
'''simple docstring'''
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		return 1.0 / (1.0 + np.exp(-_outputs			))




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		snake_case  :       List[str]     			=						np.max(_outputs ,       axis=-1 ,       keepdims=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Any     			=						np.exp(_outputs - maxes			)
		return shifted_exp / shifted_exp.sum(axis=-1 ,       keepdims=SCREAMING_SNAKE_CASE__			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""sigmoid"""
		lowerCamelCase     							=						"""softmax"""
		lowerCamelCase     							=						"""none"""







@add_end_docstrings(
    __SCREAMING_SNAKE_CASE    ,					r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """    ,					)
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						False
		lowerCamelCase     							=						ClassificationFunction.NONE



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






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				self.check_model_type(
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				    if self.framework == '''tf'''
				    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Union[str, Any]=None  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			Union[str, Any]=""  ,	**UpperCamelCase__				:			Optional[int]      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						tokenizer_kwargs

				snake_case  :       Tuple     			=						{}
				if hasattr(self.model.config  ,	'''return_all_scores'''      ) and return_all_scores is None:
						snake_case  :       int     			=						self.model.config.return_all_scores

				if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) or top_k is None:
						snake_case  :       int     			=						top_k
						snake_case  :       Union[str, Any]     			=						False
				elif return_all_scores is not None:
						warnings.warn(
						    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
						    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''  ,	UpperCamelCase__  ,	)
						if return_all_scores:
								snake_case  :       Optional[Any]     			=						None
						else:
								snake_case  :       Union[str, Any]     			=						1

				if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
						snake_case  :       int     			=						ClassificationFunction[function_to_apply.upper()]

				if function_to_apply is not None:
						snake_case  :       Optional[int]     			=						function_to_apply
				return preprocess_params, {}, postprocess_params



		def __call__(      self				:			Any  ,	*UpperCamelCase__				:			int  ,	**UpperCamelCase__				:			int      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						super().__call__(*UpperCamelCase__  ,	**UpperCamelCase__      )
				# TODO try and retrieve it in a nicer way from _sanitize_parameters.
				snake_case  :       Optional[int]     			=						'''top_k''' not in kwargs
				if isinstance(args[0]  ,	UpperCamelCase__      ) and _legacy:
						# This pipeline is odd, and return a list when single item is run
						return [result]
				else:
						return result



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			int      )						->			Dict[str, GenericTensor]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.framework
				if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
						return self.tokenizer(**UpperCamelCase__  ,	return_tensors=UpperCamelCase__  ,	**UpperCamelCase__      )
				elif isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) and len(UpperCamelCase__      ) == 1 and isinstance(inputs[0]  ,	UpperCamelCase__      ) and len(inputs[0]      ) == 2:
						# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
						return self.tokenizer(
						    text=inputs[0][0]  ,	text_pair=inputs[0][1]  ,	return_tensors=UpperCamelCase__  ,	**UpperCamelCase__      )
				elif isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
						# This is likely an invalid usage of the pipeline attempting to pass text pairs.
						raise ValueError(
						    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
						    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''      )
				return self.tokenizer(UpperCamelCase__  ,	return_tensors=UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple      )						->			int:






				"""simple docstring"""







				return self.model(**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			Optional[int]=1  ,	UpperCamelCase__				:			List[Any]=True      )						->			List[str]:






				"""simple docstring"""







				if function_to_apply is None:
						if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
								snake_case  :       Any     			=						ClassificationFunction.SIGMOID
						elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
								snake_case  :       int     			=						ClassificationFunction.SOFTMAX
						elif hasattr(self.model.config  ,	'''function_to_apply'''      ) and function_to_apply is None:
								snake_case  :       List[Any]     			=						self.model.config.function_to_apply
						else:
								snake_case  :       List[Any]     			=						ClassificationFunction.NONE

				snake_case  :       Tuple     			=						model_outputs['''logits'''][0]
				snake_case  :       str     			=						outputs.numpy()

				if function_to_apply == ClassificationFunction.SIGMOID:
						snake_case  :       int     			=						sigmoid(UpperCamelCase__      )
				elif function_to_apply == ClassificationFunction.SOFTMAX:
						snake_case  :       Union[str, Any]     			=						softmax(UpperCamelCase__      )
				elif function_to_apply == ClassificationFunction.NONE:
						snake_case  :       List[str]     			=						outputs
				else:
						raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}'      )

				if top_k == 1 and _legacy:
						return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

				snake_case  :       Tuple     			=						[
				    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(UpperCamelCase__      )
				]
				if not _legacy:
						dict_scores.sort(key=lambda UpperCamelCase__      : x["score"]  ,	reverse=UpperCamelCase__      )
						if top_k is not None:
								snake_case  :       Tuple     			=						dict_scores[:top_k]
				return dict_scores




