






"""simple docstring"""





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 ):

      '''simple docstring'''



      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any						,				__lowercase       :      List[str]=13						,				__lowercase       :      Dict=7						,				__lowercase       :      Any=True						,				__lowercase       :      Union[str, Any]=True						,				__lowercase       :      Union[str, Any]=True						,				__lowercase       :      Union[str, Any]=True						,				__lowercase       :      Optional[Any]=99						,				__lowercase       :      Optional[int]=32						,				__lowercase       :      int=5						,				__lowercase       :      List[str]=4						,				__lowercase       :      List[str]=37						,				__lowercase       :      Optional[Any]="gelu"						,				__lowercase       :      Dict=0.1						,				__lowercase       :      List[Any]=0.1						,				__lowercase       :      Optional[Any]=512						,				__lowercase       :      Optional[Any]=16						,				__lowercase       :      List[str]=2						,				__lowercase       :      str=0.0_2						,				__lowercase       :      int=4						,				):


        '''simple docstring'''




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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						List[str]       =						None
        if self.use_attention_mask:
          __UpperCAmelCase    :						Tuple       =						random_attention_mask([self.batch_size, self.seq_length] )

        __UpperCAmelCase    :						int       =						None
        if self.use_token_type_ids:
          __UpperCAmelCase    :						List[str]       =						ids_tensor([self.batch_size, self.seq_length]						,				self.type_vocab_size )

        __UpperCAmelCase    :						Tuple       =						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       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.prepare_config_and_inputs()
        __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						config_and_inputs
        __UpperCAmelCase    :						str       =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.prepare_config_and_inputs()
        __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						config_and_inputs

        __UpperCAmelCase    :						List[Any]       =						True
        __UpperCAmelCase    :						Optional[int]       =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
        __UpperCAmelCase    :						Dict       =						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							(			_lowerCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       int         =       True

      _A     :       Tuple         =       (
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						FlaxRobertaPreLayerNormModelTester(self )







      @slow
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        for model_class_name in self.all_model_classes:
          __UpperCAmelCase    :						Union[str, Any]       =						model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						,				from_pt=_lowerCAmelCase )
          __UpperCAmelCase    :						List[Any]       =						model(np.ones((1, 1) ) )
          self.assertIsNotNone(_lowerCAmelCase )




@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      @slow
      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						,				from_pt=_lowerCAmelCase )

        __UpperCAmelCase    :						Optional[int]       =						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,				dtype=jnp.intaa )
        __UpperCAmelCase    :						Optional[int]       =						model(_lowerCAmelCase )[0]
        __UpperCAmelCase    :						Tuple       =						[1, 11, 50_265]
        self.assertEqual(list(output.shape )						,				_lowerCAmelCase )
        # compare the actual values for a slice.
        __UpperCAmelCase    :						Dict       =						np.array(
            [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]						,				dtype=np.floataa )
        self.assertTrue(np.allclose(output[:, :3, :3]						,				_lowerCAmelCase						,				atol=1e-4 ) )







      @slow
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						,				from_pt=_lowerCAmelCase )

        __UpperCAmelCase    :						int       =						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,				dtype=jnp.intaa )
        __UpperCAmelCase    :						Dict       =						model(_lowerCAmelCase )[0]
        # compare the actual values for a slice.
        __UpperCAmelCase    :						str       =						np.array(
            [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]						,				dtype=np.floataa )
        self.assertTrue(np.allclose(output[:, :3, :3]						,				_lowerCAmelCase						,				atol=1e-4 ) )






"""simple docstring"""





# flake8: noqa
# Lint as: python3
lowercase__				:Tuple				   =       [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental