


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )






class  __magic_name__							:






       def __init__(      self  ,							snake_case  ,							snake_case=1_3  ,							snake_case=7  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=9_9  ,							snake_case=3_2  ,							snake_case=2  ,							snake_case=4  ,							snake_case=3_7  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=1_6  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=3  ,							snake_case=4  ,							snake_case=None  ,							)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =parent
          _UpperCAmelCase       :       List[str]							   =1_3
          _UpperCAmelCase       :       Optional[Any]							   =7
          _UpperCAmelCase       :       str							   =True
          _UpperCAmelCase       :       Dict							   =True
          _UpperCAmelCase       :       Optional[int]							   =True
          _UpperCAmelCase       :       Any							   =True
          _UpperCAmelCase       :       Optional[int]							   =9_9
          _UpperCAmelCase       :       Dict							   =3_2
          _UpperCAmelCase       :       List[Any]							   =2
          _UpperCAmelCase       :       Dict							   =4
          _UpperCAmelCase       :       Any							   =3_7
          _UpperCAmelCase       :       Dict							   ='gelu'
          _UpperCAmelCase       :       str							   =0.1
          _UpperCAmelCase       :       List[Any]							   =0.1
          _UpperCAmelCase       :       Optional[int]							   =5_1_2
          _UpperCAmelCase       :       List[Any]							   =1_6
          _UpperCAmelCase       :       Optional[int]							   =2
          _UpperCAmelCase       :       int							   =0.02
          _UpperCAmelCase       :       Optional[int]							   =3
          _UpperCAmelCase       :       str							   =4
          _UpperCAmelCase       :       List[Any]							   =None






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

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

          _UpperCAmelCase       :       List[Any]							   =None
          if self.use_token_type_ids:
             _UpperCAmelCase       :       Union[str, Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size)

          _UpperCAmelCase       :       Union[str, Any]							   =None
          _UpperCAmelCase       :       Optional[int]							   =None
          _UpperCAmelCase       :       Tuple							   =None
          if self.use_labels:
             _UpperCAmelCase       :       Any							   =ids_tensor([self.batch_size]  ,							self.type_sequence_label_size)
             _UpperCAmelCase       :       Optional[Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.num_labels)
             _UpperCAmelCase       :       Any							   =ids_tensor([self.batch_size]  ,							self.num_choices)

          _UpperCAmelCase       :       Dict							   =RoFormerConfig(
              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  ,							initializer_range=self.initializer_range  ,							return_dict=__SCREAMING_SNAKE_CASE  ,							)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






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




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =TFRoFormerModel(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       List[Any]							   ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

          _UpperCAmelCase       :       Any							   =[input_ids, input_mask]
          _UpperCAmelCase       :       Any							   =model(__SCREAMING_SNAKE_CASE)

          _UpperCAmelCase       :       Optional[Any]							   =model(__SCREAMING_SNAKE_CASE)

          self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =True
          _UpperCAmelCase       :       Optional[Any]							   =TFRoFormerForCausalLM(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       List[Any]							   ={
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          _UpperCAmelCase       :       Dict							   =model(__SCREAMING_SNAKE_CASE)['logits']
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape)  ,							[self.batch_size, self.seq_length, self.vocab_size])






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =TFRoFormerForMaskedLM(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       int							   ={
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          _UpperCAmelCase       :       Dict							   =model(__SCREAMING_SNAKE_CASE)
          self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.num_labels
          _UpperCAmelCase       :       Union[str, Any]							   =TFRoFormerForSequenceClassification(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       int							   ={
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          _UpperCAmelCase       :       Union[str, Any]							   =model(__SCREAMING_SNAKE_CASE)
          self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_labels))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.num_choices
          _UpperCAmelCase       :       Dict							   =TFRoFormerForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       Optional[int]							   =tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE  ,							1)  ,							(1, self.num_choices, 1))
          _UpperCAmelCase       :       int							   =tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE  ,							1)  ,							(1, self.num_choices, 1))
          _UpperCAmelCase       :       Optional[int]							   =tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE  ,							1)  ,							(1, self.num_choices, 1))
          _UpperCAmelCase       :       List[Any]							   ={
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
              'token_type_ids': multiple_choice_token_type_ids,
          }
          _UpperCAmelCase       :       Optional[int]							   =model(__SCREAMING_SNAKE_CASE)
          self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_choices))






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.num_labels
          _UpperCAmelCase       :       Optional[Any]							   =TFRoFormerForTokenClassification(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       str							   ={
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          _UpperCAmelCase       :       List[Any]							   =model(__SCREAMING_SNAKE_CASE)
          self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.num_labels))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =TFRoFormerForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
          _UpperCAmelCase       :       Optional[Any]							   ={
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          _UpperCAmelCase       :       Optional[Any]							   =model(__SCREAMING_SNAKE_CASE)
          self.parent.assertEqual(result.start_logits.shape  ,							(self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape  ,							(self.batch_size, self.seq_length))






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =self.prepare_config_and_inputs()
          (
              (
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	(
              _UpperCAmelCase   
          )   ,	   
          )       :       Optional[int]							   =config_and_inputs
          _UpperCAmelCase       :       Any							   ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict






