





'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.roberta.modeling_flax_roberta import (
       FlaxRobertaForCausalLM,
       FlaxRobertaForMaskedLM,
       FlaxRobertaForMultipleChoice,
       FlaxRobertaForQuestionAnswering,
       FlaxRobertaForSequenceClassification,
       FlaxRobertaForTokenClassification,
       FlaxRobertaModel,
   )
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	seq_length
            _UpperCamelCase       =	is_training
            _UpperCamelCase       =	use_attention_mask
            _UpperCamelCase       =	use_token_type_ids
            _UpperCamelCase       =	use_labels
            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	type_vocab_size
            _UpperCamelCase       =	type_sequence_label_size
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	num_choices





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	RobertaConfig(
                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=lowerCAmelCase__	,			initializer_range=self.initializer_range	,			)

            return config, input_ids, token_type_ids, attention_mask





       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




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







       def snake_case__						(							self       :		List[str]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	config_and_inputs

            _UpperCamelCase       =	True
            _UpperCamelCase       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2					)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )

@require_flax
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   True

       _snake_case     :					Optional[Any]						    =   (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	FlaxRobertaModelTester(self					)







       @slow
       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase       =	model_class_name.from_pretrained('''roberta-base'''	,			from_pt=lowerCAmelCase__					)
                 _UpperCamelCase       =	model(np.ones((1, 1)					)					)
                 self.assertIsNotNone(lowerCAmelCase__					)










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



