from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
        TFMPNetForMaskedLM,
        TFMPNetForMultipleChoice,
        TFMPNetForQuestionAnswering,
        TFMPNetForSequenceClassification,
        TFMPNetForTokenClassification,
        TFMPNetModel,
    )
class 				lowerCamelCase			:






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase=13  ,		__lowerCamelCase=7  ,		__lowerCamelCase=True  ,		__lowerCamelCase=True  ,		__lowerCamelCase=False  ,		__lowerCamelCase=True  ,		__lowerCamelCase=99  ,		__lowerCamelCase=64  ,		__lowerCamelCase=2  ,		__lowerCamelCase=4  ,		__lowerCamelCase=64  ,		__lowerCamelCase="gelu"  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=5_12  ,		__lowerCamelCase=16  ,		__lowerCamelCase=2  ,		__lowerCamelCase=0.02  ,		__lowerCamelCase=3  ,		__lowerCamelCase=4  ,		__lowerCamelCase=None  ,		)     ->				Dict:


          '''simple docstring'''







          snake_case:						Dict         =   parent
          snake_case:						Optional[Any]         =   batch_size
          snake_case:						List[Any]         =   seq_length
          snake_case:						List[Any]         =   is_training
          snake_case:						Union[str, Any]         =   use_input_mask
          snake_case:						Dict         =   use_token_type_ids
          snake_case:						List[str]         =   use_labels
          snake_case:						str         =   vocab_size
          snake_case:						Any         =   hidden_size
          snake_case:						List[Any]         =   num_hidden_layers
          snake_case:						Tuple         =   num_attention_heads
          snake_case:						int         =   intermediate_size
          snake_case:						Dict         =   hidden_act
          snake_case:						Any         =   hidden_dropout_prob
          snake_case:						List[str]         =   attention_probs_dropout_prob
          snake_case:						Dict         =   max_position_embeddings
          snake_case:						Optional[int]         =   type_vocab_size
          snake_case:						Dict         =   type_sequence_label_size
          snake_case:						List[Any]         =   initializer_range
          snake_case:						Optional[int]         =   num_labels
          snake_case:						Tuple         =   num_choices
          snake_case:						Dict         =   scope






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case:						Dict         =   ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

          snake_case:						Optional[Any]         =   None
          if self.use_input_mask:
              snake_case:						Any         =   random_attention_mask([self.batch_size, self.seq_length]						)

          snake_case:						Dict         =   None
          snake_case:						Dict         =   None
          snake_case:						Dict         =   None
          if self.use_labels:
              snake_case:						List[Any]         =   ids_tensor([self.batch_size]  ,		self.type_sequence_label_size						)
              snake_case:						Optional[int]         =   ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels						)
              snake_case:						Union[str, Any]         =   ids_tensor([self.batch_size]  ,		self.num_choices						)

          snake_case:						str         =   MPNetConfig(
              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  ,		initializer_range=self.initializer_range  ,		)
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				List[str]:


          '''simple docstring'''







          snake_case:						int         =   TFMPNetModel(config=__lowerCamelCase						)
          snake_case:						Union[str, Any]         =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
          snake_case:						int         =   model(__lowerCamelCase						)
          snake_case:						int         =   [input_ids, input_mask]
          snake_case:						Union[str, Any]         =   model(__lowerCamelCase						)
          self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Dict         =   TFMPNetForMaskedLM(config=__lowerCamelCase						)
          snake_case:						Dict         =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
          snake_case:						Tuple         =   model(__lowerCamelCase						)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				str:


          '''simple docstring'''







          snake_case:						List[Any]         =   TFMPNetForQuestionAnswering(config=__lowerCamelCase						)
          snake_case:						str         =   {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
          }
          snake_case:						Any         =   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 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				str:


