



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase	       =	{
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase	       =	[
        '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''NezhaForNextSentencePrediction''',
        '''NezhaForMaskedLM''',
        '''NezhaForPreTraining''',
        '''NezhaForMultipleChoice''',
        '''NezhaForQuestionAnswering''',
        '''NezhaForSequenceClassification''',
        '''NezhaForTokenClassification''',
        '''NezhaModel''',
        '''NezhaPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nezha import (
            NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
            NezhaForMaskedLM,
            NezhaForMultipleChoice,
            NezhaForNextSentencePrediction,
            NezhaForPreTraining,
            NezhaForQuestionAnswering,
            NezhaForSequenceClassification,
            NezhaForTokenClassification,
            NezhaModel,
            NezhaPreTrainedModel,
        )


else:
    import sys

    __UpperCAmelCase	       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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

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

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

          UpperCamelCase   :  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=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

          return config, input_ids, token_type_ids, attention_mask
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     config_and_inputs
          UpperCamelCase   :  Union[str, Any]						     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     config_and_inputs

          UpperCamelCase   :  Dict						     =     True
          UpperCamelCase   :  List[Any]						     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
          UpperCamelCase   :  List[Any]						     =     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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  True

       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     FlaxRobertaPreLayerNormModelTester(self	)






       @slow
       def  snake_case_	(      self	)					->		List[str]:
          for model_class_name in self.all_model_classes:
             UpperCamelCase   :  List[str]						     =     model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[Any]						     =     model(np.ones((1, 1)	)	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Union[str, Any]						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          UpperCamelCase   :  List[Any]						     =     [1, 11, 5_0265]
          self.assertEqual(list(output.shape	),      SCREAMING_SNAKE_CASE_	)
          # compare the actual values for a slice.
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)






       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Dict						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          # compare the actual values for a slice.
          UpperCamelCase   :  Any						     =     np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)



