




'''simple docstring'''






class  UpperCAmelCase__						:



    def __init__(	self      :       int     ):
          _lowerCamelCase				:   dict[str, TrieNode]								=   {}  # Mapping from char to TrieNode
          _lowerCamelCase				:   Optional[int]								=   False



    def   lowerCamelCase_  (	self      :       str,__A      :       list[str]     ):
          for word in words:
                self.insert(__A     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       str     ):
          _lowerCamelCase				:   List[Any]								=   self
          for char in word:
                if char not in curr.nodes:
                      _lowerCamelCase				:   Tuple								=   TrieNode()
                _lowerCamelCase				:   Any								=   curr.nodes[char]
          _lowerCamelCase				:   List[str]								=   True



    def   lowerCamelCase_  (	self      :       Any,__A      :       str     ):
          _lowerCamelCase				:   Dict								=   self
          for char in word:
                if char not in curr.nodes:
                      return False
                _lowerCamelCase				:   Optional[int]								=   curr.nodes[char]
          return curr.is_leaf



    def   lowerCamelCase_  (	self      :       Any,__A      :       str     ):

          def _delete(__A      :       TrieNode,__A      :       str,__A      :       int     ) -> bool:
                if index == len(__A     ):
                      # If word does not exist
                      if not curr.is_leaf:
                            return False
                      _lowerCamelCase				:   int								=   False
                      return len(curr.nodes     ) == 0
                _lowerCamelCase				:   Dict								=   word[index]
                _lowerCamelCase				:   Optional[Any]								=   curr.nodes.get(__A     )
                # If char not in current trie node
                if not char_node:
                      return False
                # Flag to check if node can be deleted
                _lowerCamelCase				:   List[Any]								=   _delete(__A,__A,index + 1     )
                if delete_curr:
                      del curr.nodes[char]
                      return len(curr.nodes     ) == 0
                return delete_curr

          _delete(self,__A,0     )







def 		A_       (	_lowerCAmelCase   :    TrieNode				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      if node.is_leaf:
            print(_lowerCAmelCase				,	end=" "							)

      for key, value in node.nodes.items():
            print_words(_lowerCAmelCase				,	word + key							)







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   "banana bananas bandana band apple all beast".split()
      _lowerCamelCase				:   str								=   TrieNode()
      root.insert_many(_lowerCAmelCase							)
      # print_words(root, "")
      assert all(root.find(_lowerCAmelCase							) for word in words							)
      assert root.find("banana"							)
      assert not root.find("bandanas"							)
      assert not root.find("apps"							)
      assert root.find("apple"							)
      assert root.find("all"							)
      root.delete("all"							)
      assert not root.find("all"							)
      root.delete("banana"							)
      assert not root.find("banana"							)
      assert root.find("bananas"							)
      return True







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    bool							):



      """simple docstring"""




      print(str(_lowerCAmelCase							)				,	"works!" if passes else "doesn't work :("							)







def 		A_       (	):



      """simple docstring"""




      assert test_trie()







def 		A_       (	):



      """simple docstring"""




      print_results("Testing trie functionality"				,	test_trie()							)


if __name__ == "__main__":
 main()

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, 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():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

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

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

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              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   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   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
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )