

import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
					import torch

					from transformers import (
					    MraForMaskedLM,
					    MraForMultipleChoice,
					    MraForQuestionAnswering,
					    MraForSequenceClassification,
					    MraForTokenClassification,
					    MraModel,
					)
					from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str=2				,  _UpperCamelCase					:							Dict=8				,  _UpperCamelCase					:							str=True				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							Dict=True				,  _UpperCamelCase					:							Optional[int]=9_9				,  _UpperCamelCase					:							Any=1_6				,  _UpperCamelCase					:							List[str]=5				,  _UpperCamelCase					:							Optional[Any]=2				,  _UpperCamelCase					:							str=3_6				,  _UpperCamelCase					:							Dict="gelu"				,  _UpperCamelCase					:							Optional[int]=0.0				,  _UpperCamelCase					:							str=0.0				,  _UpperCamelCase					:							Any=5_1_2				,  _UpperCamelCase					:							int=1_6				,  _UpperCamelCase					:							List[Any]=2				,  _UpperCamelCase					:							Tuple=0.02				,  _UpperCamelCase					:							Any=3				,  _UpperCamelCase					:							Dict=4				,  _UpperCamelCase					:							Dict=None				,  ) ->Optional[Any]:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      seq_length
									snake_case_  =      is_training
									snake_case_  =      use_input_mask
									snake_case_  =      use_token_type_ids
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      type_vocab_size
									snake_case_  =      type_sequence_label_size
									snake_case_  =      initializer_range
									snake_case_  =      num_labels
									snake_case_  =      num_choices
									snake_case_  =      scope







			def 							snake_case__(	self					:							List[Any]				) ->List[Any]:
									snake_case_  =      ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size				)

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

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

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

									snake_case_  =      self.get_config()

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







			def 							snake_case__(	self					:							Optional[int]				) ->Optional[Any]:
									return MraConfig(
									    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				,  is_decoder=_UpperCamelCase				,  initializer_range=self.initializer_range				,  )







			def 							snake_case__(	self					:							List[Any]				) ->str:
									snake_case_  =      self.get_config()
									snake_case_  =      3_0_0
									return config







			def 							snake_case__(	self					:							List[str]				) ->Optional[Any]:
									(
									    (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       
									)  =      self.prepare_config_and_inputs()

									snake_case_  =      True
									snake_case_  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
									snake_case_  =      ids_tensor([self.batch_size, self.seq_length]				,  vocab_size=2				)

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







			def 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Dict				) ->Dict:
									snake_case_  =      MraModel(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				)
									snake_case_  =      model(_UpperCamelCase				,  token_type_ids=_UpperCamelCase				)
									snake_case_  =      model(_UpperCamelCase				)
									self.parent.assertEqual(result.last_hidden_state.shape				,  (self.batch_size, self.seq_length, self.hidden_size)				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Any				,  ) ->Optional[Any]:
									snake_case_  =      True
									snake_case_  =      MraModel(_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(
									    _UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  encoder_hidden_states=_UpperCamelCase				,  encoder_attention_mask=_UpperCamelCase				,  )
									snake_case_  =      model(
									    _UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  encoder_hidden_states=_UpperCamelCase				,  )
									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				)
									self.parent.assertEqual(result.last_hidden_state.shape				,  (self.batch_size, self.seq_length, self.hidden_size)				)







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[Any]				) ->str:
									snake_case_  =      MraForMaskedLM(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  labels=_UpperCamelCase				)
									self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.seq_length, self.vocab_size)				)







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Optional[Any]				) ->Any:
									snake_case_  =      MraForQuestionAnswering(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(
									    _UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  start_positions=_UpperCamelCase				,  end_positions=_UpperCamelCase				,  )
									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 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[str]				) ->Optional[Any]:
									snake_case_  =      self.num_labels
									snake_case_  =      MraForSequenceClassification(_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  labels=_UpperCamelCase				)
									self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.num_labels)				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[int]				) ->int:
									snake_case_  =      self.num_labels
									snake_case_  =      MraForTokenClassification(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  labels=_UpperCamelCase				)
									self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.seq_length, self.num_labels)				)







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Any				) ->Any:
									snake_case_  =      self.num_choices
									snake_case_  =      MraForMultipleChoice(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      input_ids.unsqueeze(1				).expand(-1				,  self.num_choices				,  -1				).contiguous()
									snake_case_  =      token_type_ids.unsqueeze(1				).expand(-1				,  self.num_choices				,  -1				).contiguous()
									snake_case_  =      input_mask.unsqueeze(1				).expand(-1				,  self.num_choices				,  -1				).contiguous()
									snake_case_  =      model(
									    _UpperCamelCase				,  attention_mask=_UpperCamelCase				,  token_type_ids=_UpperCamelCase				,  labels=_UpperCamelCase				,  )
									self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.num_choices)				)







			def 							snake_case__(	self					:							str				) ->List[str]:
									snake_case_  =      self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       (
									    snake_case_
									),       
									)  =      config_and_inputs
									snake_case_  =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
									return config, inputs_dict






@require_torch
class       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							False
			SCREAMING_SNAKE_CASE	:  str		     =							False
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							False
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							False

			SCREAMING_SNAKE_CASE	:  List[str]		     =							()







			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      MraModelTester(self				)
									snake_case_  =      ConfigTester(self				,  config_class=_UpperCamelCase				,  hidden_size=3_7				)







			def 							snake_case__(	self					:							Dict				) ->Optional[int]:
									self.config_tester.run_common_tests()







			def 							snake_case__(	self					:							Union[str, Any]				) ->Dict:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase				)







			def 							snake_case__(	self					:							int				) ->Optional[int]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															snake_case_  =      type
															self.model_tester.create_and_check_model(*_UpperCamelCase				)







			def 							snake_case__(	self					:							Union[str, Any]				) ->List[Any]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->Tuple:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase				)







			def 							snake_case__(	self					:							Any				) ->Optional[int]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase				)







			def 							snake_case__(	self					:							str				) ->List[str]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase				)







			def 							snake_case__(	self					:							int				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							int				) ->str:
									for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_  =      MraModel.from_pretrained(_UpperCamelCase				)
															self.assertIsNotNone(_UpperCamelCase				)







			@unittest.skip(reason='''MRA does not output attentions'''				)
			def 							snake_case__(	self					:							Union[str, Any]				) ->Tuple:
									return






@require_torch
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			@slow
			def 							snake_case__(	self					:							List[Any]				) ->Optional[Any]:
									snake_case_  =      MraModel.from_pretrained('''uw-madison/mra-base-512-4'''				)
									snake_case_  =      torch.arange(2_5_6				).unsqueeze(0				)

									with torch.no_grad():
															snake_case_  =      model(_UpperCamelCase				)[0]

									snake_case_  =      torch.Size((1, 2_5_6, 7_6_8)				)
									self.assertEqual(output.shape				,  _UpperCamelCase				)

									snake_case_  =      torch.tensor(
									    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]				)

									self.assertTrue(torch.allclose(output[:, :3, :3]				,  _UpperCamelCase				,  atol=1e-4				)				)







			@slow
			def 							snake_case__(	self					:							List[str]				) ->int:
									snake_case_  =      MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''				)
									snake_case_  =      torch.arange(2_5_6				).unsqueeze(0				)

									with torch.no_grad():
															snake_case_  =      model(_UpperCamelCase				)[0]

									snake_case_  =      5_0_2_6_5

									snake_case_  =      torch.Size((1, 2_5_6, vocab_size)				)
									self.assertEqual(output.shape				,  _UpperCamelCase				)

									snake_case_  =      torch.tensor(
									    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]				)

									self.assertTrue(torch.allclose(output[:, :3, :3]				,  _UpperCamelCase				,  atol=1e-4				)				)







			@slow
			def 							snake_case__(	self					:							Union[str, Any]				) ->Any:
									snake_case_  =      MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''				)
									snake_case_  =      torch.arange(4_0_9_6				).unsqueeze(0				)

									with torch.no_grad():
															snake_case_  =      model(_UpperCamelCase				)[0]

									snake_case_  =      5_0_2_6_5

									snake_case_  =      torch.Size((1, 4_0_9_6, vocab_size)				)
									self.assertEqual(output.shape				,  _UpperCamelCase				)

									snake_case_  =      torch.tensor(
									    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]				)

									self.assertTrue(torch.allclose(output[:, :3, :3]				,  _UpperCamelCase				,  atol=1e-4				)				)

'''simple docstring'''

from __future__ import annotations

import pandas as pd


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	[0] * no_of_processes
 lowerCAmelCase__     :   Any     		=	[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i]

 lowerCAmelCase__     :   str     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	9_9_9_9_9_9_9_9_9
 lowerCAmelCase__     :   int     		=	0
 lowerCAmelCase__     :   str     		=	False

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

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

  lowerCAmelCase__     :   Optional[Any]     		=	remaining_time[short]
  if minm == 0:
   lowerCAmelCase__     :   Optional[int]     		=	9_9_9_9_9_9_9_9_9

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

   # Find finish time of current process
   lowerCAmelCase__     :   Tuple     		=	increment_time + 1

   # Calculate waiting time
   lowerCAmelCase__     :   Dict     		=	finish_time - arrival_time[short]
   lowerCAmelCase__     :   Optional[Any]     		=	finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCAmelCase__     :   Tuple     		=	0

        # Increment time
  increment_time += 1
 return waiting_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[0] * no_of_processes
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i] + waiting_time[i]
 return turn_around_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	0
 lowerCAmelCase__     :   Optional[int]     		=	0
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Tuple     		=	total_waiting_time + waiting_time[i]
  lowerCAmelCase__     :   str     		=	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""")
     snake_case									=    int(input())
     snake_case									=    [0] * no_of_processes
     snake_case									=    [0] * no_of_processes
     snake_case									=    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))
          snake_case					,					snake_case									=    map(int, input().split())

     snake_case									=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     snake_case									=    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)





