
'''simple docstring'''

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class  A  (						SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Optional[Any]   =  DebertaTokenizer
	snake_case__   :int   =  True
	snake_case__   :Dict   =  DebertaTokenizerFast





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				lowerCAmelCase__										=				[
				    "l",
				    "o",
				    "w",
				    "e",
				    "r",
				    "s",
				    "t",
				    "i",
				    "d",
				    "n",
				    "\u0120",
				    "\u0120l",
				    "\u0120n",
				    "\u0120lo",
				    "\u0120low",
				    "er",
				    "\u0120lowest",
				    "\u0120newer",
				    "\u0120wider",
				    "[UNK]",
				]
				lowerCAmelCase__										=				dict(zip(__magic_name__   ,				range(len(__magic_name__				)				)				)				)
				lowerCAmelCase__										=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
				lowerCAmelCase__										=				{"unk_token": "[UNK]"}

				lowerCAmelCase__										=				os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["vocab_file"]				)
				lowerCAmelCase__										=				os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["merges_file"]				)
				with open(self.vocab_file   ,				"w"   ,				encoding="utf-8"				) as fp:
							fp.write(json.dumps(__magic_name__				) + "\n"				)
				with open(self.merges_file   ,				"w"   ,				encoding="utf-8"				) as fp:
							fp.write("\n".join(__magic_name__				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				kwargs.update(self.special_tokens_map				)
				return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				"lower newer"
				lowerCAmelCase__										=				"lower newer"
				return input_text, output_text





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_tokenizer()
				lowerCAmelCase__										=				"lower newer"
				lowerCAmelCase__										=				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
				lowerCAmelCase__										=				tokenizer.tokenize(__magic_name__				)
				self.assertListEqual(__magic_name__   ,				__magic_name__				)

				lowerCAmelCase__										=				tokens + [tokenizer.unk_token]
				lowerCAmelCase__										=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_tokenizer()
				lowerCAmelCase__										=				tokenizer("Hello"   ,				"World"				)
				lowerCAmelCase__										=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
				self.assertListEqual(tokd["token_type_ids"]   ,				__magic_name__				)





	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.tokenizer_class.from_pretrained("microsoft/deberta-base"				)

				lowerCAmelCase__										=				tokenizer.encode("sequence builders"   ,				add_special_tokens=__magic_name__				)
				lowerCAmelCase__										=				tokenizer.encode("multi-sequence build"   ,				add_special_tokens=__magic_name__				)

				lowerCAmelCase__										=				tokenizer.encode(
				    "sequence builders"   ,				add_special_tokens=__magic_name__   ,				add_prefix_space=__magic_name__				)
				lowerCAmelCase__										=				tokenizer.encode(
				    "sequence builders"   ,				"multi-sequence build"   ,				add_special_tokens=__magic_name__   ,				add_prefix_space=__magic_name__				)

				lowerCAmelCase__										=				tokenizer.build_inputs_with_special_tokens(__magic_name__				)
				lowerCAmelCase__										=				tokenizer.build_inputs_with_special_tokens(__magic_name__   ,				__magic_name__				)

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode







	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				[self.tokenizer_class]
				if self.test_rust_tokenizer:
							tokenizer_classes.append(self.rust_tokenizer_class				)

				for tokenizer_class in tokenizer_classes:
							lowerCAmelCase__										=				tokenizer_class.from_pretrained("microsoft/deberta-base"				)

							lowerCAmelCase__										=				[
							    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
							    "ALBERT incorporates two parameter reduction techniques",
							    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
							    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
							    " vocabulary embedding.",
							]

							lowerCAmelCase__										=				tokenizer(__magic_name__   ,				padding=__magic_name__				)
							lowerCAmelCase__										=				[tokenizer.decode(__magic_name__   ,				skip_special_tokens=__magic_name__				) for seq in encoding["input_ids"]]

							# fmt: off
							lowerCAmelCase__										=				{
							    "input_ids": [
							        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
							    ],
							    "token_type_ids": [
							        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
							    ],
							    "attention_mask": [
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
							    ]
							}
							# fmt: on

							lowerCAmelCase__										=				[
							    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
							    "ALBERT incorporates two parameter reduction techniques",
							    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
							    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
							    " vocabulary embedding.",
							]

							self.assertDictEqual(encoding.data   ,				__magic_name__				)

							for expected, decoded in zip(__magic_name__   ,				__magic_name__				):
										self.assertEqual(__magic_name__   ,				__magic_name__				)









from __future__ import annotations

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



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						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   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> 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(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> 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(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

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

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




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

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

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

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

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

