


def    a		(							SCREAMING_SNAKE_CASE_					:						list				):






       """simple docstring"""





       UpperCamelCase			:		str      		=						len(_UpperCAmelCase				)
       for cycle_start in range(0							,	array_len - 1				):
              UpperCamelCase			:		Any      		=						array[cycle_start]

              UpperCamelCase			:		str      		=						cycle_start
              for i in range(cycle_start + 1							,	_UpperCAmelCase				):
                     if array[i] < item:
                            pos += 1

              if pos == cycle_start:
                     continue

              while item == array[pos]:
                     pos += 1

              UpperCamelCase		,       UpperCamelCase			:		Tuple      		=						item, array[pos]
              while pos != cycle_start:
                     UpperCamelCase			:		List[Any]      		=						cycle_start
                     for i in range(cycle_start + 1							,	_UpperCAmelCase				):
                            if array[i] < item:
                                   pos += 1

                     while item == array[pos]:
                            pos += 1

                     UpperCamelCase		,       UpperCamelCase			:		str      		=						item, array[pos]

       return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]








"""simple docstring"""
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 	a				:



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







  """simple docstring"""






  lowerCAmelCase  							=      parent
  lowerCAmelCase  							=      batch_size
  lowerCAmelCase  							=      seq_length
  lowerCAmelCase  							=      is_training
  lowerCAmelCase  							=      use_input_mask
  lowerCAmelCase  							=      use_token_type_ids
  lowerCAmelCase  							=      use_labels
  lowerCAmelCase  							=      vocab_size
  lowerCAmelCase  							=      hidden_size
  lowerCAmelCase  							=      num_hidden_layers
  lowerCAmelCase  							=      num_attention_heads
  lowerCAmelCase  							=      intermediate_size
  lowerCAmelCase  							=      hidden_act
  lowerCAmelCase  							=      hidden_dropout_prob
  lowerCAmelCase  							=      attention_probs_dropout_prob
  lowerCAmelCase  							=      max_position_embeddings
  lowerCAmelCase  							=      type_vocab_size
  lowerCAmelCase  							=      type_sequence_label_size
  lowerCAmelCase  							=      initializer_range
  lowerCAmelCase  							=      num_labels
  lowerCAmelCase  							=      num_choices
  lowerCAmelCase  							=      scope



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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

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

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

  lowerCAmelCase  							=      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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModel(config=_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  lowerCAmelCase  							=      [input_ids, input_mask]
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetForMaskedLM(config=_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetForQuestionAnswering(config=_snake_case			)
  lowerCAmelCase  							=      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
  }
  lowerCAmelCase  							=      model(_snake_case			)
  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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForSequenceClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_choices
  lowerCAmelCase  							=      TFMPNetForMultipleChoice(_snake_case			)
  lowerCAmelCase  							=      tf.tile(tf.expand_dims(_snake_case							,    1			)							,    (1, self.num_choices, 1)			)
  lowerCAmelCase  							=      tf.tile(tf.expand_dims(_snake_case							,    1			)							,    (1, self.num_choices, 1)			)
  lowerCAmelCase  							=      {
      'input_ids': multiple_choice_inputs_ids,
      'attention_mask': multiple_choice_input_mask,
  }
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForTokenClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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







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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModelTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    hidden_size=37			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_model(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_masked_lm(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_question_answering(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_sequence_classification(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_multiple_choice(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_token_classification(*_snake_case			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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







@require_tf
class 	a				(					unittest.TestCase   ):



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModel.from_pretrained('microsoft/mpnet-base'			)
  lowerCAmelCase  							=      tf.constant([[0, 1, 2, 3, 4, 5]]			)
  lowerCAmelCase  							=      model(_snake_case			)[0]

  lowerCAmelCase  							=      [1, 6, 7_68]
  self.assertEqual(output.shape							,    _snake_case			)

  lowerCAmelCase  							=      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]							,    _snake_case							,    atol=1E-4			)





