



'''simple docstring'''

import collections.abc
from typing import Optional, Tuple, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCAmelCase__						:Optional[Any]          =			logging.get_logger(__name__)

# General docstring
UpperCAmelCase__						:List[str]          =			'''PoolFormerConfig'''

# Base docstring
UpperCAmelCase__						:Tuple          =			'''sail/poolformer_s12'''
UpperCAmelCase__						:Optional[int]          =			[1, 512, 7, 7]

# Image classification docstring
UpperCAmelCase__						:Optional[Any]          =			'''sail/poolformer_s12'''
UpperCAmelCase__						:Union[str, Any]          =			'''tabby, tabby cat'''

UpperCAmelCase__						:List[str]          =			[
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def     __lowercase  (_lowercase,						_lowercase = 0.0,						_lowercase = False     )   ->     List[str]:


     """simple docstring"""

     if drop_prob == 0.0 or not training:
          return input
     __lowerCamelCase						:       Optional[Any]				   =				1 - drop_prob
     __lowerCamelCase						:       Union[str, Any]				   =				(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
     __lowerCamelCase						:       Optional[int]				   =				keep_prob + torch.rand(UpperCAmelCase__,						dtype=input.dtype,						device=input.device     )
     random_tensor.floor_()  # binarize
     __lowerCamelCase						:       List[Any]				   =				input.div(UpperCAmelCase__     ) * random_tensor
     return output







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Tuple	,    A__      : Optional[float] = None					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Tuple				   =				drop_prob




       def 					a_					(					self      : Union[str, Any]	,    A__      : torch.Tensor					):


            """simple docstring"""







            return drop_path(__lowerCAmelCase	,    self.drop_prob	,    self.training					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return "p={}".format(self.drop_prob					)







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Union[str, Any]	,    A__      : Tuple	,    A__      : Union[str, Any]	,    A__      : Any	,    A__      : Union[str, Any]	,    A__      : Dict	,    A__      : Dict=None					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Union[str, Any]				   =				patch_size if isinstance(__lowerCAmelCase	,    collections.abc.Iterable					) else (patch_size, patch_size)
            __lowerCamelCase						:       int				   =				stride if isinstance(__lowerCAmelCase	,    collections.abc.Iterable					) else (stride, stride)
            __lowerCamelCase						:       Optional[Any]				   =				padding if isinstance(__lowerCAmelCase	,    collections.abc.Iterable					) else (padding, padding)

            __lowerCamelCase						:       Optional[Any]				   =				nn.Convad(__lowerCAmelCase	,    __lowerCAmelCase	,    kernel_size=__lowerCAmelCase	,    stride=__lowerCAmelCase	,    padding=__lowerCAmelCase					)
            __lowerCamelCase						:       List[str]				   =				norm_layer(__lowerCAmelCase					) if norm_layer else nn.Identity()




       def 					a_					(					self      : List[str]	,    A__      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.projection(__lowerCAmelCase					)
            __lowerCamelCase						:       Dict				   =				self.norm(__lowerCAmelCase					)
            return embeddings







class 	SCREAMING_SNAKE_CASE	(      nn.GroupNorm  ):




       def __init__(					self      : Tuple	,    A__      : int	,    **A__      : Optional[Any]					):


            """simple docstring"""







            super().__init__(1	,    __lowerCAmelCase	,    **__lowerCAmelCase					)







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : int	,    A__      : Optional[Any]					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Tuple				   =				nn.AvgPoolad(__lowerCAmelCase	,    stride=1	,    padding=pool_size // 2	,    count_include_pad=__lowerCAmelCase					)




       def 					a_					(					self      : Optional[Any]	,    A__      : List[Any]					):


