





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







        '''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 		__snake_case						(      self :  Optional[int]):







        '''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__					     = 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 		__snake_case						(      self :  Tuple):







        '''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




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



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

        lowerCAmelCase__					     = True
        lowerCAmelCase__					     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        lowerCAmelCase__					     = 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_			(						SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= True

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




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaxRobertaPreLayerNormModelTester(self)




 @slow
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               lowerCAmelCase__					     = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=lowerCamelCase_)
               lowerCAmelCase__					     = model(np.ones((1, 1)))
               self.assertIsNotNone(lowerCamelCase_)






@require_flax
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 @slow
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=lowerCamelCase_)

        lowerCAmelCase__					     = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,   dtype=jnp.intaa)
        lowerCAmelCase__					     = model(lowerCamelCase_)[0]
        lowerCAmelCase__					     = [1, 11, 50_265]
        self.assertEqual(list(output.shape)      ,   lowerCamelCase_)
        # compare the actual values for a slice.
        lowerCAmelCase__					     = np.array(
            [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]      ,   dtype=np.floataa)
        self.assertTrue(np.allclose(output[:, :3, :3]      ,   lowerCamelCase_      ,   atol=1e-4))




 @slow
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=lowerCamelCase_)

        lowerCAmelCase__					     = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,   dtype=jnp.intaa)
        lowerCAmelCase__					     = model(lowerCamelCase_)[0]
        # compare the actual values for a slice.
        lowerCAmelCase__					     = np.array(
            [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]      ,   dtype=np.floataa)
        self.assertTrue(np.allclose(output[:, :3, :3]      ,   lowerCamelCase_      ,   atol=1e-4))

from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  pyspark.sql.DataFrame      ,   lowercase__ :  Optional[NamedSplit] = None      ,   lowercase__ :  Optional[Features] = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = "arrow"      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



        super().__init__(
            split=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   keep_in_memory=lowercase__      ,   streaming=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = load_from_cache_file
        lowerCAmelCase__					     = file_format
        lowerCAmelCase__					     = Spark(
            df=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   working_dir=lowercase__      ,   **lowercase__      ,   )




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        if self.streaming:
               return self.builder.as_streaming_dataset(split=self.split)
        lowerCAmelCase__					     = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowercase__      ,   file_format=self._file_format      ,   )
        return self.builder.as_dataset(split=self.split)
