



import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.electra.modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
    )



class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 def __init__(  self		,      a		,      a=1_3		,      a=7		,      a=True		,      a=True		,      a=True		,      a=True		,      a=9_9		,      a=2_4		,      a=3_2		,      a=5		,      a=4		,      a=3_7		,      a="gelu"		,      a=0.1		,      a=0.1		,      a=5_1_2		,      a=1_6		,      a=2		,      a=0.02		,      a=4		,      )		->							Dict:




   """simple docstring"""







   _A          =      parent
   _A          =      batch_size
   _A          =      seq_length
   _A          =      is_training
   _A          =      use_attention_mask
   _A          =      use_token_type_ids
   _A          =      use_labels
   _A          =      vocab_size
   _A          =      hidden_size
   _A          =      embedding_size
   _A          =      num_hidden_layers
   _A          =      num_attention_heads
   _A          =      intermediate_size
   _A          =      hidden_act
   _A          =      hidden_dropout_prob
   _A          =      attention_probs_dropout_prob
   _A          =      max_position_embeddings
   _A          =      type_vocab_size
   _A          =      type_sequence_label_size
   _A          =      initializer_range
   _A          =      num_choices
 def lowercase_						(  self   )		->							Tuple:




   """simple docstring"""







   _A          =      ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size   )

   _A          =      None
   if self.use_attention_mask:
     _A          =      random_attention_mask([self.batch_size, self.seq_length]   )

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

   _A          =      ElectraConfig(
       vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      embedding_size=self.embedding_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		,      initializer_range=self.initializer_range		,      )

   return config, input_ids, token_type_ids, attention_mask



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




   """simple docstring"""







   _A          =      self.prepare_config_and_inputs()
   _A      ,				_A      ,				_A      ,				_A          =      config_and_inputs
   _A          =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
   return config, inputs_dict







@require_flax
class 						_snake_case (     lowerCamelCase   ,unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     True

 lowerCamelCase_       =     (
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      FlaxElectraModelTester(self   )



 @slow
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   for model_class_name in self.all_model_classes:
     if model_class_name == FlaxElectraForMaskedLM:
       _A          =      model_class_name.from_pretrained('''google/electra-small-generator'''   )
     else:
       _A          =      model_class_name.from_pretrained('''google/electra-small-discriminator'''   )
     _A          =      model(np.ones((1, 1)   )   )
     self.assertIsNotNone(a   )




from __future__ import annotations




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




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




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [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								=   [1_9, 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								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   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}''')