





from ..utils import DummyObject, requires_backends





class 				A_				(      metaclass=UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[str]      =  ['''flax''', '''transformers''']







       def __init__(    self	:	Optional[Any]						,*__A	:	Optional[int]						,**__A	:	int  )						->					List[Any]:
         requires_backends(self						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Tuple						,*__A	:	Union[str, Any]						,**__A	:	str  )						->					Tuple:
         requires_backends(cls						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Optional[int]						,*__A	:	Dict						,**__A	:	Any  )						->					Optional[Any]:
         requires_backends(cls						,['flax', 'transformers']  )





class 				A_				(      metaclass=UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  ['''flax''', '''transformers''']







       def __init__(    self	:	Any						,*__A	:	Any						,**__A	:	Dict  )						->					str:
         requires_backends(self						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Optional[Any]						,*__A	:	Tuple						,**__A	:	List[Any]  )						->					str:
         requires_backends(cls						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	int						,*__A	:	Optional[int]						,**__A	:	Dict  )						->					int:
         requires_backends(cls						,['flax', 'transformers']  )





class 				A_				(      metaclass=UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[Any]      =  ['''flax''', '''transformers''']







       def __init__(    self	:	List[str]						,*__A	:	Dict						,**__A	:	List[Any]  )						->					Union[str, Any]:
         requires_backends(self						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Union[str, Any]						,*__A	:	List[Any]						,**__A	:	Union[str, Any]  )						->					int:
         requires_backends(cls						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[str]						,*__A	:	Optional[int]						,**__A	:	Tuple  )						->					Any:
         requires_backends(cls						,['flax', 'transformers']  )





class 				A_				(      metaclass=UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  ['''flax''', '''transformers''']







       def __init__(    self	:	Any						,*__A	:	Dict						,**__A	:	int  )						->					List[Any]:
         requires_backends(self						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	Dict						,*__A	:	Optional[int]						,**__A	:	Any  )						->					Any:
         requires_backends(cls						,['flax', 'transformers']  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[str]						,*__A	:	List[str]						,**__A	:	Tuple  )						->					Dict:
         requires_backends(cls						,['flax', 'transformers']  )






import unittest

import numpy as np

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

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[Any]						,__A	:	int						,__A	:	str=13						,__A	:	str=7						,__A	:	Optional[Any]=True						,__A	:	Optional[int]=True						,__A	:	int=True						,__A	:	List[str]=True						,__A	:	Dict=99						,__A	:	Dict=32						,__A	:	Dict=5						,__A	:	Dict=4						,__A	:	int=37						,__A	:	Any="gelu"						,__A	:	int=0.1						,__A	:	int=0.1						,__A	:	Dict=512						,__A	:	List[str]=16						,__A	:	Tuple=2						,__A	:	Dict=0.02						,__A	:	int=4						,)						->					Optional[int]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= seq_length
         _lowercase							= is_training
         _lowercase							= use_attention_mask
         _lowercase							= use_token_type_ids
         _lowercase							= use_labels
         _lowercase							= vocab_size
         _lowercase							= hidden_size
         _lowercase							= num_hidden_layers
         _lowercase							= num_attention_heads
         _lowercase							= intermediate_size
         _lowercase							= hidden_act
         _lowercase							= hidden_dropout_prob
         _lowercase							= attention_probs_dropout_prob
         _lowercase							= max_position_embeddings
         _lowercase							= type_vocab_size
         _lowercase							= type_sequence_label_size
         _lowercase							= initializer_range
         _lowercase							= num_choices







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

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

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

         _lowercase							= RobertaPreLayerNormConfig(
             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    __UpperCAmelCase (    self	:	Optional[int]  )						->					Tuple:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs

         _lowercase							= True
         _lowercase							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         _lowercase							= 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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  True

       SCREAMING_SNAKE_CASE_      : Dict      =  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= FlaxRobertaPreLayerNormModelTester(self  )







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Dict:
         for model_class_name in self.all_model_classes:
           _lowercase							= model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )
           _lowercase							= model(np.ones((1, 1)  )  )
           self.assertIsNotNone(__A  )





@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @slow
       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         _lowercase							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         _lowercase							= [1, 11, 5_0265]
         self.assertEqual(list(output.shape  )						,__A  )
         # compare the actual values for a slice.
         _lowercase							= np.array(
             [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]						,dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]						,__A						,atol=1e-4  )  )







       @slow
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         # compare the actual values for a slice.
         _lowercase							= np.array(
             [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]						,dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]						,__A						,atol=1e-4  )  )