa, Pa])
 lowercase__ 	=     MLFQ(number_of_queues, time_slices, queue, 0)
 lowercase__ 	=     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()}"
 )



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

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=12   ,      lowercase=7   ,      lowercase=True   ,      lowercase=True   ,      lowercase=True   ,      lowercase=99   ,      lowercase=32   ,      lowercase=32   ,      lowercase=2   ,      lowercase=4   ,      lowercase=37   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=5_12   ,      lowercase=0.02   ,      lowercase=0   ,      lowercase=None   ,      )     ->      Optional[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     parent
        a__:					Any      							=     batch_size
        a__:					int      							=     seq_length
        a__:					List[str]      							=     is_training
        a__:					Any      							=     use_input_mask
        a__:					Optional[Any]      							=     use_labels
        a__:					List[Any]      							=     vocab_size
        a__:					Optional[Any]      							=     hidden_size
        a__:					Any      							=     projection_dim
        a__:					List[str]      							=     num_hidden_layers
        a__:					Dict      							=     num_attention_heads
        a__:					int      							=     intermediate_size
        a__:					Tuple      							=     dropout
        a__:					Union[str, Any]      							=     attention_dropout
        a__:					str      							=     max_position_embeddings
        a__:					List[str]      							=     initializer_range
        a__:					Optional[int]      							=     scope
        a__:					List[Any]      							=     bos_token_id



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					str      							=     ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size)

        a__:					str      							=     None
        if self.use_input_mask:
          a__:					List[str]      							=     random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
          a__:					str      							=     input_mask.numpy()
          a__							,   a__:					Any      							=     input_mask.shape
          a__:					Tuple      							=     np.random.randint(1   ,      seq_length - 1   ,      size=(batch_size,))
          for batch_idx, start_index in enumerate(lowercase):
            a__:					Tuple      							=     1
            a__:					Any      							=     0

        a__:					Any      							=     self.get_config()

        return config, input_ids, tf.convert_to_tensor(lowercase)



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





        '''simple docstring'''





        return BlipTextConfig(
            vocab_size=self.vocab_size   ,      hidden_size=self.hidden_size   ,      projection_dim=self.projection_dim   ,      num_hidden_layers=self.num_hidden_layers   ,      num_attention_heads=self.num_attention_heads   ,      intermediate_size=self.intermediate_size   ,      dropout=self.dropout   ,      attention_dropout=self.attention_dropout   ,      max_position_embeddings=self.max_position_embeddings   ,      initializer_range=self.initializer_range   ,      bos_token_id=self.bos_token_id   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Any      							=     TFBlipTextModel(config=lowercase)
        a__:					Union[str, Any]      							=     model(lowercase   ,      attention_mask=lowercase   ,      training=lowercase)
        a__:					List[Any]      							=     model(lowercase   ,      training=lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape   ,      (self.batch_size, self.hidden_size))



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Tuple      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					Optional[Any]      							=     config_and_inputs
        a__:					Dict      							=     {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(TFBlipTextModel,) if is_tf_available() else ()
      a__				=		False
      a__				=		False
      a__				=		False



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     BlipTextModelTester(self)
        a__:					int      							=     ConfigTester(self   ,      config_class=lowercase   ,      hidden_size=37)



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        self.config_tester.run_common_tests()



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





        '''simple docstring'''





        a__:					List[str]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



      @unittest.skip(reason='Blip does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        pass



      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Tuple      							=     TFBlipTextModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase=True)     ->      str:





        '''simple docstring'''





        super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase)