@require_tf
class  __magic_name__							(		_A ,_A ,unittest.TestCase    ):
       UpperCAmelCase										=(
           (
               TFRoFormerModel,
               TFRoFormerForCausalLM,
               TFRoFormerForMaskedLM,
               TFRoFormerForQuestionAnswering,
               TFRoFormerForSequenceClassification,
               TFRoFormerForTokenClassification,
               TFRoFormerForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase										=(
           {
               "feature-extraction": TFRoFormerModel,
               "fill-mask": TFRoFormerForMaskedLM,
               "question-answering": TFRoFormerForQuestionAnswering,
               "text-classification": TFRoFormerForSequenceClassification,
               "text-generation": TFRoFormerForCausalLM,
               "token-classification": TFRoFormerForTokenClassification,
               "zero-shot": TFRoFormerForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       UpperCAmelCase										=False
       UpperCAmelCase										=False






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Dict:




          '''simple docstring'''
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
             return True

          return False






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =TFRoFormerModelTester(self)
          _UpperCAmelCase       :       Union[str, Any]							   =ConfigTester(self  ,							config_class=__SCREAMING_SNAKE_CASE  ,							hidden_size=3_7)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          self.config_tester.run_common_tests()






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




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE)






       @slow
       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE)






@require_tf
class  __magic_name__							(		unittest.TestCase    ):






       @slow
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
          _UpperCAmelCase       :       Optional[int]							   =tf.constant([[0, 1, 2, 3, 4, 5]])
          _UpperCAmelCase       :       int							   =model(__SCREAMING_SNAKE_CASE)[0]

          # TODO Replace vocab size
          _UpperCAmelCase       :       List[str]							   =5_0_0_0_0

          _UpperCAmelCase       :       List[Any]							   =[1, 6, vocab_size]
          self.assertEqual(output.shape  ,							__SCREAMING_SNAKE_CASE)

          print(output[:, :3, :3])

          # TODO Replace values below with what was printed above.
          _UpperCAmelCase       :       List[Any]							   =tf.constant(
              [
                  [
                      [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                      [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                      [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
                  ]
              ])
          tf.debugging.assert_near(output[:, :3, :3]  ,							__SCREAMING_SNAKE_CASE  ,							atol=1E-4)






@require_tf
class  __magic_name__							(		unittest.TestCase    ):
       UpperCAmelCase										=1e-4






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =tf.constant([[4, 1_0]])
          _UpperCAmelCase       :       Union[str, Any]							   =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6  ,							embedding_dim=6)

          _UpperCAmelCase       :       List[str]							   =emba(input_ids.shape)
          _UpperCAmelCase       :       List[Any]							   =tf.constant(
              [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]])

          tf.debugging.assert_near(__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							atol=self.tolerance)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =tf.constant(
              [
                  [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
                  [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
                  [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
              ])
          _UpperCAmelCase       :       Union[str, Any]							   =TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2  ,							embedding_dim=5_1_2)
          emba([2, 1_6, 5_1_2])
          _UpperCAmelCase       :       List[Any]							   =emba.weight[:3, :5]

          tf.debugging.assert_near(__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							atol=self.tolerance)






@require_tf
class  __magic_name__							(		unittest.TestCase    ):
       UpperCAmelCase										=1e-4






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          # 2,12,16,64
          _UpperCAmelCase       :       List[Any]							   =tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4  ,							dtype=tf.floataa)  ,							shape=(2, 1_2, 1_6, 6_4)) / 1_0_0

          _UpperCAmelCase       :       Optional[Any]							   =-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4  ,							dtype=tf.floataa)  ,							shape=(2, 1_2, 1_6, 6_4)) / 1_0_0

          _UpperCAmelCase       :       int							   =TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2  ,							embedding_dim=6_4)
          _UpperCAmelCase       :       int							   =embed_positions([2, 1_6, 7_6_8])[None, None, :, :]

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              __SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE)

          _UpperCAmelCase       :       List[str]							   =tf.constant(
              [
                  [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
                  [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
                  [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
                  [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
                  [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
                  [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
              ])
          _UpperCAmelCase       :       Optional[int]							   =tf.constant(
              [
                  [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
                  [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
                  [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
                  [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
                  [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
                  [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
              ])

          tf.debugging.assert_near(query_layer[0, 0, :6, :8]  ,							__SCREAMING_SNAKE_CASE  ,							atol=self.tolerance)
          tf.debugging.assert_near(key_layer[0, 0, :6, :8]  ,							__SCREAMING_SNAKE_CASE  ,							atol=self.tolerance)





'''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  __magic_name__							(		unittest.TestCase    ):






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




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






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

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

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

          _UpperCAmelCase       :       Union[str, 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       lowerCAmelCase		(      self)						->  str:




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






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =config_and_inputs

          _UpperCAmelCase       :       Tuple							   =True
          _UpperCAmelCase       :       Any							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _UpperCAmelCase       :       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  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=True

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






       def       lowerCAmelCase		(      self)						->  List[str]:




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






       @slow
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for model_class_name in self.all_model_classes:
             _UpperCAmelCase       :       List[str]							   =model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)
             _UpperCAmelCase       :       Dict							   =model(np.ones((1, 1)))
             self.assertIsNotNone(snake_case)






@require_flax
class  __magic_name__							(		unittest.TestCase    ):






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       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)
          _UpperCAmelCase       :       str							   =model(snake_case)[0]
          _UpperCAmelCase       :       int							   =[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape)  ,							snake_case)
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =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]  ,							snake_case  ,							atol=1E-4))






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       List[str]							   =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]
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =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]  ,							snake_case  ,							atol=1E-4))

