





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     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=13					,							__magic_name__=7					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=99					,							__magic_name__=32					,							__magic_name__=5					,							__magic_name__=4					,							__magic_name__=37					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=16					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=4					,							)     ->   Union[str, Any]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



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

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

							snake_case_						:    List[str]				=  None
							if self.use_token_type_ids:
											snake_case_						:    List[str]				=  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=__magic_name__					,							initializer_range=self.initializer_range					,							)

							return config, input_ids, token_type_ids, attention_mask






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


							'''simple docstring'''



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






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


							'''simple docstring'''



							snake_case_						:    List[Any]				=  self.prepare_config_and_inputs()
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Union[str, Any]				=  config_and_inputs

							snake_case_						:    Any				=  True
							snake_case_						:    Dict				=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
							snake_case_						:    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 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      True

			lowerCamelCase_     :     str       			=      (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  FlaxRobertaPreLayerNormModelTester(self    )






			@slow
			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



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




@require_flax
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			@slow
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''					,							from_pt=__magic_name__    )

							snake_case_						:    Optional[Any]				=  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,							dtype=jnp.intaa    )
							snake_case_						:    Optional[int]				=  model(__magic_name__    )[0]
							snake_case_						:    Optional[Any]				=  [1, 11, 5_0265]
							self.assertEqual(list(output.shape    )					,							__magic_name__    )
							# compare the actual values for a slice.
							snake_case_						:    Dict				=  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]					,							__magic_name__					,							atol=1e-4    )    )






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


							'''simple docstring'''



							snake_case_						:    Any				=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''					,							from_pt=__magic_name__    )

							snake_case_						:    List[str]				=  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,							dtype=jnp.intaa    )
							snake_case_						:    int				=  model(__magic_name__    )[0]
							# compare the actual values for a slice.
							snake_case_						:    Any				=  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]					,							__magic_name__					,							atol=1e-4    )    )







def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return "\n".join(
				    f'''{number} * {i} = {number * i}''' for i in range(1 ,				number_of_terms + 1 ) )


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=1_0))
