


import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       NystromformerForMaskedLM,
       NystromformerForMultipleChoice,
       NystromformerForQuestionAnswering,
       NystromformerForSequenceClassification,
       NystromformerForTokenClassification,
       NystromformerModel,
   )
   from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST



class        a					:





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=13    ,	SCREAMING_SNAKE_CASE_					:				Any=7    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				List[str]=99    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=32    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=5    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=4    ,	SCREAMING_SNAKE_CASE_					:				List[str]=37    ,	SCREAMING_SNAKE_CASE_					:				str="gelu"    ,	SCREAMING_SNAKE_CASE_					:				Any=0.1    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=512    ,	SCREAMING_SNAKE_CASE_					:				Any=16    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.02    ,	SCREAMING_SNAKE_CASE_					:				int=3    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	):
        __lowerCamelCase:				Tuple     =	parent
        __lowerCamelCase:				Dict     =	batch_size
        __lowerCamelCase:				List[str]     =	seq_length
        __lowerCamelCase:				str     =	is_training
        __lowerCamelCase:				Optional[Any]     =	use_input_mask
        __lowerCamelCase:				Optional[Any]     =	use_token_type_ids
        __lowerCamelCase:				int     =	use_labels
        __lowerCamelCase:				Optional[Any]     =	vocab_size
        __lowerCamelCase:				Tuple     =	hidden_size
        __lowerCamelCase:				int     =	num_hidden_layers
        __lowerCamelCase:				int     =	num_attention_heads
        __lowerCamelCase:				Optional[Any]     =	intermediate_size
        __lowerCamelCase:				List[str]     =	hidden_act
        __lowerCamelCase:				int     =	hidden_dropout_prob
        __lowerCamelCase:				List[Any]     =	attention_probs_dropout_prob
        __lowerCamelCase:				int     =	max_position_embeddings
        __lowerCamelCase:				Tuple     =	type_vocab_size
        __lowerCamelCase:				str     =	type_sequence_label_size
        __lowerCamelCase:				Dict     =	initializer_range
        __lowerCamelCase:				Tuple     =	num_labels
        __lowerCamelCase:				List[Any]     =	num_choices
        __lowerCamelCase:				int     =	scope





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				List[Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				str     =	None
        if self.use_input_mask:
           __lowerCamelCase:				Tuple     =	random_attention_mask([self.batch_size, self.seq_length]							)

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

        __lowerCamelCase:				Union[str, Any]     =	None
        __lowerCamelCase:				List[Any]     =	None
        __lowerCamelCase:				int     =	None
        if self.use_labels:
           __lowerCamelCase:				int     =	ids_tensor([self.batch_size]    ,	self.type_sequence_label_size							)
           __lowerCamelCase:				Dict     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels							)
           __lowerCamelCase:				Dict     =	ids_tensor([self.batch_size]    ,	self.num_choices							)

        __lowerCamelCase:				str     =	self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return NystromformerConfig(
            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=SCREAMING_SNAKE_CASE_    ,	initializer_range=self.initializer_range    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				List[str]     =	NystromformerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Tuple     =	NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				List[Any]     =	NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Union[str, Any]     =	model(
            SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	start_positions=SCREAMING_SNAKE_CASE_    ,	end_positions=SCREAMING_SNAKE_CASE_    ,	)
        self.parent.assertEqual(result.start_logits.shape    ,	(self.batch_size, self.seq_length)							)
        self.parent.assertEqual(result.end_logits.shape    ,	(self.batch_size, self.seq_length)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        __lowerCamelCase:				Optional[Any]     =	self.num_labels
        __lowerCamelCase:				int     =	NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Optional[Any]     =	self.num_labels
        __lowerCamelCase:				List[str]     =	NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Union[str, Any]     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.num_labels)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Union[str, Any]     =	self.num_choices
        __lowerCamelCase:				str     =	NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Any     =	input_ids.unsqueeze(1							).expand(-1    ,	self.num_choices    ,	-1							).contiguous()
        __lowerCamelCase:				Any     =	token_type_ids.unsqueeze(1							).expand(-1    ,	self.num_choices    ,	-1							).contiguous()
        __lowerCamelCase:				List[Any]     =	input_mask.unsqueeze(1							).expand(-1    ,	self.num_choices    ,	-1							).contiguous()
        __lowerCamelCase:				List[str]     =	model(
            SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	token_type_ids=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_    ,	)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_choices)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				List[Any]     =	self.prepare_config_and_inputs()
        (
            (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     (
            __lowerCamelCase
        )   ,     
        ):				Union[str, Any]     =	config_and_inputs
        __lowerCamelCase:				str     =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	(
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__ :  int						      =	(
         {
             "feature-extraction": NystromformerModel,
             "fill-mask": NystromformerForMaskedLM,
             "question-answering": NystromformerForQuestionAnswering,
             "text-classification": NystromformerForSequenceClassification,
             "token-classification": NystromformerForTokenClassification,
             "zero-shot": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__ :  Tuple						      =	False
     UpperCAmelCase__ :  Tuple						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Any     =	NystromformerModelTester(self							)
        __lowerCamelCase:				Any     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	hidden_size=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           __lowerCamelCase:				int     =	type
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Tuple     =	NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



@require_torch
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Optional[int]     =	NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""							)
        __lowerCamelCase:				Optional[Any]     =	torch.tensor([[0, 1, 2, 3, 4, 5]]							)

        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				str     =	torch.Size((1, 6, 768)							)
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
            [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]							)

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







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""the [MASK] of Belgium is Brussels"""

        __lowerCamelCase:				Optional[Any]     =	AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""							)
        __lowerCamelCase:				Optional[int]     =	NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""							)

        __lowerCamelCase:				str     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							)

        with torch.no_grad():
           __lowerCamelCase:				Any     =	model(encoding.input_ids							).logits

        __lowerCamelCase:				Any     =	token_logits[:, 2, :].argmax(-1							)[0]

        self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_							)    ,	"""capital"""							)






class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str = ""    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        # Mapping from the first character of the prefix of the node
        __lowerCamelCase:				dict[str, RadixNode]     =	{}

        # A node will be a leaf if the tree contains its word
        __lowerCamelCase:				str     =	is_leaf

        __lowerCamelCase:				Optional[int]     =	prefix





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Optional[Any]     =	0
        for q, w in zip(self.prefix    ,	SCREAMING_SNAKE_CASE_							):
           if q != w:
              break

           x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				list[str]							):
        for word in words:
           self.insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
           __lowerCamelCase:				Union[str, Any]     =	True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
           __lowerCamelCase:				Any     =	RadixNode(prefix=SCREAMING_SNAKE_CASE_    ,	is_leaf=SCREAMING_SNAKE_CASE_							)

        else:
           __lowerCamelCase:				Union[str, Any]     =	self.nodes[word[0]]
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
              self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
              __lowerCamelCase:				List[Any]     =	remaining_prefix

              __lowerCamelCase:				Optional[Any]     =	self.nodes[matching_string[0]]
              __lowerCamelCase:				Optional[int]     =	RadixNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Union[str, Any]     =	aux_node

              if remaining_word == "":
                 __lowerCamelCase:				Optional[int]     =	True
              else:
                 self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				int     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
              return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
              return incoming_node.find(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				str     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
              return incoming_node.delete(SCREAMING_SNAKE_CASE_							)
           else:
              # If it is not a leaf, we don't have to delete
              if not incoming_node.is_leaf:
                 return False
              else:
                 # We delete the nodes if no edges go from it
                 if len(incoming_node.nodes							) == 0:
                    del self.nodes[word[0]]
                    # We merge the current node with its only child
                    if len(self.nodes							) == 1 and not self.is_leaf:
                       __lowerCamelCase:				List[Any]     =	list(self.nodes.values()							)[0]
                       __lowerCamelCase:				Any     =	merging_node.is_leaf
                       self.prefix += merging_node.prefix
                       __lowerCamelCase:				Tuple     =	merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                 elif len(incoming_node.nodes							) > 1:
                    __lowerCamelCase:				int     =	False
                 # If there is 1 edge, we merge it with its child
                 else:
                    __lowerCamelCase:				Union[str, Any]     =	list(incoming_node.nodes.values()							)[0]
                    __lowerCamelCase:				List[str]     =	merging_node.is_leaf
                    incoming_node.prefix += merging_node.prefix
                    __lowerCamelCase:				Union[str, Any]     =	merging_node.nodes

                 return True







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int = 0							):
        if self.prefix != "":
           print("""-""" * height    ,	self.prefix    ,	"""  (leaf)""" if self.is_leaf else """"""							)

        for value in self.nodes.values():
           value.print_tree(height + 1							)



def     __lowerCAmelCase						(							)       ->							bool:
   __lowerCamelCase:				Optional[int]     =	"""banana bananas bandana band apple all beast""".split()
   __lowerCamelCase:				Optional[Any]     =	RadixNode()
   root.insert_many(snake_case )

   assert all(root.find(snake_case ) for word in words )
   assert not root.find("""bandanas""" )
   assert not root.find("""apps""" )
   root.delete("""all""" )
   assert not root.find("""all""" )
   root.delete("""banana""" )
   assert not root.find("""banana""" )
   assert root.find("""bananas""" )

   return True



def     __lowerCAmelCase						(							)       ->							None:
   assert test_trie()



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				int     =	RadixNode()
   __lowerCamelCase:				str     =	"""banana bananas bandanas bandana band apple all beast""".split()
   root.insert_many(snake_case )

   print("""Words:"""						,      snake_case )
   print("""Tree:""" )
   root.print_tree()


if __name__ == "__main__":
   main()


