

"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=1_3 ,lowerCamelCase_=7 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=9_9 ,lowerCamelCase_=3_2 ,lowerCamelCase_=5 ,lowerCamelCase_=4 ,lowerCamelCase_=3_7 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=5_1_2 ,lowerCamelCase_=1_6 ,lowerCamelCase_=2 ,lowerCamelCase_=0.02 ,lowerCamelCase_=4 ,)    ->      Dict:
          A				  =		parent
          A				  =		batch_size
          A				  =		seq_length
          A				  =		is_training
          A				  =		use_attention_mask
          A				  =		use_token_type_ids
          A				  =		use_labels
          A				  =		vocab_size
          A				  =		hidden_size
          A				  =		num_hidden_layers
          A				  =		num_attention_heads
          A				  =		intermediate_size
          A				  =		hidden_act
          A				  =		hidden_dropout_prob
          A				  =		attention_probs_dropout_prob
          A				  =		max_position_embeddings
          A				  =		type_vocab_size
          A				  =		type_sequence_label_size
          A				  =		initializer_range
          A				  =		num_choices




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size    )

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

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

          A				  =		RoFormerConfig(
              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 UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.prepare_config_and_inputs()
          A  ,			A  ,			A  ,			A				  =		config_and_inputs
          A				  =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
          return config, inputs_dict





@require_flax
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   True

    _lowerCamelCase					=   (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		FlaxRoFormerModelTester(self    )




    @slow
    def UpperCamelCase__		( self    )    ->      Optional[int]:
          for model_class_name in self.all_model_classes:
                A				  =		model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" ,from_pt=lowerCamelCase_    )
                A				  =		model(np.ones((1, 1)    )    )
                self.assertIsNotNone(lowerCamelCase_    )





@require_flax
class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    @slow
    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""    )
          A				  =		jnp.array([[0, 1, 2, 3, 4, 5]]    )
          A				  =		model(lowerCamelCase_    )[0]

          A				  =		5_0_0_0_0

          A				  =		(1, 6, vocab_size)
          self.assertEqual(output.shape ,lowerCamelCase_    )

          A				  =		jnp.array(
              [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]    )

          self.assertTrue(jnp.allclose(output[:, :3, :3] ,lowerCamelCase_ ,atol=1E-4    )    )







"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




