



"""simple docstring"""






def 			snake_case_    ( A_   :						List[str]		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					0
       _lowerCamelCase		:							Union[str, Any]            =					len(A_		)

       for i in range(n - 1		):
              for j in range(i + 1,							A_		):
                     if arr[i] > arr[j]:
                            num_inversions += 1

       return num_inversions






def 			snake_case_    ( A_   :						List[str]		):


       '''simple docstring'''





       if len(A_		) <= 1:
              return arr, 0
       _lowerCamelCase		:							int            =					len(A_		) // 2
       _lowerCamelCase		:							int            =					arr[0:mid]
       _lowerCamelCase		:							int            =					arr[mid:]

       _lowerCamelCase     ,       _lowerCamelCase		:							str            =					count_inversions_recursive(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							int            =					count_inversions_recursive(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							Optional[Any]            =					_count_cross_inversions(A_,							A_		)

       _lowerCamelCase		:							Optional[Any]            =					inversion_p + inversions_q + cross_inversions
       return c, num_inversions






def 			snake_case_    ( A_   :						int,							A_   :						Dict		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					[]
       _lowerCamelCase		:							List[str]            =					0
       while i < len(A_		) and j < len(A_		):
              if p[i] > q[j]:
                     # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
                     # These are all inversions. The claim emerges from the
                     # property that P is sorted.
                     num_inversion += len(A_		) - i
                     r.append(q[j]		)
                     j += 1
              else:
                     r.append(p[i]		)
                     i += 1

       if i < len(A_		):
              r.extend(p[i:]		)
       else:
              r.extend(q[j:]		)

       return r, num_inversion






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					[10, 2, 1, 5, 5, 2, 11]

       # this arr has 8 inversions:
       # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

       _lowerCamelCase		:							List[str]            =					count_inversions_bf(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					count_inversions_recursive(A_		)

       assert num_inversions_bf == num_inversions_recursive == 8

       print('''number of inversions = ''',							A_		)

       # testing an array with zero inversion (a sorted arr_1)

       arr_a.sort()
       _lowerCamelCase		:							List[Any]            =					count_inversions_bf(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							List[Any]            =					count_inversions_recursive(A_		)

       assert num_inversions_bf == num_inversions_recursive == 0
       print('''number of inversions = ''',							A_		)

       # an empty list should also have zero inversions
       _lowerCamelCase		:							str            =					[]
       _lowerCamelCase		:							Optional[int]            =					count_inversions_bf(A_		)
       _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					count_inversions_recursive(A_		)

       assert num_inversions_bf == num_inversions_recursive == 0
       print('''number of inversions = ''',							A_		)


if __name__ == "__main__":
   main()







'''simple docstring'''





import unittest

import numpy as np

from transformers import AlbertConfig, 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():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=True					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=4					,  )       ->	List[str]:


      """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           =    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    a__    (self			)       ->	str:


      """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           =    AlbertConfig(
          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=A					,  initializer_range=self.initializer_range					,  )

      return config, input_ids, token_type_ids, attention_mask



 def    a__    (self			)       ->	List[str]:


      """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 __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



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


      """simple docstring"""



      _a           =    FlaxAlbertModelTester(self			)



 @slow
 def    a__    (self			)       ->	str:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a           =    model_class_name.from_pretrained('''albert-base-v2'''			)
           _a           =    model(np.ones((1, 1)			)			)
           self.assertIsNotNone(A			)






@require_flax
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    FlaxAlbertModel.from_pretrained('''albert-base-v2'''			)
      _a           =    np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]			)
      _a           =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      _a           =    model(A					,  attention_mask=A			)[0]
      _a           =    (1, 11, 768)
      self.assertEqual(output.shape					,  A			)
      _a           =    np.array(
          [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]			)

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]					,  A					,  atol=1E-4			)			)



