






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 						_UpperCAmelCase	(	unittest.TestCase		):




      """simple docstring"""



      def __init__( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase=13     ,						_lowerCAmelCase=7     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=99     ,						_lowerCAmelCase=32     ,						_lowerCAmelCase=5     ,						_lowerCAmelCase=4     ,						_lowerCAmelCase=37     ,						_lowerCAmelCase="gelu"     ,						_lowerCAmelCase=0.1     ,						_lowerCAmelCase=0.1     ,						_lowerCAmelCase=512     ,						_lowerCAmelCase=16     ,						_lowerCAmelCase=2     ,						_lowerCAmelCase=0.02     ,						_lowerCAmelCase=4     ,						):



          '''simple docstring'''
          lowerCAmelCase__ :Union[str, Any]   				=					parent
          lowerCAmelCase__ :Tuple   				=					batch_size
          lowerCAmelCase__ :List[Any]   				=					seq_length
          lowerCAmelCase__ :Optional[int]   				=					is_training
          lowerCAmelCase__ :Any   				=					use_attention_mask
          lowerCAmelCase__ :Dict   				=					use_token_type_ids
          lowerCAmelCase__ :str   				=					use_labels
          lowerCAmelCase__ :List[Any]   				=					vocab_size
          lowerCAmelCase__ :Tuple   				=					hidden_size
          lowerCAmelCase__ :Union[str, Any]   				=					num_hidden_layers
          lowerCAmelCase__ :List[Any]   				=					num_attention_heads
          lowerCAmelCase__ :Tuple   				=					intermediate_size
          lowerCAmelCase__ :Tuple   				=					hidden_act
          lowerCAmelCase__ :List[str]   				=					hidden_dropout_prob
          lowerCAmelCase__ :Dict   				=					attention_probs_dropout_prob
          lowerCAmelCase__ :Optional[int]   				=					max_position_embeddings
          lowerCAmelCase__ :str   				=					type_vocab_size
          lowerCAmelCase__ :Union[str, Any]   				=					type_sequence_label_size
          lowerCAmelCase__ :Any   				=					initializer_range
          lowerCAmelCase__ :Optional[int]   				=					num_choices
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[Any]   				=					ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size		)

          lowerCAmelCase__ :Optional[int]   				=					None
          if self.use_attention_mask:
              lowerCAmelCase__ :Optional[Any]   				=					random_attention_mask([self.batch_size, self.seq_length]		)

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

          lowerCAmelCase__ :Dict   				=					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       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Union[str, Any]   				=					self.prepare_config_and_inputs()
          lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__ :Any   				=					config_and_inputs
          lowerCAmelCase__ :Dict   				=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Dict   				=					self.prepare_config_and_inputs()
          lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__ :List[Any]   				=					config_and_inputs

          lowerCAmelCase__ :Dict   				=					True
          lowerCAmelCase__ :Optional[Any]   				=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
          lowerCAmelCase__ :List[str]   				=					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 						_UpperCAmelCase	(	A__					,				unittest.TestCase		):




      """simple docstring"""



      A    =     True

      A    =     (
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					FlaxRobertaPreLayerNormModelTester(self		)



      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          for model_class_name in self.all_model_classes:
              lowerCAmelCase__ :Any   				=					model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,						from_pt=a__		)
              lowerCAmelCase__ :Optional[int]   				=					model(np.ones((1, 1)		)		)
              self.assertIsNotNone(a__		)







@require_flax
class 						_UpperCAmelCase	(	unittest.TestCase		):




      """simple docstring"""



      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[Any]   				=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,						from_pt=a__		)

          lowerCAmelCase__ :List[Any]   				=					np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,						dtype=jnp.intaa		)
          lowerCAmelCase__ :int   				=					model(a__		)[0]
          lowerCAmelCase__ :Optional[int]   				=					[1, 11, 50_265]
          self.assertEqual(list(output.shape		)     ,						a__		)
          # compare the actual values for a slice.
          lowerCAmelCase__ :List[Any]   				=					np.array(
              [[[40.4_880, 18.0_199, -5.2367], [-1.8877, -4.0885, 10.7_085], [-2.2613, -5.6110, 7.2665]]]     ,						dtype=np.floataa		)
          self.assertTrue(np.allclose(output[:, :3, :3]     ,						a__     ,						atol=1e-4		)		)



      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :List[Any]   				=					FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,						from_pt=a__		)

          lowerCAmelCase__ :Union[str, Any]   				=					np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,						dtype=jnp.intaa		)
          lowerCAmelCase__ :Dict   				=					model(a__		)[0]
          # compare the actual values for a slice.
          lowerCAmelCase__ :Tuple   				=					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]     ,						a__     ,						atol=1e-4		)		)








import re



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	str:

    if len(re.findall('''[ATCG]'''			,							UpperCamelCase_						)						) != len(UpperCamelCase_						):
        raise ValueError('''Invalid Strand'''						)

    return dna.translate(dna.maketrans('''ATCG'''			,							'''TAGC'''						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
