



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




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  str											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)

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

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

      lowerCAmelCase__			:  Any											= 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=_a  ,initializer_range=self.initializer_range  ,)

      return config, input_ids, token_type_ids, attention_mask






  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Optional[int]											= self.prepare_config_and_inputs()
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  int											= config_and_inputs
      lowerCAmelCase__			:  Union[str, Any]											= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
      return config, inputs_dict







@require_flax
class 	lowerCAmelCase_(    __lowercase						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				True

  __lowercase							:				List[Any]           =				(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  str											= FlaxRoFormerModelTester(self				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      for model_class_name in self.all_model_classes:
          lowerCAmelCase__			:  Any											= model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""  ,from_pt=_a				)
          lowerCAmelCase__			:  str											= model(np.ones((1, 1)				)				)
          self.assertIsNotNone(_a				)







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




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  Optional[int]											= FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""				)
      lowerCAmelCase__			:  Union[str, Any]											= jnp.array([[0, 1, 2, 3, 4, 5]]				)
      lowerCAmelCase__			:  Optional[int]											= model(_a				)[0]

      lowerCAmelCase__			:  str											= 5_0000

      lowerCAmelCase__			:  Optional[Any]											= (1, 6, vocab_size)
      self.assertEqual(output.shape  ,_a				)

      lowerCAmelCase__			:  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]  ,_a  ,atol=1E-4				)				)







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if any(not isinstance(UpperCamelCase							,   UpperCamelCase      ) or x < 0 for x in sequence      ):
        raise TypeError("""Sequence must be list of non-negative integers"""      )
    for _ in range(len(UpperCamelCase      )      ):
        for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase							,   sequence[1:]      )      ):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
 assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
 assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]


