

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


a__												=						get_tests_dir('''fixtures''')

class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Tuple:
												# A mock response for an HTTP head request to emulate server down
												_a      :       Optional[Any]   =						mock.Mock()
												_a      :       List[Any]   =						5_0_0
												_a      :       Optional[Any]   =						{}
												_a      :       List[Any]   =						HTTPError
												_a      :       Optional[Any]   =						{}

												# Download this model to make sure it's in the cache.
												_a      :       Dict   =						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=_a			) as mock_head:
																	_a      :       Tuple   =						WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''			)
																	# This check we did call the fake head request
																	mock_head.assert_called()




							def 							__lowercase							(     self			)       ->       Tuple:
												# This test is for deprecated behavior and can be removed in v5
												_a      :       int   =						WavaVecaFeatureExtractor.from_pretrained(
												    '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''			)



@is_staging_test
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@classmethod
							def 							__lowercase							(     cls			)       ->       Optional[Any]:
												_a      :       str   =						TOKEN
												HfFolder.save_token(_a			)


							@classmethod
							def 							__lowercase							(     cls			)       ->       Tuple:
												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 							__lowercase							(     self			)       ->       str:
												_a      :       int   =						WavaVecaFeatureExtractor.from_pretrained(_a			)
												feature_extractor.push_to_hub('''test-feature-extractor'''			,				use_auth_token=self._token			)

												_a      :       Optional[Any]   =						WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""			)
												for k, v in feature_extractor.__dict__.items():
																	self.assertEqual(_a			,				getattr(_a			,				_a			)			)

												# 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(
																	    _a			,				repo_id='''test-feature-extractor'''			,				push_to_hub=_a			,				use_auth_token=self._token			)

												_a      :       Tuple   =						WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""			)
												for k, v in feature_extractor.__dict__.items():
																	self.assertEqual(_a			,				getattr(_a			,				_a			)			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       str   =						WavaVecaFeatureExtractor.from_pretrained(_a			)
												feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''			,				use_auth_token=self._token			)

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

												# 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(
																	    _a			,				repo_id='''valid_org/test-feature-extractor-org'''			,				push_to_hub=_a			,				use_auth_token=self._token			)

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




							def 							__lowercase							(     self			)       ->       Any:
												CustomFeatureExtractor.register_for_auto_class()
												_a      :       Dict   =						CustomFeatureExtractor.from_pretrained(_a			)

												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'''}			,				)

												_a      :       Tuple   =						AutoFeatureExtractor.from_pretrained(
												    F"""{USER}/test-dynamic-feature-extractor"""			,				trust_remote_code=_a			)
												# 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'''			)






from __future__ import annotations

from dataclasses import dataclass

@dataclass
class 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		float
							UpperCAmelCase__			:		TreeNode | None             =       None
							UpperCAmelCase__			:		TreeNode | None             =       None



def   __UpperCAmelCase					(      __a :     TreeNode | None      )     ->	bool:

					"""simple docstring"""

					def is_valid_tree(__a :     TreeNode | None      ) -> bool:
										if node is None:
															return True

										if not isinstance(__a						,__a      ):
															return False

										try:
															float(node.data      )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

					if not is_valid_tree(__a      ):
										raise ValueError(
										    '''Each node should be type of TreeNode and data should be float.'''      )

					def is_binary_search_tree_recursive_check(
					    __a :     TreeNode | None						,__a :     float						,__a :     float      ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,__a						,node.data      )
										    and is_binary_search_tree_recursive_check(
										        node.right						,node.data						,__a      )
										)

					return is_binary_search_tree_recursive_check(__a						,-float('''inf'''      )						,float('''inf'''      )      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