            """simple docstring"""







            return self.pool(__lowerCAmelCase					) - hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Tuple	,    A__      : int	,    A__      : Optional[int]	,    A__      : Optional[int]	,    A__      : Any					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       List[Any]				   =				nn.Convad(__lowerCAmelCase	,    __lowerCAmelCase	,    1					)
            __lowerCamelCase						:       Any				   =				nn.Convad(__lowerCAmelCase	,    __lowerCAmelCase	,    1					)
            __lowerCamelCase						:       List[Any]				   =				PoolFormerDropPath(__lowerCAmelCase					)
            if isinstance(config.hidden_act	,    __lowerCAmelCase					):
                 __lowerCamelCase						:       List[Any]				   =				ACTaFN[config.hidden_act]
            else:
                 __lowerCamelCase						:       List[str]				   =				config.hidden_act




       def 					a_					(					self      : Tuple	,    A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.conva(__lowerCAmelCase					)
            __lowerCamelCase						:       int				   =				self.act_fn(__lowerCAmelCase					)
            __lowerCamelCase						:       Union[str, Any]				   =				self.drop(__lowerCAmelCase					)
            __lowerCamelCase						:       Tuple				   =				self.conva(__lowerCAmelCase					)
            __lowerCamelCase						:       Optional[Any]				   =				self.drop(__lowerCAmelCase					)

            return hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Dict	,    A__      : Optional[Any]	,    A__      : int	,    A__      : Dict	,    A__      : Dict	,    A__      : Tuple	,    A__      : Union[str, Any]					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       List[Any]				   =				PoolFormerPooling(__lowerCAmelCase					)
            __lowerCamelCase						:       Optional[Any]				   =				PoolFormerOutput(__lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase	,    __lowerCAmelCase					)
            __lowerCamelCase						:       int				   =				PoolFormerGroupNorm(__lowerCAmelCase					)
            __lowerCamelCase						:       Any				   =				PoolFormerGroupNorm(__lowerCAmelCase					)

            # Useful for training neural nets
            __lowerCamelCase						:       Optional[int]				   =				PoolFormerDropPath(__lowerCAmelCase					) if drop_path > 0.0 else nn.Identity()
            __lowerCamelCase						:       Any				   =				config.use_layer_scale
            if config.use_layer_scale:
                 __lowerCamelCase						:       str				   =				nn.Parameter(
                     config.layer_scale_init_value * torch.ones((__lowerCAmelCase)					)	,    requires_grad=__lowerCAmelCase					)
                 __lowerCamelCase						:       List[str]				   =				nn.Parameter(
                     config.layer_scale_init_value * torch.ones((__lowerCAmelCase)					)	,    requires_grad=__lowerCAmelCase					)




       def 					a_					(					self      : Dict	,    A__      : Dict					):


            """simple docstring"""







            if self.use_layer_scale:
                 __lowerCamelCase						:       Any				   =				self.pooling(self.before_norm(__lowerCAmelCase					)					)
                 __lowerCamelCase						:       Union[str, Any]				   =				self.layer_scale_a.unsqueeze(-1					).unsqueeze(-1					) * pooling_output
                 # First residual connection
                 __lowerCamelCase						:       Optional[Any]				   =				hidden_states + self.drop_path(__lowerCAmelCase					)
                 __lowerCamelCase						:       Optional[Any]				   =				()

                 __lowerCamelCase						:       List[Any]				   =				self.output(self.after_norm(__lowerCAmelCase					)					)
                 __lowerCamelCase						:       List[Any]				   =				self.layer_scale_a.unsqueeze(-1					).unsqueeze(-1					) * layer_output
                 # Second residual connection
                 __lowerCamelCase						:       Optional[int]				   =				hidden_states + self.drop_path(__lowerCAmelCase					)

                 __lowerCamelCase						:       Union[str, Any]				   =				(output,) + outputs
                 return outputs

            else:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.drop_path(self.pooling(self.before_norm(__lowerCAmelCase					)					)					)
                 # First residual connection
                 __lowerCamelCase						:       List[Any]				   =				pooling_output + hidden_states
                 __lowerCamelCase						:       Any				   =				()

