'''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       _a    (		unittest.TestCase      ):


  """simple docstring"""

  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       List[Any]=13   ,				lowercase_      :       Any=7   ,				lowercase_      :       Any=True   ,				lowercase_      :       str=True   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=True   ,				lowercase_      :       List[Any]=99   ,				lowercase_      :       Tuple=32   ,				lowercase_      :       List[str]=5   ,				lowercase_      :       List[Any]=4   ,				lowercase_      :       Optional[int]=37   ,				lowercase_      :       str="gelu"   ,				lowercase_      :       int=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       int=512   ,				lowercase_      :       str=16   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       Dict=0.0_2   ,				lowercase_      :       Optional[int]=4   ,				):







    '''simple docstring'''


    lowercase_     =       parent
    lowercase_     =       batch_size
    lowercase_     =       seq_length
    lowercase_     =       is_training
    lowercase_     =       use_attention_mask
    lowercase_     =       use_token_type_ids
    lowercase_     =       use_labels
    lowercase_     =       vocab_size
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       max_position_embeddings
    lowercase_     =       type_vocab_size
    lowercase_     =       type_sequence_label_size
    lowercase_     =       initializer_range
    lowercase_     =       num_choices




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size      )

    lowercase_     =       None
    if self.use_attention_mask:
      lowercase_     =       random_attention_mask([self.batch_size, self.seq_length]      )

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

    lowercase_     =       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 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.prepare_config_and_inputs()
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       config_and_inputs
    lowercase_     =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
    return config, inputs_dict





  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       self.prepare_config_and_inputs()
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       config_and_inputs

    lowercase_     =       True
    lowercase_     =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
    lowercase_     =       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       _a    (		__a ,				unittest.TestCase      ):


  """simple docstring"""

  A_    							=						True

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




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       FlaxRobertaPreLayerNormModelTester(self      )





  @slow
  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for model_class_name in self.all_model_classes:
      lowercase_     =       model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,				from_pt=lowercase_      )
      lowercase_     =       model(np.ones((1, 1)      )      )
      self.assertIsNotNone(lowercase_      )






@require_flax
class       _a    (		unittest.TestCase      ):


  """simple docstring"""

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







    '''simple docstring'''


    lowercase_     =       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,				from_pt=lowercase_      )

    lowercase_     =       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   ,				dtype=jnp.intaa      )
    lowercase_     =       model(lowercase_      )[0]
    lowercase_     =       [1, 11, 50_265]
    self.assertEqual(list(output.shape      )   ,				lowercase_      )
    # compare the actual values for a slice.
    lowercase_     =       np.array(
        [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,				dtype=np.floataa      )
    self.assertTrue(np.allclose(output[:, :3, :3]   ,				lowercase_   ,				atol=1e-4      )      )





  @slow
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    lowercase_     =       FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,				from_pt=lowercase_      )

    lowercase_     =       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   ,				dtype=jnp.intaa      )
    lowercase_     =       model(lowercase_      )[0]
    # compare the actual values for a slice.
    lowercase_     =       np.array(
        [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,				dtype=np.floataa      )
    self.assertTrue(np.allclose(output[:, :3, :3]   ,				lowercase_   ,				atol=1e-4      )      )


'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