          '''simple docstring'''







          snake_case:						Any         =   self.num_labels
          snake_case:						List[Any]         =   TFMPNetForSequenceClassification(__lowerCamelCase						)
          snake_case:						List[Any]         =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
          snake_case:						List[str]         =   model(__lowerCamelCase						)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels)						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Any:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   self.num_choices
          snake_case:						Optional[int]         =   TFMPNetForMultipleChoice(__lowerCamelCase						)
          snake_case:						Optional[Any]         =   tf.tile(tf.expand_dims(__lowerCamelCase  ,		1						)  ,		(1, self.num_choices, 1)						)
          snake_case:						List[Any]         =   tf.tile(tf.expand_dims(__lowerCamelCase  ,		1						)  ,		(1, self.num_choices, 1)						)
          snake_case:						List[Any]         =   {
              """input_ids""": multiple_choice_inputs_ids,
              """attention_mask""": multiple_choice_input_mask,
          }
          snake_case:						Optional[int]         =   model(__lowerCamelCase						)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_choices)						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Optional[int]         =   self.num_labels
          snake_case:						Any         =   TFMPNetForTokenClassification(__lowerCamelCase						)
          snake_case:						Dict         =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
          snake_case:						Optional[Any]         =   model(__lowerCamelCase						)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels)						)






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







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





@require_tf
class 				lowerCamelCase			(							__snake_case				,     __snake_case				,     unittest.TestCase					):
      __lowerCamelCase							=					(
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      __lowerCamelCase							=					(
          {
              'feature-extraction': TFMPNetModel,
              'fill-mask': TFMPNetForMaskedLM,
              'question-answering': TFMPNetForQuestionAnswering,
              'text-classification': TFMPNetForSequenceClassification,
              'token-classification': TFMPNetForTokenClassification,
              'zero-shot': TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __lowerCamelCase							=					False
      __lowerCamelCase							=					False






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Dict         =   TFMPNetModelTester(self						)
          snake_case:						Optional[Any]         =   ConfigTester(self  ,		config_class=__lowerCamelCase  ,		hidden_size=37						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          self.config_tester.run_common_tests()






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						Any         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case:						Optional[int]         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						int         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						List[Any]         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase						)






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


          '''simple docstring'''







          snake_case:						str         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Dict         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase						)






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          for model_name in ["microsoft/mpnet-base"]:
              snake_case:						str         =   TFMPNetModel.from_pretrained(__lowerCamelCase						)
              self.assertIsNotNone(__lowerCamelCase						)





@require_tf
class 				lowerCamelCase			(							unittest.TestCase					):






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						int         =   TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""						)
          snake_case:						int         =   tf.constant([[0, 1, 2, 3, 4, 5]]						)
          snake_case:						int         =   model(__lowerCamelCase						)[0]

          snake_case:						int         =   [1, 6, 7_68]
          self.assertEqual(output.shape  ,		__lowerCamelCase						)

          snake_case:						List[str]         =   tf.constant(
              [
                  [
                      [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                      [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                      [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                  ]
              ]						)
          tf.debugging.assert_near(output[:, :3, :3]  ,		__lowerCamelCase  ,		atol=1e-4						)



from __future__ import annotations

from statistics import mean

def   a_					(_lowerCAmelCase  :   list[int]       )->				list[int]:
    snake_case:						Tuple         =   2
    snake_case:						str         =   list(_lowerCAmelCase       )
    snake_case:						Union[str, Any]         =   [0] * len(_lowerCAmelCase       )
    snake_case:						List[Any]         =   0
    while True:
        snake_case:						Optional[int]         =   True
        for i, burst_time in enumerate(_lowerCAmelCase       ):
            if rem_burst_times[i] > 0:
                snake_case:						Dict         =   False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    snake_case:						Optional[Any]         =   t - burst_time
                    snake_case:						Tuple         =   0
        if done is True:
            return waiting_times

def   a_					(_lowerCAmelCase  :   list[int]			,	_lowerCAmelCase  :   list[int]       )->				list[int]:
    return [burst + waiting for burst, waiting in zip(_lowerCAmelCase			,	_lowerCAmelCase       )]


if __name__ == "__main__":
    __lowerCAmelCase    :  List[Any]										= [3, 5, 7]
    __lowerCAmelCase    :  List[Any]										= calculate_waiting_times(burst_times)
    __lowerCAmelCase    :  str										= calculate_turn_around_times(burst_times, waiting_times)
    print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
    for i, burst_time in enumerate(burst_times):
        print(
            F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
            F"""{turn_around_times[i]}"""
        )
    print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
    print(F"""Average turn around time = {mean(turn_around_times):.5f}""")


