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 		_lowercase						(					unittest.TestCase       ):

 def __init__( self	:							str   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Any=1_3   ,      snake_case	:							Optional[int]=7   ,      snake_case	:							Tuple=True   ,      snake_case	:							Any=True   ,      snake_case	:							Dict=True   ,      snake_case	:							int=True   ,      snake_case	:							Tuple=9_9   ,      snake_case	:							Optional[int]=3_2   ,      snake_case	:							int=5   ,      snake_case	:							Optional[Any]=4   ,      snake_case	:							Any=3_7   ,      snake_case	:							Union[str, Any]="gelu"   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							Any=0.1   ,      snake_case	:							List[Any]=5_1_2   ,      snake_case	:							int=1_6   ,      snake_case	:							Optional[Any]=2   ,      snake_case	:							Any=0.02   ,      snake_case	:							Dict=4   ,      )   ->						Optional[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

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

  UpperCamelCase_					:						Dict =					None
  if self.use_token_type_ids:
   UpperCamelCase_					:						Dict =					ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size			)

  UpperCamelCase_					:						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	:							Dict			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.prepare_config_and_inputs()
  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Tuple =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.prepare_config_and_inputs()
  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Tuple =					config_and_inputs

  UpperCamelCase_					:						List[str] =					True
  UpperCamelCase_					:						Union[str, Any] =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
  UpperCamelCase_					:						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 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   True

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					FlaxRobertaPreLayerNormModelTester(self			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Union[str, Any]:

  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCamelCase_					:						Any =					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,      from_pt=snake_case			)
   UpperCamelCase_					:						str =					model(np.ones((1, 1)			)			)
   self.assertIsNotNone(snake_case			)





@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,      from_pt=snake_case			)

  UpperCamelCase_					:						Tuple =					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			)
  UpperCamelCase_					:						Tuple =					model(snake_case			)[0]
  UpperCamelCase_					:						str =					[1, 1_1, 5_0_2_6_5]
  self.assertEqual(list(output.shape			)   ,      snake_case			)
  # compare the actual values for a slice.
  UpperCamelCase_					:						Any =					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	:							int			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,      from_pt=snake_case			)

  UpperCamelCase_					:						Tuple =					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			)
  UpperCamelCase_					:						Optional[Any] =					model(snake_case			)[0]
  # compare the actual values for a slice.
  UpperCamelCase_					:						Any =					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			)			)




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



