




import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				np.ndarray      ,      SCREAMING_SNAKE_CASE_							:				np.ndarray      ,      SCREAMING_SNAKE_CASE_							:				float = 1E-12      ,      SCREAMING_SNAKE_CASE_							:				int = 1_00      ,      ):
					assert np.shape(SCREAMING_SNAKE_CASE_     )[0] == np.shape(SCREAMING_SNAKE_CASE_     )[1]
					# Ensure proper dimensionality.
					assert np.shape(SCREAMING_SNAKE_CASE_     )[0] == np.shape(SCREAMING_SNAKE_CASE_     )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(SCREAMING_SNAKE_CASE_     ) == np.iscomplexobj(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       np.iscomplexobj(SCREAMING_SNAKE_CASE_     )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(SCREAMING_SNAKE_CASE_      ,      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.

					__lowerCAmelCase				   =       False
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       1E12

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

										# Check convergence.
										__lowerCAmelCase				   =       np.abs(lambda_ - lambda_previous     ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															__lowerCAmelCase				   =       True

										__lowerCAmelCase				   =       lambda_

					if is_complex:
										__lowerCAmelCase				   =       np.real(lambda_     )

					return lambda_, vector







def 	_a      (						):
					__lowerCAmelCase				   =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
					__lowerCAmelCase				   =       np.array([41, 4, 20]     )
					__lowerCAmelCase				   =       real_input_matrix.astype(np.complexaaa     )
					__lowerCAmelCase				   =       np.triu(1j * complex_input_matrix      ,      1     )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					__lowerCAmelCase				   =       np.array([41, 4, 20]     ).astype(np.complexaaa     )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															__lowerCAmelCase				   =       real_input_matrix
															__lowerCAmelCase				   =       real_vector
										elif problem_type == "complex":
															__lowerCAmelCase				   =       complex_input_matrix
															__lowerCAmelCase				   =       complex_vector

										# Our implementation.
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       power_iteration(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       np.linalg.eigh(SCREAMING_SNAKE_CASE_     )
										# Last eigenvalue is the maximum one.
										__lowerCAmelCase				   =       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										__lowerCAmelCase				   =       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(SCREAMING_SNAKE_CASE_     ) - np.abs(SCREAMING_SNAKE_CASE_     )     ) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()






"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