'''simple docstring'''
from __future__ import annotations

import queue






class 	snake_case__       :







		"""simple docstring"""

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






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						data
				snake_case  :       Tuple     			=						None
				snake_case  :       int     			=						None







def    _UpperCamelCase						(       )  ->							TreeNode:


		'''simple docstring'''


		print('''\n********Press N to stop entering at any point of time********\n'''			)
		snake_case  :       List[Any]     			=						input('''Enter the value of the root node: '''			).strip().lower()
		snake_case  :       queue.Queue     			=						queue.Queue()
		snake_case  :       List[Any]     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       List[str]     			=						q.get()
				snake_case  :       List[str]     			=						F'Enter the left node of {node_found.data}: '
				snake_case  :       List[Any]     			=						input(SCREAMING_SNAKE_CASE__			).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				snake_case  :       Any     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
				snake_case  :       List[str]     			=						left_node
				q.put(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Union[str, Any]     			=						F'Enter the right node of {node_found.data}: '
				snake_case  :       Optional[Any]     			=						input(SCREAMING_SNAKE_CASE__			).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				snake_case  :       Any     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
				snake_case  :       Union[str, Any]     			=						right_node
				q.put(SCREAMING_SNAKE_CASE__			)
		raise




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		print(node.data ,       end=''','''			)
		pre_order(node.left			)
		pre_order(node.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		in_order(node.left			)
		print(node.data ,       end=''','''			)
		in_order(node.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		post_order(node.left			)
		post_order(node.right			)
		print(node.data ,       end=''','''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       Any     			=						q.get()
				print(node_dequeued.data ,       end=''','''			)
				if node_dequeued.left:
						q.put(node_dequeued.left			)
				if node_dequeued.right:
						q.put(node_dequeued.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       str     			=						[]
				while not q.empty():
						snake_case  :       int     			=						q.get()
						print(node_dequeued.data ,       end=''','''			)
						if node_dequeued.left:
								list_.append(node_dequeued.left			)
						if node_dequeued.right:
								list_.append(node_dequeued.right			)
				print()
				for node in list_:
						q.put(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       list[TreeNode]     			=						[]
		snake_case  :       Union[str, Any]     			=						node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data ,       end=''','''			)
						stack.append(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Dict     			=						n.left
				# end of while means current node doesn't have left child
				snake_case  :       List[Any]     			=						stack.pop()
				# start to traverse its right child
				snake_case  :       Any     			=						n.right




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       list[TreeNode]     			=						[]
		snake_case  :       Dict     			=						node
		while n or stack:
				while n:
						stack.append(SCREAMING_SNAKE_CASE__			)
						snake_case  :       List[str]     			=						n.left
				snake_case  :       Union[str, Any]     			=						stack.pop()
				print(n.data ,       end=''','''			)
				snake_case  :       int     			=						n.right




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case			,snake_case  :       Dict     			=						[], []
		snake_case  :       Optional[int]     			=						node
		stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # to find the reversed order of post order, store it in stack2
				snake_case  :       Union[str, Any]     			=						stacka.pop()
				if n.left:
						stacka.append(n.left			)
				if n.right:
						stacka.append(n.right			)
				stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data ,       end=''','''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "" ,       SCREAMING_SNAKE_CASE__=50 ,       SCREAMING_SNAKE_CASE__="*"			)  ->							str:


		'''simple docstring'''


		if not s:
				return "\n" + width * char
		snake_case			,snake_case  :       Any     			=						divmod(width - len(SCREAMING_SNAKE_CASE__			) - 2 ,       2			)
		return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("Binary Tree Traversals"))

						lowercase__						       =      build_tree()
						print(prompt("Pre Order Traversal"))
						pre_order(node)
						print(prompt() + "\n")

						print(prompt("In Order Traversal"))
						in_order(node)
						print(prompt() + "\n")

						print(prompt("Post Order Traversal"))
						post_order(node)
						print(prompt() + "\n")

						print(prompt("Level Order Traversal"))
						level_order(node)
						print(prompt() + "\n")

						print(prompt("Actual Level Order Traversal"))
						level_order_actual(node)
						print("*" * 5_0 + "\n")

						print(prompt("Pre Order Traversal - Iteration Version"))
						pre_order_iter(node)
						print(prompt() + "\n")

						print(prompt("In Order Traversal - Iteration Version"))
						in_order_iter(node)
						print(prompt() + "\n")

						print(prompt("Post Order Traversal - Iteration Version"))
						post_order_iter(node)
						print(prompt())


