





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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



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





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Tuple        =      ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size	)

        A_       :					Any        =      None
        if self.use_attention_mask:
           A_       :					Dict        =      random_attention_mask([self.batch_size, self.seq_length]	)

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

        A_       :					List[Any]        =      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=snake_case      ,   initializer_range=self.initializer_range      ,   )

        return config, input_ids, token_type_ids, attention_mask



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Dict        =      self.prepare_config_and_inputs()
        A_   ,    A_   ,    A_   ,    A_       :					Tuple        =      config_and_inputs
        A_       :					Dict        =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        A_       :					str        =      self.prepare_config_and_inputs()
        A_   ,    A_   ,    A_   ,    A_       :					List[str]        =      config_and_inputs

        A_       :					int        =      True
        A_       :					List[str]        =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
        A_       :					Tuple        =      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        __magic_name__	(				lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  True

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



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Dict        =      FlaxRobertaPreLayerNormModelTester(self	)





     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_       :					Optional[int]        =      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,   from_pt=snake_case	)
           A_       :					Optional[Any]        =      model(np.ones((1, 1)	)	)
           self.assertIsNotNone(snake_case	)




@require_flax
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					List[Any]        =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,   from_pt=snake_case	)

        A_       :					Any        =      np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,   dtype=jnp.intaa	)
        A_       :					str        =      model(snake_case	)[0]
        A_       :					Union[str, Any]        =      [1, 11, 50_265]
        self.assertEqual(list(output.shape	)      ,   snake_case	)
        # compare the actual values for a slice.
        A_       :					int        =      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]      ,   snake_case      ,   atol=1e-4	)	)





     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        A_       :					Optional[int]        =      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,   from_pt=snake_case	)

        A_       :					Optional[int]        =      np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,   dtype=jnp.intaa	)
        A_       :					int        =      model(snake_case	)[0]
        # compare the actual values for a slice.
        A_       :					str        =      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]      ,   snake_case      ,   atol=1e-4	)	)







import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
