






'''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 a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=1_3					,   __a=7					,   __a=True					,   __a=True					,   __a=True					,   __a=True					,   __a=9_9					,   __a=3_2					,   __a=5					,   __a=4					,   __a=3_7					,   __a="gelu"					,   __a=0.1					,   __a=0.1					,   __a=5_1_2					,   __a=1_6					,   __a=2					,   __a=0.02					,   __a=4					,   )			->      Dict:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size)

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

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

      __snake_case				:      List[str]     							= 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=__a					,   initializer_range=self.initializer_range					,   )

      return config, input_ids, token_type_ids, attention_mask







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      List[str]     							= self.prepare_config_and_inputs()
      __snake_case				:      Tuple     							= config_and_inputs
      __snake_case				:      Union[str, Any]     							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= self.prepare_config_and_inputs()
      __snake_case				:      int     							= config_and_inputs

      __snake_case				:      int     							= True
      __snake_case				:      Union[str, Any]     							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case				:      Optional[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 a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							True

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







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      str     							= FlaxRobertaPreLayerNormModelTester(self)







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






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




@require_flax
class a_    (    unittest.TestCase							):







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      List[Any]     							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=__a)

      __snake_case				:      Optional[int]     							= np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,   dtype=jnp.intaa)
      __snake_case				:      List[str]     							= model(__a)[0]
      __snake_case				:      Dict     							= [1, 1_1, 5_0_2_6_5]
      self.assertEqual(list(output.shape)					,   __a)
      # compare the actual values for a slice.
      __snake_case				:      Optional[Any]     							= 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]					,   __a					,   atol=1E-4))







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,   from_pt=__a)

      __snake_case				:      Dict     							= np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,   dtype=jnp.intaa)
      __snake_case				:      List[str]     							= model(__a)[0]
      # compare the actual values for a slice.
      __snake_case				:      Tuple     							= 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]					,   __a					,   atol=1E-4))







'''simple docstring'''






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   if not is_accelerate_available():
      return method
   __snake_case				:      Optional[Any]     							= version.parse(accelerate.__version__  ).base_version
   if version.parse(A  ) < version.parse('0.17.0'  ):
      return method

   def wrapper(self  :       Optional[Any]  ,		*A  :       Optional[Any]  ,		**A  :       Optional[int]  ):
      if hasattr(self  ,		'_hf_hook'  ) and hasattr(self._hf_hook  ,		'pre_forward'  ):
         self._hf_hook.pre_forward(self  )
      return method(self  ,		*A  ,		**A  )

   return wrapper