






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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	List[str]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[str, Any]=13					, UpperCAmelCase_       :	Union[str, Any]=7					, UpperCAmelCase_       :	List[str]=True					, UpperCAmelCase_       :	Dict=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	str=True					, UpperCAmelCase_       :	List[str]=99					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Tuple=5					, UpperCAmelCase_       :	Dict=4					, UpperCAmelCase_       :	Optional[Any]=37					, UpperCAmelCase_       :	Union[str, Any]="gelu"					, UpperCAmelCase_       :	int=0.1					, UpperCAmelCase_       :	Optional[Any]=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	List[Any]=16					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	Union[str, Any]=0.02					, UpperCAmelCase_       :	Dict=4					, ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    parent
       SCREAMING_SNAKE_CASE       :      List[str]							=    batch_size
       SCREAMING_SNAKE_CASE       :      str							=    seq_length
       SCREAMING_SNAKE_CASE       :      Tuple							=    is_training
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    use_attention_mask
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    use_token_type_ids
       SCREAMING_SNAKE_CASE       :      Dict							=    use_labels
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    vocab_size
       SCREAMING_SNAKE_CASE       :      int							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      str							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    type_sequence_label_size
       SCREAMING_SNAKE_CASE       :      List[str]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Dict							=    num_choices


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size      )

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

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

       SCREAMING_SNAKE_CASE       :      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=UpperCAmelCase_					, initializer_range=self.initializer_range					, )

       return config, input_ids, token_type_ids, attention_mask


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      Any							=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict






     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    config_and_inputs

       SCREAMING_SNAKE_CASE       :      Dict							=    True
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
       SCREAMING_SNAKE_CASE       :      Dict							=    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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	True

     UpperCamelCase_							:		Optional[int]			=	(
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    FlaxRobertaPreLayerNormModelTester(self      )






     @slow
     def 				_A       (				self       :	Union[str, Any]      ):
       for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      List[str]							=    model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					, from_pt=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(np.ones((1, 1)      )      )
         self.assertIsNotNone(UpperCAmelCase_      )





@require_flax
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @slow
     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					, from_pt=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					, dtype=jnp.intaa      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(UpperCAmelCase_      )[0]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [1, 11, 5_0265]
       self.assertEqual(list(output.shape      )					, UpperCAmelCase_      )
       # compare the actual values for a slice.
       SCREAMING_SNAKE_CASE       :      Any							=    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]					, UpperCAmelCase_					, atol=1E-4      )      )






     @slow
     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					, from_pt=UpperCAmelCase_      )

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














from __future__ import annotations

from cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