                 # Second residual connection inside the PoolFormerOutput block
                 __lowerCamelCase						:       Dict				   =				self.drop_path(self.output(self.after_norm(__lowerCAmelCase					)					)					)
                 __lowerCamelCase						:       Dict				   =				hidden_states + layer_output

                 __lowerCamelCase						:       Tuple				   =				(output,) + outputs
                 return outputs







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Any	,    A__      : Dict					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       int				   =				config
            # stochastic depth decay rule
            __lowerCamelCase						:       int				   =				[x.item() for x in torch.linspace(0	,    config.drop_path_rate	,    sum(config.depths					)					)]

            # patch embeddings
            __lowerCamelCase						:       List[Any]				   =				[]
            for i in range(config.num_encoder_blocks					):
                 embeddings.append(
                     PoolFormerEmbeddings(
                         patch_size=config.patch_sizes[i]	,    stride=config.strides[i]	,    padding=config.padding[i]	,    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]	,    hidden_size=config.hidden_sizes[i]	,    )					)
            __lowerCamelCase						:       List[Any]				   =				nn.ModuleList(__lowerCAmelCase					)

            # Transformer blocks
            __lowerCamelCase						:       str				   =				[]
            __lowerCamelCase						:       Any				   =				0
            for i in range(config.num_encoder_blocks					):
                 # each block consists of layers
                 __lowerCamelCase						:       List[str]				   =				[]
                 if i != 0:
                      cur += config.depths[i - 1]
                 for j in range(config.depths[i]					):
                      layers.append(
                          PoolFormerLayer(
                              __lowerCAmelCase	,    num_channels=config.hidden_sizes[i]	,    pool_size=config.pool_size	,    hidden_size=config.hidden_sizes[i]	,    intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio					)	,    drop_path=dpr[cur + j]	,    )					)
                 blocks.append(nn.ModuleList(__lowerCAmelCase					)					)

            __lowerCamelCase						:       int				   =				nn.ModuleList(__lowerCAmelCase					)




       def 					a_					(					self      : Any	,    A__      : Dict	,    A__      : Optional[Any]=False	,    A__      : List[str]=True					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				() if output_hidden_states else None

            __lowerCamelCase						:       Dict				   =				pixel_values
            for idx, layers in enumerate(zip(self.patch_embeddings	,    self.block					)					):
                 __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				layers
                 # Get patch embeddings from hidden_states
                 __lowerCamelCase						:       Optional[int]				   =				embedding_layer(__lowerCAmelCase					)
                 # Send the embeddings through the blocks
                 for _, blk in enumerate(__lowerCAmelCase					):
                      __lowerCamelCase						:       Any				   =				blk(__lowerCAmelCase					)
                      __lowerCamelCase						:       List[Any]				   =				layer_outputs[0]

                 if output_hidden_states:
                      __lowerCamelCase						:       Optional[Any]				   =				all_hidden_states + (hidden_states,)

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

            return BaseModelOutputWithNoAttention(last_hidden_state=__lowerCAmelCase	,    hidden_states=__lowerCAmelCase					)







class 	SCREAMING_SNAKE_CASE	(      UpperCAmelCase__  ):
       snake_case__    :     Any   		=			PoolFormerConfig
       snake_case__    :     int   		=			'poolformer'
       snake_case__    :     Optional[Any]   		=			'pixel_values'
       snake_case__    :     str   		=			True




       def 					a_					(					self      : List[str]	,    A__      : List[str]					):


            """simple docstring"""







            if isinstance(__lowerCAmelCase	,    (nn.Linear, nn.Convad)					):
                 module.weight.data.normal_(mean=0.0	,    std=self.config.initializer_range					)
                 if module.bias is not None:
                      module.bias.data.zero_()
            elif isinstance(__lowerCAmelCase	,    nn.LayerNorm					):
                 module.bias.data.zero_()
                 module.weight.data.fill_(1.0					)




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[int]	,    A__      : Optional[Any]=False					):


            """simple docstring"""







            if isinstance(__lowerCAmelCase	,    __lowerCAmelCase					):
                 __lowerCamelCase						:       Tuple				   =				value

