


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							:					int     						=     get_tests_dir('fixtures')






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		mock.Mock()
											__snake_case				=		5_0_0
											__snake_case				=		{}
											__snake_case				=		HTTPError
											__snake_case				=		{}

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Tuple:




											'''simple docstring'''

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






@is_staging_test
class     UpperCamelCase(  unittest.TestCase   ):


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Union[str, Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		TOKEN
											HfFolder.save_token(SCREAMING_SNAKE_CASE						)


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Tuple						)			->	int:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE						)
											feature_extractor.push_to_hub("test-feature-extractor"     ,    use_auth_token=self._token						)

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

											# 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(
																	    SCREAMING_SNAKE_CASE     ,    repo_id="test-feature-extractor"     ,    push_to_hub=SCREAMING_SNAKE_CASE     ,    use_auth_token=self._token						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Any:




											'''simple docstring'''

											__snake_case				=		WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE						)
											feature_extractor.push_to_hub("valid_org/test-feature-extractor"     ,    use_auth_token=self._token						)

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

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

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											CustomFeatureExtractor.register_for_auto_class()
											__snake_case				=		CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE						)

											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"}     ,    )

											__snake_case				=		AutoFeatureExtractor.from_pretrained(
											    f'''{USER}/test-dynamic-feature-extractor'''     ,    trust_remote_code=SCREAMING_SNAKE_CASE						)
											# 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 decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
	# Find root of polynomial
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





