



"""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 	__snake_case	(     unittest.TestCase):




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






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size		)

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

         _lowerCamelCase						:    str         =			None
         if self.use_token_type_ids:
                _lowerCamelCase						:    Optional[int]         =			ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size		)

         _lowerCamelCase						:    Optional[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=__lowerCAmelCase						, initializer_range=self.initializer_range						, )

         return config, input_ids, token_type_ids, attention_mask




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			self.prepare_config_and_inputs()
         _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Optional[Any]         =			config_and_inputs
         _lowerCamelCase						:    str         =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			self.prepare_config_and_inputs()
         _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			config_and_inputs

         _lowerCamelCase						:    List[str]         =			True
         _lowerCamelCase						:    Optional[Any]         =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
         _lowerCamelCase						:    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 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						True

  snake_case__    :       int        =						(
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			FlaxRobertaPreLayerNormModelTester(self		)




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         for model_class_name in self.all_model_classes:
                _lowerCamelCase						:    int         =			model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						, from_pt=__lowerCAmelCase		)
                _lowerCamelCase						:    Dict         =			model(np.ones((1, 1)		)		)
                self.assertIsNotNone(__lowerCAmelCase		)






@require_flax
class 	__snake_case	(     unittest.TestCase):




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						, from_pt=__lowerCAmelCase		)

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




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''						, from_pt=__lowerCAmelCase		)

         _lowerCamelCase						:    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		)
         _lowerCamelCase						:    Optional[Any]         =			model(__lowerCAmelCase		)[0]
         # compare the actual values for a slice.
         _lowerCamelCase						:    Optional[Any]         =			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]						, __lowerCAmelCase						, atol=1E-4		)		)







"""simple docstring"""






from maths.prime_factors import prime_factors






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       if not isinstance(A_,    A_						):
              _lowerCamelCase						:    str         =			F'''Input value of [number={number}] must be an integer'''
              raise TypeError(A_						)
       if number < 1:
              raise ValueError('''Input must be a positive integer'''						)
       return -1 if len(prime_factors(A_						)						) % 2 else 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()


