


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


snake_case__			 =						get_tests_dir('''fixtures''')


class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Tuple						)      ->Tuple:






									"""simple docstring"""






									a__ :str						  =    mock.Mock()
									a__ :Optional[Any]						  =    500
									a__ :Any						  =    {}
									a__ :int						  =    HTTPError
									a__ :str						  =    {}

									# Download this model to make sure it's in the cache.
									a__ :List[Any]						  =    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__ :Union[str, Any]						  =    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						:   Tuple						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    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						:   Optional[int]						)      ->List[str]:






									"""simple docstring"""






									a__ :str						  =    TOKEN
									HfFolder.save_token(__A						)




						@classmethod
						def  _snake_case					(	cls						:   int						)      ->Optional[Any]:






									"""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						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    WavaVecaFeatureExtractor.from_pretrained(__A						)
									feature_extractor.push_to_hub("test-feature-extractor"   ,     use_auth_token=self._token						)

									a__ :Union[str, 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__ :Optional[Any]						  =    WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''						)
									for k, v in feature_extractor.__dict__.items():
												self.assertEqual(__A   ,     getattr(__A   ,     __A						)						)




						def  _snake_case					(	self						:   Dict						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    WavaVecaFeatureExtractor.from_pretrained(__A						)
									feature_extractor.push_to_hub("valid_org/test-feature-extractor"   ,     use_auth_token=self._token						)

									a__ :str						  =    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__ :int						  =    WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"						)
									for k, v in feature_extractor.__dict__.items():
												self.assertEqual(__A   ,     getattr(__A   ,     __A						)						)






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






									"""simple docstring"""






									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__ :str						  =    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"						)






import numpy as np


def 				lowerCamelCase__	(      a	:   np.ndarray	,  a	:   np.ndarray	,  a	:   float = 1e-12	,  a	:   int = 100	,  )    ->	tuple[float, np.ndarray]:





			"""simple docstring"""
			assert np.shape(a				)[0] == np.shape(a				)[1]
			# Ensure proper dimensionality.
			assert np.shape(a				)[0] == np.shape(a				)[0]
			# Ensure inputs are either both complex or both real
			assert np.iscomplexobj(a				) == np.iscomplexobj(a				)
			a__ :List[str]						  =    np.iscomplexobj(a				)
			if is_complex:
						# Ensure complex input_matrix is Hermitian
						assert np.array_equal(a	,  input_matrix.conj().T				)

			# Set convergence to False. Will define convergence when we exceed max_iterations
			# or when we have small changes from one iteration to next.

			a__ :List[str]						  =    False
			a__ :str						  =    0
			a__ :Dict						  =    0
			a__ :Tuple						  =    1e12

			while not convergence:
						# Multiple matrix by the vector.
						a__ :Tuple						  =    np.dot(a	,  a				)
						# Normalize the resulting output vector.
						a__ :List[Any]						  =    w / np.linalg.norm(a				)
						# Find rayleigh quotient
						# (faster than usual b/c we know vector is normalized already)
						a__ :List[str]						  =    vector.conj().T if is_complex else vector.T
						a__ :Any						  =    np.dot(a	,  np.dot(a	,  a				)				)

						# Check convergence.
						a__ :Optional[Any]						  =    np.abs(lambda_ - lambda_previous				) / lambda_
						iterations += 1

						if error <= error_tol or iterations >= max_iterations:
									a__ :List[str]						  =    True

						a__ :Optional[int]						  =    lambda_

			if is_complex:
						a__ :Union[str, Any]						  =    np.real(lambda_				)

			return lambda_, vector


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :List[str]						  =    np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
			a__ :Dict						  =    np.array([41, 4, 20]				)
			a__ :Union[str, Any]						  =    real_input_matrix.astype(np.complexaaa				)
			a__ :Optional[Any]						  =    np.triu(1j * complex_input_matrix	,  1				)
			complex_input_matrix += imag_matrix
			complex_input_matrix += -1 * imag_matrix.T
			a__ :Union[str, Any]						  =    np.array([41, 4, 20]				).astype(np.complexaaa				)

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									a__ :Union[str, Any]						  =    real_input_matrix
									a__ :int						  =    real_vector
						elif problem_type == "complex":
									a__ :Optional[Any]						  =    complex_input_matrix
									a__ :List[Any]						  =    complex_vector

						# Our implementation.
						a__  ,  a__ :Any						  =    power_iteration(a	,  a				)

						# Numpy implementation.

						# Get eigenvalues and eigenvectors using built-in numpy
						# eigh (eigh used for symmetric or hermetian matrices).
						a__  ,  a__ :int						  =    np.linalg.eigh(a				)
						# Last eigenvalue is the maximum one.
						a__ :int						  =    eigen_values[-1]
						# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
						a__ :Optional[Any]						  =    eigen_vectors[:, -1]

						# Check our implementation and numpy gives close answers.
						assert np.abs(eigen_value - eigen_value_max				) <= 1e-6
						# Take absolute values element wise of each eigenvector.
						# as they are only unique to a minus sign.
						assert np.linalg.norm(np.abs(a				) - np.abs(a				)				) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


