


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 		_snake_case  (				unittest.TestCase							):




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size   )

       a						:Union[str, Any]										=	None
       if self.use_attention_mask:
             a						:int										=	random_attention_mask([self.batch_size, self.seq_length]   )

       a						:Optional[int]										=	None
       if self.use_token_type_ids:
             a						:Any										=	ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size   )

       a						:Optional[int]										=	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=_lowerCamelCase ,  initializer_range=self.initializer_range ,  )

       return config, input_ids, token_type_ids, attention_mask




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	self.prepare_config_and_inputs()
       a						:List[str]										=	config_and_inputs
       a						:str										=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.prepare_config_and_inputs()
       a						:Optional[Any]										=	config_and_inputs

       a						:Any										=	True
       a						:Optional[int]										=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       a						:Optional[int]										=	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 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	True

 SCREAMING_SNAKE_CASE__				=	(
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	FlaxRobertaPreLayerNormModelTester(self   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       for model_class_name in self.all_model_classes:
             a						:Tuple										=	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,  from_pt=_lowerCamelCase   )
             a						:Dict										=	model(np.ones((1, 1)   )   )
             self.assertIsNotNone(_lowerCamelCase   )







@require_flax
class 		_snake_case  (				unittest.TestCase							):




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,  from_pt=_lowerCamelCase   )

       a						:str										=	np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,  dtype=jnp.intaa   )
       a						:List[str]										=	model(_lowerCamelCase   )[0]
       a						:List[Any]										=	[1, 11, 5_0265]
       self.assertEqual(list(output.shape   ) ,  _lowerCamelCase   )
       # compare the actual values for a slice.
       a						:Union[str, Any]										=	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] ,  _lowerCamelCase ,  atol=1e-4   )   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,  from_pt=_lowerCamelCase   )

       a						:Tuple										=	np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,  dtype=jnp.intaa   )
       a						:Optional[int]										=	model(_lowerCamelCase   )[0]
       # compare the actual values for a slice.
       a						:Optional[Any]										=	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] ,  _lowerCamelCase ,  atol=1e-4   )   )









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





