"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class       a_      :
					UpperCamelCase_ :	int
					UpperCamelCase_ :	Node | None			=    None
					UpperCamelCase_ :	Node | None			=    None







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   Node(1     )
		lowerCAmelCase__        =   Node(2     )
		lowerCAmelCase__        =   Node(3     )
		lowerCAmelCase__        =   Node(4     )
		lowerCAmelCase__        =   Node(5     )
		return tree







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return [root.data, *preorder(root.left     ), *preorder(root.right     )] if root else []







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return postorder(root.left     ) + postorder(root.right     ) + [root.data] if root else []







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return [*inorder(root.left     ), root.data, *inorder(root.right     )] if root else []







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return (max(height(root.left     )			,  height(root.right     )     ) + 1) if root else 0







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []

		if root is None:
				return output

		lowerCAmelCase__        =   deque([root]     )

		while process_queue:
				lowerCAmelCase__        =   process_queue.popleft()
				output.append(node.data     )

				if node.left:
						process_queue.append(node.left     )
				if node.right:
						process_queue.append(node.right     )
		return output







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []

		def populate_output(lowerCamelCase__			,  lowerCamelCase__     ) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data     )
				elif level > 1:
						populate_output(root.left			,  level - 1     )
						populate_output(root.right			,  level - 1     )

		populate_output(lowerCamelCase__			,  lowerCamelCase__     )
		return output







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []

		def populate_output(lowerCamelCase__			,  lowerCamelCase__     ) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data     )
				elif level > 1:
						populate_output(root.right			,  level - 1     )
						populate_output(root.left			,  level - 1     )

		populate_output(lowerCamelCase__			,  lowerCamelCase__     )
		return output







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if root is None:
				return []

		lowerCAmelCase__        =   []

		lowerCAmelCase__        =   0
		lowerCAmelCase__        =   height(lowerCamelCase__     )

		for h in range(1			,  height_tree + 1     ):
				if not flag:
						output.append(get_nodes_from_left_to_right(lowerCamelCase__			,  lowerCamelCase__     )     )
						lowerCAmelCase__        =   1
				else:
						output.append(get_nodes_from_right_to_left(lowerCamelCase__			,  lowerCamelCase__     )     )
						lowerCAmelCase__        =   0

		return output







def 							_UpperCAmelCase		(       ):  # Main function for testing.





		"""simple docstring"""




		lowerCAmelCase__        =   make_tree()
		print(f"""In-order Traversal: {inorder(lowerCamelCase__     )}"""     )
		print(f"""Pre-order Traversal: {preorder(lowerCamelCase__     )}"""     )
		print(f"""Post-order Traversal: {postorder(lowerCamelCase__     )}"""			,  """\n"""     )

		print(f"""Height of Tree: {height(lowerCamelCase__     )}"""			,  """\n"""     )

		print("""Complete Level Order Traversal: """     )
		print(level_order(lowerCamelCase__     )			,  """\n"""     )

		print("""Level-wise order Traversal: """     )

		for level in range(1			,  height(lowerCamelCase__     ) + 1     ):
				print(f"""Level {level}:"""			,  get_nodes_from_left_to_right(lowerCamelCase__			,  level=lowerCamelCase__     )     )

		print("""\nZigZag order Traversal: """     )
		print(zigzag(lowerCamelCase__     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




