


from __future__ import annotations

import queue
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = data
								__lowercase			       = None
								__lowercase			       = None




def 		lowercase_	(  ):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(__UpperCamelCase							,      __UpperCamelCase	) or not node:
						return
			__lowercase			       = queue.Queue()
			q.put(__UpperCamelCase	)
			while not q.empty():
						__lowercase			       = 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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(__UpperCamelCase							,      __UpperCamelCase	) or not node:
						return
			__lowercase			       = queue.Queue()
			q.put(__UpperCamelCase	)
			while not q.empty():
						__lowercase			       = []
						while not q.empty():
									__lowercase			       = 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(__UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(__UpperCamelCase							,      __UpperCamelCase	) or not node:
						return
			__lowercase			       = []
			__lowercase			       = node
			while n or stack:
						while n:  # start from root node, find its left child
									print(n.data							,      end=''','''	)
									stack.append(__UpperCamelCase	)
									__lowercase			       = n.left
						# end of while means current node doesn't have left child
						__lowercase			       = stack.pop()
						# start to traverse its right child
						__lowercase			       = n.right


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(__UpperCamelCase							,      __UpperCamelCase	) or not node:
						return
			__lowercase			       = []
			__lowercase			       = node
			while n or stack:
						while n:
									stack.append(__UpperCamelCase	)
									__lowercase			       = n.left
						__lowercase			       = stack.pop()
						print(n.data							,      end=''','''	)
						__lowercase			       = n.right


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase = ""							,      _UpperCamelCase=50							,      _UpperCamelCase="*"	):


			'''simple docstring'''







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


if __name__ == "__main__":
	import doctest

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

	a			:			Optional[Any]												=					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'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger("transformers.models.speecht5")




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"upsamples.{i}.1.bias"]

      for i in range(len(config.upsample_rates    ) * len(config.resblock_kernel_sizes    )    ):
            for j in range(len(config.resblock_dilation_sizes    )    ):
                  SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['output_conv.1.weight_v']
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint['output_conv.1.bias']

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.from_numpy(__UpperCamelCase    ).float()

      model.save_pretrained(__UpperCamelCase    )

      if repo_id:
            print('Pushing to the hub...'    )
            model.push_to_hub(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
      parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
      )

      UpperCamelCase_    	=				parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )


