





from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


UpperCAmelCase_     							= logging.get_logger(__name__)

# General docstring
UpperCAmelCase_     							= 'ResNetConfig'

# Base docstring
UpperCAmelCase_     							= 'microsoft/resnet-50'
UpperCAmelCase_     							= [1, 2048, 7, 7]

# Image classification docstring
UpperCAmelCase_     							= 'microsoft/resnet-50'
UpperCAmelCase_     							= 'tiger cat'

UpperCAmelCase_     							= [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = 3,		__magic_name__ = 1,		__magic_name__ = "relu"							) ->      List[str]:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Optional[Any]          =      nn.Convad(
           __magic_name__,		__magic_name__,		kernel_size=__magic_name__,		stride=__magic_name__,		padding=kernel_size // 2,		bias=__magic_name__							)
       UpperCamelCase__							:				List[str]          =      nn.BatchNormad(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      ACTaFN[activation] if activation is not None else nn.Identity()




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tensor:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.convolution(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      self.normalization(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.activation(__magic_name__							)
       return hidden_state






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Tuple          =      ResNetConvLayer(
           config.num_channels,		config.embedding_size,		kernel_size=7,		stride=2,		activation=config.hidden_act							)
       UpperCamelCase__							:				int          =      nn.MaxPoolad(kernel_size=3,		stride=2,		padding=1							)
       UpperCamelCase__							:				Dict          =      config.num_channels




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tensor:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      pixel_values.shape[1]
       if num_channels != self.num_channels:
           raise ValueError(
               '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''							)
       UpperCamelCase__							:				List[Any]          =      self.embedder(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      self.pooler(__magic_name__							)
       return embedding






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = 2							) ->      Tuple:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				str          =      nn.Convad(__magic_name__,		__magic_name__,		kernel_size=1,		stride=__magic_name__,		bias=__magic_name__							)
       UpperCamelCase__							:				List[str]          =      nn.BatchNormad(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tensor:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.convolution(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.normalization(__magic_name__							)
       return hidden_state






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = 1,		__magic_name__ = "relu"							) ->      Tuple:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Optional[Any]          =      in_channels != out_channels or stride != 1
       UpperCamelCase__							:				Any          =      (
           ResNetShortCut(__magic_name__,		__magic_name__,		stride=__magic_name__							) if should_apply_shortcut else nn.Identity()
       )
       UpperCamelCase__							:				Optional[int]          =      nn.Sequential(
           ResNetConvLayer(__magic_name__,		__magic_name__,		stride=__magic_name__							),		ResNetConvLayer(__magic_name__,		__magic_name__,		activation=__magic_name__							),		)
       UpperCamelCase__							:				Optional[int]          =      ACTaFN[activation]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      hidden_state
       UpperCamelCase__							:				Dict          =      self.layer(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.shortcut(__magic_name__							)
       hidden_state += residual
       UpperCamelCase__							:				Any          =      self.activation(__magic_name__							)
       return hidden_state






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__ = 1,		__magic_name__ = "relu",		__magic_name__ = 4							) ->      int:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				List[str]          =      in_channels != out_channels or stride != 1
       UpperCamelCase__							:				List[Any]          =      out_channels // reduction
       UpperCamelCase__							:				Union[str, Any]          =      (
           ResNetShortCut(__magic_name__,		__magic_name__,		stride=__magic_name__							) if should_apply_shortcut else nn.Identity()
       )
       UpperCamelCase__							:				Union[str, Any]          =      nn.Sequential(
           ResNetConvLayer(__magic_name__,		__magic_name__,		kernel_size=1							),		ResNetConvLayer(__magic_name__,		__magic_name__,		stride=__magic_name__							),		ResNetConvLayer(__magic_name__,		__magic_name__,		kernel_size=1,		activation=__magic_name__							),		)
       UpperCamelCase__							:				Any          =      ACTaFN[activation]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      hidden_state
       UpperCamelCase__							:				str          =      self.layer(__magic_name__							)
       UpperCamelCase__							:				str          =      self.shortcut(__magic_name__							)
       hidden_state += residual
       UpperCamelCase__							:				str          =      self.activation(__magic_name__							)
       return hidden_state






