





'''simple docstring'''






from __future__ import annotations

import queue
class 		lowerCamelCase  :



	def __init__(   self		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   data
			lowerCAmelCase     :    Dict						=   None
			lowerCAmelCase     :    str						=   None






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






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		print(node.data      ,end=","	)
		pre_order(node.left	)
		pre_order(node.right	)






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		in_order(node.left	)
		print(node.data      ,end=","	)
		in_order(node.right	)






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		post_order(node.left	)
		post_order(node.right	)
		print(node.data      ,end=","	)






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		lowerCAmelCase     :    queue.Queue						=   queue.Queue()
		q.put(a_	)
		while not q.empty():
				lowerCAmelCase     :    str						=   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      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		lowerCAmelCase     :    queue.Queue						=   queue.Queue()
		q.put(a_	)
		while not q.empty():
				lowerCAmelCase     :    List[Any]						=   []
				while not q.empty():
						lowerCAmelCase     :    Optional[Any]						=   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(a_	)






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		lowerCAmelCase     :    list[TreeNode]						=   []
		lowerCAmelCase     :    Union[str, Any]						=   node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data      ,end=","	)
						stack.append(a_	)
						lowerCAmelCase     :    Union[str, Any]						=   n.left
				# end of while means current node doesn't have left child
				lowerCAmelCase     :    Optional[int]						=   stack.pop()
				# start to traverse its right child
				lowerCAmelCase     :    Tuple						=   n.right






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		lowerCAmelCase     :    list[TreeNode]						=   []
		lowerCAmelCase     :    List[Any]						=   node
		while n or stack:
				while n:
						stack.append(a_	)
						lowerCAmelCase     :    Optional[Any]						=   n.left
				lowerCAmelCase     :    List[str]						=   stack.pop()
				print(n.data      ,end=","	)
				lowerCAmelCase     :    Dict						=   n.right






def      __A						(							a_						:  TreeNode	):
		if not isinstance(a_      ,a_	) or not node:
				return
		lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   [], []
		lowerCAmelCase     :    List[Any]						=   node
		stacka.append(a_	)
		while stacka:  # to find the reversed order of post order, store it in stack2
				lowerCAmelCase     :    Any						=   stacka.pop()
				if n.left:
						stacka.append(n.left	)
				if n.right:
						stacka.append(n.right	)
				stacka.append(a_	)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data      ,end=","	)






def      __A						(							a_						:  str = ""      ,a_						:  Dict=5_0      ,a_						:  Optional[int]="*"	):
		if not s:
				return "\n" + width * char
		lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   divmod(width - len(a_	) - 2      ,2	)
		return f'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
					import doctest

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

					lowerCAmelCase       		=      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("""*""" * 50 + """\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())










'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



