




from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
      )





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	List[Any]       ,       _lowerCAmelCase						:	Dict       ,       _lowerCAmelCase						:	Optional[Any]=13       ,       _lowerCAmelCase						:	Any=7       ,       _lowerCAmelCase						:	Optional[int]=True       ,       _lowerCAmelCase						:	Union[str, Any]=True       ,       _lowerCAmelCase						:	List[Any]=True       ,       _lowerCAmelCase						:	List[str]=True       ,       _lowerCAmelCase						:	Union[str, Any]=99       ,       _lowerCAmelCase						:	List[Any]=32       ,       _lowerCAmelCase						:	Tuple=2       ,       _lowerCAmelCase						:	List[str]=4       ,       _lowerCAmelCase						:	Optional[int]=37       ,       _lowerCAmelCase						:	Dict="gelu"       ,       _lowerCAmelCase						:	Union[str, Any]=0.1       ,       _lowerCAmelCase						:	Dict=0.1       ,       _lowerCAmelCase						:	List[str]=512       ,       _lowerCAmelCase						:	str=16       ,       _lowerCAmelCase						:	Dict=2       ,       _lowerCAmelCase						:	Tuple=0.02       ,       _lowerCAmelCase						:	Union[str, Any]=3       ,       _lowerCAmelCase						:	List[Any]=4       ,       _lowerCAmelCase						:	List[str]=None       ,       ):
       SCREAMING_SNAKE_CASE_					=    parent
       SCREAMING_SNAKE_CASE_					=    13
       SCREAMING_SNAKE_CASE_					=    7
       SCREAMING_SNAKE_CASE_					=    True
       SCREAMING_SNAKE_CASE_					=    True
       SCREAMING_SNAKE_CASE_					=    True
       SCREAMING_SNAKE_CASE_					=    True
       SCREAMING_SNAKE_CASE_					=    99
       SCREAMING_SNAKE_CASE_					=    32
       SCREAMING_SNAKE_CASE_					=    2
       SCREAMING_SNAKE_CASE_					=    4
       SCREAMING_SNAKE_CASE_					=    37
       SCREAMING_SNAKE_CASE_					=    'gelu'
       SCREAMING_SNAKE_CASE_					=    0.1
       SCREAMING_SNAKE_CASE_					=    0.1
       SCREAMING_SNAKE_CASE_					=    512
       SCREAMING_SNAKE_CASE_					=    16
       SCREAMING_SNAKE_CASE_					=    False
       SCREAMING_SNAKE_CASE_					=    -1
       SCREAMING_SNAKE_CASE_					=    True
       SCREAMING_SNAKE_CASE_					=    2
       SCREAMING_SNAKE_CASE_					=    0.02
       SCREAMING_SNAKE_CASE_					=    3
       SCREAMING_SNAKE_CASE_					=    4
       SCREAMING_SNAKE_CASE_					=    None



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size		)

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

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

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

       SCREAMING_SNAKE_CASE_					=    DebertaConfig(
           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       ,       max_relative_positions=self.max_relative_positions       ,       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    lowerCAmelCase_						(							self						:	List[str]       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaModel(config=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       SCREAMING_SNAKE_CASE_					=    [input_ids, input_mask]
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)

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



 def    lowerCAmelCase_						(							self						:	List[Any]       ,       _lowerCAmelCase						:	List[Any]       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	List[Any]       ,       _lowerCAmelCase						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaForMaskedLM(config=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)		)



 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	int		):
       SCREAMING_SNAKE_CASE_					=    self.num_labels
       SCREAMING_SNAKE_CASE_					=    TFDebertaForSequenceClassification(config=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels)		)



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str		):
       SCREAMING_SNAKE_CASE_					=    self.num_labels
       SCREAMING_SNAKE_CASE_					=    TFDebertaForTokenClassification(config=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.num_labels)		)



 def    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	str		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaForQuestionAnswering(config=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       SCREAMING_SNAKE_CASE_					=    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						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,				(
           SCREAMING_SNAKE_CASE_		
       )		,						
       )					=    config_and_inputs
       SCREAMING_SNAKE_CASE_					=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_tf
class  lowerCamelCase_							(   UpperCAmelCase_ ,     UpperCAmelCase_ ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					(
     (
         TFDebertaModel,
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 lowercase_       					=					(
     {
         "feature-extraction": TFDebertaModel,
         "fill-mask": TFDebertaForMaskedLM,
         "question-answering": TFDebertaForQuestionAnswering,
         "text-classification": TFDebertaForSequenceClassification,
         "token-classification": TFDebertaForTokenClassification,
         "zero-shot": TFDebertaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 lowercase_       					=					False
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaModelTester(self		)
       SCREAMING_SNAKE_CASE_					=    ConfigTester(self       ,       config_class=_lowerCAmelCase       ,       hidden_size=37		)



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       self.config_tester.run_common_tests()



 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase		)




 @slow
 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaModel.from_pretrained('kamalkraj/deberta-base'		)
       self.assertIsNotNone(_lowerCAmelCase		)





@require_tf
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







 @unittest.skip(reason='Model not available yet'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       pass




 @slow
 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    TFDebertaModel.from_pretrained('kamalkraj/deberta-base'		)
       SCREAMING_SNAKE_CASE_					=    tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]		)
       SCREAMING_SNAKE_CASE_					=    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       attention_mask=_lowerCAmelCase		)[0]

       SCREAMING_SNAKE_CASE_					=    tf.constant(
           [
               [
                   [-0.5985_5896, -0.8055_2566, -0.846_2135],
                   [1.448_4025, -0.9348_3794, -0.8059_3085],
                   [0.312_2741, 0.0031_6059, -1.413_1377],
               ]
           ]		)
       tf.debugging.assert_near(output[:, 1:4, 1:4]       ,       _lowerCAmelCase       ,       atol=1E-4		)





from __future__ import annotations

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      SCREAMING_SNAKE_CASE_					=    [0] * len(__UpperCAmelCase							)
      for i in range(1    ,    len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_					=    duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]    ,    __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(__UpperCAmelCase							)
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)


if __name__ == "__main__":
   # process id's
   lowerCamelCase__				:     Union[str, Any]			       =							[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   lowerCamelCase__				:     Optional[Any]			       =							[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   lowerCamelCase__				:     Dict			       =							calculate_waiting_times(duration_times)
   lowerCamelCase__				:     int			       =							calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   lowerCamelCase__				:     Any			       =							calculate_average_waiting_time(waiting_times)
   lowerCamelCase__				:     Tuple			       =							calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')