




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 :  Dict						,							A_ :  Optional[int]						,							A_ :  Any=13						,							A_ :  Union[str, Any]=7						,							A_ :  Tuple=True						,							A_ :  Optional[Any]=True						,							A_ :  int=True						,							A_ :  List[Any]=True						,							A_ :  Dict=99						,							A_ :  Optional[Any]=32						,							A_ :  Optional[Any]=5						,							A_ :  Union[str, Any]=4						,							A_ :  Any=37						,							A_ :  Tuple="gelu"						,							A_ :  Optional[Any]=0.1						,							A_ :  Dict=0.1						,							A_ :  List[Any]=512						,							A_ :  Union[str, Any]=16						,							A_ :  Tuple=2						,							A_ :  Dict=0.02						,							A_ :  List[Any]=4						,							)       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   seq_length
       lowerCamelCase_							=   is_training
       lowerCamelCase_							=   use_attention_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_choices

 def     a__			(    self :  str  )       ->     str:

       """simple docstring"""






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

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

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

       lowerCamelCase_							=   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 :  Dict  )       ->     int:

       """simple docstring"""






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







@require_flax
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )

 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   FlaxAlbertModelTester(self  )





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

       """simple docstring"""






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







@require_flax
class   A(						unittest.TestCase       ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_							=   FlaxAlbertModel.from_pretrained('albert-base-v2'  )
       lowerCamelCase_							=   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
       lowerCamelCase_							=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       lowerCamelCase_							=   model(A_						,							attention_mask=A_  )[0]
       lowerCamelCase_							=   (1, 11, 768)
       self.assertEqual(output.shape						,							A_  )
       lowerCamelCase_							=   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  )  )









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      if not all(char in '01' for char in bin_string					):
            raise ValueError('Non-binary value was passed to the function'					)
      if not bin_string:
            raise ValueError('Empty string was passed to the function'					)
      lowerCamelCase_							=   ''
      while len(lowercase					) % 3 != 0:
            lowerCamelCase_							=   '0' + bin_string
      lowerCamelCase_							=   [
          bin_string[index : index + 3]
          for index in range(len(lowercase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCamelCase_							=   0
            for index, val in enumerate(lowercase					):
                  oct_val += int(2 ** (2 - index) * int(lowercase					)					)
            oct_string += str(lowercase					)
      return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()