UpperCAmelCase__						:Any          =			r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

UpperCAmelCase__						:Optional[int]          =			r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
'''







@add_start_docstrings(
    'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'    ,   UpperCAmelCase__    ,   )
class 	SCREAMING_SNAKE_CASE	(      UpperCAmelCase__  ):




       def __init__(					self      : Any	,    A__      : Union[str, Any]					):


            """simple docstring"""







            super().__init__(__lowerCAmelCase					)
            __lowerCamelCase						:       List[str]				   =				config

            __lowerCamelCase						:       Optional[Any]				   =				PoolFormerEncoder(__lowerCAmelCase					)

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            return self.embeddings.patch_embeddings




       @add_start_docstrings_to_model_forward(__lowerCAmelCase					)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC	,    output_type=__lowerCAmelCase	,    config_class=_CONFIG_FOR_DOC	,    modality="""vision"""	,    expected_output=_EXPECTED_OUTPUT_SHAPE	,    )
       def 					a_					(					self      : int	,    A__      : Optional[torch.FloatTensor] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[bool] = None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __lowerCamelCase						:       List[Any]				   =				return_dict if return_dict is not None else self.config.use_return_dict

            if pixel_values is None:
                 raise ValueError("""You have to specify pixel_values"""					)

            __lowerCamelCase						:       int				   =				self.encoder(
                __lowerCAmelCase	,    output_hidden_states=__lowerCAmelCase	,    return_dict=__lowerCAmelCase	,    )
            __lowerCamelCase						:       List[str]				   =				encoder_outputs[0]

            if not return_dict:
                 return (sequence_output, None) + encoder_outputs[1:]

            return BaseModelOutputWithNoAttention(
                last_hidden_state=__lowerCAmelCase	,    hidden_states=encoder_outputs.hidden_states	,    )







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Union[str, Any]	,    A__      : Any					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Any				   =				nn.Linear(config.hidden_size	,    config.hidden_size					)




       def 					a_					(					self      : int	,    A__      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.dense(__lowerCAmelCase					)
            return output







@add_start_docstrings(
    '\n    PoolFormer Model transformer with an image classification head on top\n    '    ,   UpperCAmelCase__    ,   )
class 	SCREAMING_SNAKE_CASE	(      UpperCAmelCase__  ):




       def __init__(					self      : Dict	,    A__      : Optional[int]					):


            """simple docstring"""







            super().__init__(__lowerCAmelCase					)
            __lowerCamelCase						:       int				   =				config.num_labels
            __lowerCamelCase						:       List[Any]				   =				PoolFormerModel(__lowerCAmelCase					)

            # Final norm
            __lowerCamelCase						:       Optional[int]				   =				PoolFormerGroupNorm(config.hidden_sizes[-1]					)
            # Classifier head
            __lowerCamelCase						:       Any				   =				(
                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(__lowerCAmelCase					)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT	,    output_type=__lowerCAmelCase	,    config_class=_CONFIG_FOR_DOC	,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,    )
       def 					a_					(					self      : Union[str, Any]	,    A__      : Optional[torch.FloatTensor] = None	,    A__      : Optional[torch.LongTensor] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[bool] = None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				return_dict if return_dict is not None else self.config.use_return_dict

            __lowerCamelCase						:       Optional[int]				   =				self.poolformer(
                __lowerCAmelCase	,    output_hidden_states=__lowerCAmelCase	,    return_dict=__lowerCAmelCase	,    )

