



"""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  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






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







								'''simple docstring'''

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





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







								'''simple docstring'''

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

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

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

								snake_case_ :			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=_lowercase				,     initializer_range=self.initializer_range				,     )

								return config, input_ids, token_type_ids, attention_mask





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			str            =      self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_ :			List[Any]            =      config_and_inputs
								snake_case_ :			Any            =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict





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







								'''simple docstring'''

								snake_case_ :			Tuple            =      self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_ :			Optional[int]            =      config_and_inputs

								snake_case_ :			Union[str, Any]            =      True
								snake_case_ :			Optional[Any]            =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
								snake_case_ :			int            =      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     (					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							True

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





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      FlaxRobertaPreLayerNormModelTester(self )





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







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_ :			Optional[int]            =      model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				,     from_pt=_lowercase )
														snake_case_ :			List[str]            =      model(np.ones((1, 1) ) )
														self.assertIsNotNone(_lowercase )




@require_flax
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@slow
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				,     from_pt=_lowercase )

								snake_case_ :			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 )
								snake_case_ :			Dict            =      model(_lowercase )[0]
								snake_case_ :			Optional[Any]            =      [1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape )				,     _lowercase )
								# compare the actual values for a slice.
								snake_case_ :			Tuple            =      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]				,     _lowercase				,     atol=1E-4 ) )





		@slow
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				,     from_pt=_lowercase )

								snake_case_ :			Union[str, Any]            =      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_ :			Dict            =      model(_lowercase )[0]
								# compare the actual values for a slice.
								snake_case_ :			List[str]            =      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]				,     _lowercase				,     atol=1E-4 ) )








"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
