

'''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    a__  (							unittest.TestCase  ):


   def __init__( self			,				_UpperCamelCase			,				_UpperCamelCase=13			,				_UpperCamelCase=7			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=99			,				_UpperCamelCase=32			,				_UpperCamelCase=5			,				_UpperCamelCase=4			,				_UpperCamelCase=37			,				_UpperCamelCase="gelu"			,				_UpperCamelCase=0.1			,				_UpperCamelCase=0.1			,				_UpperCamelCase=512			,				_UpperCamelCase=16			,				_UpperCamelCase=2			,				_UpperCamelCase=0.0_2			,				_UpperCamelCase=4			,				):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      int			     =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size  )

      _lowercase			:      List[Any]			     =   None
      if self.use_attention_mask:
         _lowercase			:      Tuple			     =   random_attention_mask([self.batch_size, self.seq_length]  )

      _lowercase			:      List[Any]			     =   None
      if self.use_token_type_ids:
         _lowercase			:      str			     =   ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size  )

      _lowercase			:      Optional[int]			     =   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=_UpperCAmelCase			,				initializer_range=self.initializer_range			,				)

      return config, input_ids, token_type_ids, attention_mask






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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




@require_flax
class    a__  (							_UpperCAmelCase  ,      unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE    :     List[str]	       =							True

   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							(
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      str			     =   FlaxRoFormerModelTester(self  )






   @slow
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      for model_class_name in self.all_model_classes:
         _lowercase			:      Dict			     =   model_class_name.from_pretrained("junnyu/roformer_chinese_small"			,				from_pt=_UpperCAmelCase  )
         _lowercase			:      int			     =   model(np.ones((1, 1)  )  )
         self.assertIsNotNone(_UpperCAmelCase  )




@require_flax
class    a__  (							unittest.TestCase  ):


   @slow
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Any			     =   FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"  )
      _lowercase			:      Optional[int]			     =   jnp.array([[0, 1, 2, 3, 4, 5]]  )
      _lowercase			:      List[Any]			     =   model(_UpperCAmelCase  )[0]

      _lowercase			:      str			     =   50000

      _lowercase			:      Tuple			     =   (1, 6, vocab_size)
      self.assertEqual(output.shape			,				_UpperCAmelCase  )

      _lowercase			:      List[Any]			     =   jnp.array(
          [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]  )

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







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  bool:
   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[str]:
   lowercase__:   str					=      []
   lowercase__:   str					=      1_1
   lowercase__:   str					=      int('''1''' + '''0''' * digit_len )
   for num in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__UpperCAmelCase       ,    __UpperCAmelCase ):
               solutions.append(F"""{num}/{den}""" )
         den += 1
      num += 1
      lowercase__:   Dict					=      1_0
   return solutions



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 2 )  ->  int:
   lowercase__:   List[str]					=      1.0
   for fraction in fraction_list(__UpperCAmelCase ):
      lowercase__:   List[str]					=      Fraction(__UpperCAmelCase )
      result *= frac.denominator / frac.numerator
   return int(__UpperCAmelCase )


if __name__ == "__main__":
       print(solution())
