

'''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  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=13      ,    _UpperCamelCase=7      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=99      ,    _UpperCamelCase=32      ,    _UpperCamelCase=5      ,    _UpperCamelCase=4      ,    _UpperCamelCase=37      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=16      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=4      ,    ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size  )

      _lowercase      :				int      					=       None
      if self.use_attention_mask:
         _lowercase      :				List[str]      					=       random_attention_mask([self.batch_size, self.seq_length]  )

      _lowercase      :				Union[str, Any]      					=       None
      if self.use_token_type_ids:
         _lowercase      :				Any      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size  )

      _lowercase      :				str      					=       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=_SCREAMING_SNAKE_CASE      ,    initializer_range=self.initializer_range      ,    )

      return config, input_ids, token_type_ids, attention_mask






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.prepare_config_and_inputs()
      _lowercase      :				Optional[Any]      					=       config_and_inputs
      _lowercase      :				Dict      					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict




@require_flax
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							(
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       FlaxAlbertModelTester(self  )






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for model_class_name in self.all_model_classes:
         _lowercase      :				Union[str, Any]      					=       model_class_name.from_pretrained("albert-base-v2"  )
         _lowercase      :				Any      					=       model(np.ones((1, 1)  )  )
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )




@require_flax
class        a__  (							unittest.TestCase  ):


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       FlaxAlbertModel.from_pretrained("albert-base-v2"  )
      _lowercase      :				str      					=       np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
      _lowercase      :				Dict      					=       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      _lowercase      :				Optional[Any]      					=       model(_SCREAMING_SNAKE_CASE      ,    attention_mask=_SCREAMING_SNAKE_CASE  )[0]
      _lowercase      :				Any      					=       (1, 11, 768)
      self.assertEqual(output.shape      ,    _SCREAMING_SNAKE_CASE  )
      _lowercase      :				Any      					=       np.array(
          [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]  )

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






'''simple docstring'''
from timeit import timeit







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				Union[str, Any]      					=       0
   while number:
      number &= number - 1
      result += 1
   return result







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				int      					=       0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result







def  _A		(     )      ->     None:

   def do_benchmark(snake_case					) -> None:
      _lowercase      :				Optional[int]      					=       "import __main__ as z"
      print(F'''Benchmark when {number = }:'''					)
      print(F'''{get_set_bits_count_using_modulo_operator(snake_case					) = }'''					)
      _lowercase      :				int      					=       timeit("z.get_set_bits_count_using_modulo_operator(25)"		,							setup=snake_case					)
      print(F'''timeit() runs in {timing} seconds'''					)
      print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(snake_case					) = }'''					)
      _lowercase      :				Optional[int]      					=       timeit(
          "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"		,							setup=snake_case		,							)
      print(F'''timeit() runs in {timing} seconds'''					)

   for number in (25, 37, 58, 0):
      do_benchmark(snake_case					)
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()