            __lowerCamelCase						:       str				   =				outputs[0]

            __lowerCamelCase						:       int				   =				self.classifier(self.norm(__lowerCAmelCase					).mean([-2, -1]					)					)

            __lowerCamelCase						:       str				   =				None
            if labels is not None:
                 if self.config.problem_type is None:
                      if self.num_labels == 1:
                           __lowerCamelCase						:       Union[str, Any]				   =				"""regression"""
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                           __lowerCamelCase						:       List[Any]				   =				"""single_label_classification"""
                      else:
                           __lowerCamelCase						:       List[str]				   =				"""multi_label_classification"""

                 if self.config.problem_type == "regression":
                      __lowerCamelCase						:       Optional[Any]				   =				MSELoss()
                      if self.num_labels == 1:
                           __lowerCamelCase						:       Optional[Any]				   =				loss_fct(logits.squeeze()	,    labels.squeeze()					)
                      else:
                           __lowerCamelCase						:       Dict				   =				loss_fct(__lowerCAmelCase	,    __lowerCAmelCase					)
                 elif self.config.problem_type == "single_label_classification":
                      __lowerCamelCase						:       Union[str, Any]				   =				CrossEntropyLoss()
                      __lowerCamelCase						:       Optional[Any]				   =				loss_fct(logits.view(-1	,    self.num_labels					)	,    labels.view(-1					)					)
                 elif self.config.problem_type == "multi_label_classification":
                      __lowerCamelCase						:       int				   =				BCEWithLogitsLoss()
                      __lowerCamelCase						:       Optional[Any]				   =				loss_fct(__lowerCAmelCase	,    __lowerCAmelCase					)

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

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











'''simple docstring'''

from __future__ import annotations

from random import random







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Tuple	,    A__      : int | None = None					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				value
            __lowerCamelCase						:       Dict				   =				random()
            __lowerCamelCase						:       Node | None				   =				None
            __lowerCamelCase						:       Node | None				   =				None




       def __repr__(					self      : Any					):


            """simple docstring"""







            from pprint import pformat

            if self.left is None and self.right is None:
                 return f"'{self.value}: {self.prior:.5}'"
            else:
                 return pformat(
                     {f"{self.value}: {self.prior:.5}": (self.left, self.right)}	,    indent=1					)




       def __str__(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				str(self.value					) + """ """
            __lowerCamelCase						:       Any				   =				str(self.left or """"""					)
            __lowerCamelCase						:       List[str]				   =				str(self.right or """"""					)
            return value + left + right

def     __lowercase  (_lowercase,						_lowercase     )   ->     tuple[Node | None, Node | None]:


     """simple docstring"""
     if root is None:  # None tree is split into 2 Nones
          return None, None
     elif root.value is None:
          return None, None
     else:
          if value < root.value:
               __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(root.left,						_lowercase     )
               return left, root
          else:
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				split(root.right,						_lowercase     )
               return root, right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     if (not left) or (not right):  # If one node is None, return the other
          return left or right
     elif left.prior < right.prior:
          __lowerCamelCase						:       Tuple				   =				merge(left.right,						_lowercase     )
          return left
     else:
          __lowerCamelCase						:       int				   =				merge(_lowercase,						right.left     )
          return right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				Node(_lowercase     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				split(_lowercase,						_lowercase     )
     return merge(merge(_lowercase,						_lowercase     ),						_lowercase     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(_lowercase,						value - 1     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				split(_lowercase,						_lowercase     )
     return merge(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     None:


     """simple docstring"""
     if not root:  # None
          return
     else:
          inorder(root.left     )
          print(root.value,						end=""","""     )
          inorder(root.right     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     for arg in args.split():
          if arg[0] == "+":
               __lowerCamelCase						:       Dict				   =				insert(_lowercase,						int(arg[1:]     )     )

          elif arg[0] == "-":
               __lowerCamelCase						:       List[str]				   =				erase(_lowercase,						int(arg[1:]     )     )

          else:
               print("""Unknown command"""     )

     return root
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				None
     print(
         """enter numbers to create a tree, + value to add value into treap, """
         """- value to erase all nodes with value. 'q' to quit. """     )

     __lowerCamelCase						:       int				   =				input()
     while args != "q":
          __lowerCamelCase						:       List[Any]				   =				interact_treap(_lowercase,						_lowercase     )
          print(_lowercase     )
          __lowerCamelCase						:       List[Any]				   =				input()

     print("""good by!"""     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



