





from __future__ import annotations

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

@dataclass
class     A_		:
			_A   :List[Any]  				=      42
			_A   :int  				=      None
			_A   :int  				=      None


def       UpperCamelCase__    (		):
				lowercase							=     Node(1 )
				lowercase							=     Node(2 )
				lowercase							=     Node(3 )
				lowercase							=     Node(4 )
				lowercase							=     Node(5 )
				return tree


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				return (max(height(root.left )       ,height(root.right ) ) + 1) if root else 0


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				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__    (		lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase							=     []

				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(lowercase__       ,lowercase__ )
				return output


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase							=     []

				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(lowercase__       ,lowercase__ )
				return output


def       UpperCamelCase__    (		lowerCAmelCase__ ):





				if root is None:
								return []

				lowercase							=     []

				lowercase							=     0
				lowercase							=     height(lowercase__ )

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

				return output


def       UpperCamelCase__    (		):  # Main function for testing.
				lowercase							=     make_tree()
				print(f"""In-order Traversal: {inorder(lowercase__ )}""" )
				print(f"""Pre-order Traversal: {preorder(lowercase__ )}""" )
				print(f"""Post-order Traversal: {postorder(lowercase__ )}"""       ,"""\n""" )

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

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

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

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