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = 2,		__magic_name__ = 2,		) ->      int:






       """simple docstring"""
       super().__init__()

       UpperCamelCase__							:				Tuple          =      ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

       UpperCamelCase__							:				Optional[int]          =      nn.Sequential(
           # downsampling is done in the first layer with stride of 2
           layer(__magic_name__,		__magic_name__,		stride=__magic_name__,		activation=config.hidden_act							),		*[layer(__magic_name__,		__magic_name__,		activation=config.hidden_act							) for _ in range(depth - 1							)],		)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tensor:






       """simple docstring"""
       UpperCamelCase__							:				int          =      input
       for layer in self.layers:
           UpperCamelCase__							:				Union[str, Any]          =      layer(__magic_name__							)
       return hidden_state






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Optional[Any]          =      nn.ModuleList([]							)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       self.stages.append(
           ResNetStage(
               __magic_name__,		config.embedding_size,		config.hidden_sizes[0],		stride=2 if config.downsample_in_first_stage else 1,		depth=config.depths[0],		)							)
       UpperCamelCase__							:				List[str]          =      zip(config.hidden_sizes,		config.hidden_sizes[1:]							)
       for (in_channels, out_channels), depth in zip(__magic_name__,		config.depths[1:]							):
           self.stages.append(ResNetStage(__magic_name__,		__magic_name__,		__magic_name__,		depth=__magic_name__							)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = False,		__magic_name__ = True							) ->      BaseModelOutputWithNoAttention:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      () if output_hidden_states else None

       for stage_module in self.stages:
           if output_hidden_states:
               UpperCamelCase__							:				int          =      hidden_states + (hidden_state,)

           UpperCamelCase__							:				List[str]          =      stage_module(__magic_name__							)

       if output_hidden_states:
           UpperCamelCase__							:				Tuple          =      hidden_states + (hidden_state,)

       if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

       return BaseModelOutputWithNoAttention(
           last_hidden_state=__magic_name__,		hidden_states=__magic_name__,		)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		ResNetConfig
   a						:							List[Any]   		=		"resnet"
   a						:							int   		=		"pixel_values"
   a						:							int   		=		True




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Tuple:






       """simple docstring"""
       if isinstance(__magic_name__,		nn.Convad							):
           nn.init.kaiming_normal_(module.weight,		mode='''fan_out''',		nonlinearity='''relu'''							)
       elif isinstance(__magic_name__,		(nn.BatchNormad, nn.GroupNorm)							):
           nn.init.constant_(module.weight,		1							)
           nn.init.constant_(module.bias,		0							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=False							) ->      Union[str, Any]:






       """simple docstring"""
       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Dict          =      value




UpperCAmelCase_     							= R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCAmelCase_     							= R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."			,				__lowerCamelCase			,				)
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       super().__init__(__magic_name__							)
       UpperCamelCase__							:				Dict          =      config
       UpperCamelCase__							:				str          =      ResNetEmbeddings(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      ResNetEncoder(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      nn.AdaptiveAvgPoolad((1, 1)							)
       # Initialize weights and apply final processing
       self.post_init()




   @add_start_docstrings_to_model_forward(__magic_name__							)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC,		output_type=__magic_name__,		config_class=_CONFIG_FOR_DOC,		modality='''vision''',		expected_output=_EXPECTED_OUTPUT_SHAPE,		)
   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None							) ->      BaseModelOutputWithPoolingAndNoAttention:






       """simple docstring"""
       UpperCamelCase__							:				int          =      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase__							:				Any          =      return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase__							:				str          =      self.embedder(__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      self.encoder(
           __magic_name__,		output_hidden_states=__magic_name__,		return_dict=__magic_name__							)

       UpperCamelCase__							:				Union[str, Any]          =      encoder_outputs[0]

       UpperCamelCase__							:				Dict          =      self.pooler(__magic_name__							)

       if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=__magic_name__,		pooler_output=__magic_name__,		hidden_states=encoder_outputs.hidden_states,		)






@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "			,				__lowerCamelCase			,				)
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       super().__init__(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      config.num_labels
       UpperCamelCase__							:				Any          =      ResNetModel(__magic_name__							)
       # classification head
       UpperCamelCase__							:				Optional[Any]          =      nn.Sequential(
           nn.Flatten(),		nn.Linear(config.hidden_sizes[-1],		config.num_labels							) if config.num_labels > 0 else nn.Identity(),		)
       # initialize weights and apply final processing
       self.post_init()




   @add_start_docstrings_to_model_forward(__magic_name__							)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT,		output_type=__magic_name__,		config_class=_CONFIG_FOR_DOC,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,		)
   def       UpperCamelCase__  (    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		) ->      ImageClassifierOutputWithNoAttention:






       """simple docstring"""
       UpperCamelCase__							:				int          =      return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase__							:				str          =      self.resnet(__magic_name__,		output_hidden_states=__magic_name__,		return_dict=__magic_name__							)

       UpperCamelCase__							:				Optional[Any]          =      outputs.pooler_output if return_dict else outputs[1]

       UpperCamelCase__							:				Union[str, Any]          =      self.classifier(__magic_name__							)

       UpperCamelCase__							:				Tuple          =      None

       if labels is not None:
           if self.config.problem_type is None:
               if self.num_labels == 1:
                   UpperCamelCase__							:				Optional[Any]          =      '''regression'''
               elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                   UpperCamelCase__							:				str          =      '''single_label_classification'''
               else:
                   UpperCamelCase__							:				Optional[int]          =      '''multi_label_classification'''
           if self.config.problem_type == "regression":
               UpperCamelCase__							:				str          =      MSELoss()
               if self.num_labels == 1:
                   UpperCamelCase__							:				Tuple          =      loss_fct(logits.squeeze(),		labels.squeeze()							)
               else:
                   UpperCamelCase__							:				List[str]          =      loss_fct(__magic_name__,		__magic_name__							)
           elif self.config.problem_type == "single_label_classification":
               UpperCamelCase__							:				str          =      CrossEntropyLoss()
               UpperCamelCase__							:				Optional[Any]          =      loss_fct(logits.view(-1,		self.num_labels							),		labels.view(-1							)							)
           elif self.config.problem_type == "multi_label_classification":
               UpperCamelCase__							:				Optional[int]          =      BCEWithLogitsLoss()
               UpperCamelCase__							:				Tuple          =      loss_fct(__magic_name__,		__magic_name__							)

       if not return_dict:
           UpperCamelCase__							:				List[Any]          =      (logits,) + outputs[2:]
           return (loss,) + output if loss is not None else output

       return ImageClassifierOutputWithNoAttention(loss=__magic_name__,		logits=__magic_name__,		hidden_states=outputs.hidden_states							)






@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "			,				__lowerCamelCase			,				)
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       super().__init__(__magic_name__							)
       super()._init_backbone(__magic_name__							)

       UpperCamelCase__							:				Tuple          =      [config.embedding_size] + config.hidden_sizes
       UpperCamelCase__							:				Dict          =      ResNetEmbeddings(__magic_name__							)
       UpperCamelCase__							:				str          =      ResNetEncoder(__magic_name__							)

       # initialize weights and apply final processing
       self.post_init()




   @add_start_docstrings_to_model_forward(__magic_name__							)
   @replace_return_docstrings(output_type=__magic_name__,		config_class=_CONFIG_FOR_DOC							)
   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None							) ->      BackboneOutput:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      return_dict if return_dict is not None else self.config.use_return_dict
       UpperCamelCase__							:				Any          =      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )

       UpperCamelCase__							:				List[Any]          =      self.embedder(__magic_name__							)

       UpperCamelCase__							:				int          =      self.encoder(__magic_name__,		output_hidden_states=__magic_name__,		return_dict=__magic_name__							)

       UpperCamelCase__							:				str          =      outputs.hidden_states

       UpperCamelCase__							:				Dict          =      ()
       for idx, stage in enumerate(self.stage_names							):
           if stage in self.out_features:
               feature_maps += (hidden_states[idx],)

       if not return_dict:
           UpperCamelCase__							:				Union[str, Any]          =      (feature_maps,)
           if output_hidden_states:
               output += (outputs.hidden_states,)
           return output

       return BackboneOutput(
           feature_maps=__magic_name__,		hidden_states=outputs.hidden_states if output_hidden_states else None,		attentions=__magic_name__,		)








class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "",		__magic_name__ = False							) ->      None:






       """simple docstring"""
       # Mapping from the first character of the prefix of the node
       UpperCamelCase__							:				dict[str, RadixNode]          =      {}

       # A node will be a leaf if the tree contains its word
       UpperCamelCase__							:				Optional[Any]          =      is_leaf

       UpperCamelCase__							:				List[str]          =      prefix




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      tuple[str, str, str]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      0
       for q, w in zip(self.prefix,		__magic_name__							):
           if q != w:
               break

           x += 1

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




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       for word in words:
           self.insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # Case 1: If the word is the prefix of the node
       # Solution: We set the current node as leaf
       if self.prefix == word:
           UpperCamelCase__							:				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:
           UpperCamelCase__							:				Tuple          =      RadixNode(prefix=__magic_name__,		is_leaf=__magic_name__							)

       else:
           UpperCamelCase__							:				Any          =      self.nodes[word[0]]
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)

           # 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(__magic_name__							)

           # 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:
               UpperCamelCase__							:				Tuple          =      remaining_prefix

               UpperCamelCase__							:				Tuple          =      self.nodes[matching_string[0]]
               UpperCamelCase__							:				List[Any]          =      RadixNode(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				str          =      aux_node

               if remaining_word == "":
                   UpperCamelCase__							:				Any          =      True
               else:
                   self.nodes[matching_string[0]].insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      incoming_node.match(
               __magic_name__							)
           # 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(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)
           # 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(__magic_name__							)
           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:
                           UpperCamelCase__							:				Optional[Any]          =      list(self.nodes.values()							)[0]
                           UpperCamelCase__							:				Union[str, Any]          =      merging_node.is_leaf
                           self.prefix += merging_node.prefix
                           UpperCamelCase__							:				int          =      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                   elif len(incoming_node.nodes							) > 1:
                       UpperCamelCase__							:				Any          =      False
                   # If there is 1 edge, we merge it with its child
                   else:
                       UpperCamelCase__							:				Union[str, Any]          =      list(incoming_node.nodes.values()							)[0]
                       UpperCamelCase__							:				List[str]          =      merging_node.is_leaf
                       incoming_node.prefix += merging_node.prefix
                       UpperCamelCase__							:				int          =      merging_node.nodes

                   return True




   def       UpperCamelCase__  (    self,		__magic_name__ = 0							) ->      None:






       """simple docstring"""
       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:
    UpperCamelCase__							:				Optional[int]          =      '''banana bananas bandana band apple all beast'''.split()
    UpperCamelCase__							:				Optional[int]          =      RadixNode()
    root.insert_many(__UpperCAmelCase      )

    assert all(root.find(__UpperCAmelCase      ) 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:
    UpperCamelCase__							:				int          =      RadixNode()
    UpperCamelCase__							:				Any          =      '''banana bananas bandanas bandana band apple all beast'''.split()
    root.insert_many(__UpperCAmelCase      )

    print('''Words:'''		,   __UpperCAmelCase      )
    print('''Tree:'''      )
    root.print_tree()


if __name__ == "__main__":
  main()

