



from __future__ import annotations

import queue



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  data
						lowerCAmelCase__  :       Optional[int]				=  None
						lowerCAmelCase__  :       str				=  None






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
								return
				lowerCAmelCase__  :       queue.Queue				=  queue.Queue()
				q.put(lowerCamelCase_)
				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 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
								return
				lowerCAmelCase__  :       queue.Queue				=  queue.Queue()
				q.put(lowerCamelCase_)
				while not q.empty():
								lowerCAmelCase__  :       List[str]				=  []
								while not q.empty():
												lowerCAmelCase__  :       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(lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	TreeNode):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_) or not node:
								return
				lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  [], []
				lowerCAmelCase__  :       List[Any]				=  node
				stacka.append(lowerCamelCase_)
				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(lowerCamelCase_)
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data ,end=''',''')






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str = "" ,lowerCamelCase_	:	Any=50 ,lowerCamelCase_	:	Any="*"):





				'''simple docstring'''

				if not s:
								return "\n" + width * char
				lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  divmod(width - len(lowerCamelCase_) - 2 ,2)
				return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
							import doctest

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

							__snake_case       :	TreeNode            =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())






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

