
'''simple docstring'''



from typing import Any

import numpy as np





def 	_A						(							lowercase__		):
						assert np.shape(__UpperCamelCase		)[0] == np.shape(__UpperCamelCase		)[1]

						# If matrix not symmetric, exit right away.
						if np.allclose(__UpperCamelCase						,						matrix.T		) is False:
												return False

						# Get eigenvalues and eignevectors for a symmetric matrix.
						lowercase__	,  lowercase__								=    np.linalg.eigh(__UpperCamelCase		)

						# Check sign of all eigenvalues.
						# np.all returns a value of type np.bool_
						return bool(np.all(eigen_values > 0		)		)





def 	_A						(							lowercase__		):
						lowercase__								=    np.random.randn(__UpperCamelCase						,						__UpperCamelCase		)
						lowercase__								=    np.dot(__UpperCamelCase						,						random_matrix.T		)
						assert _is_matrix_spd(__UpperCamelCase		)
						return spd_matrix





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__ = 1000						,						lowercase__ = 1e-8						,						):
						assert np.shape(__UpperCamelCase		)[0] == np.shape(__UpperCamelCase		)[1]
						assert np.shape(__UpperCamelCase		)[0] == np.shape(__UpperCamelCase		)[0]
						assert _is_matrix_spd(__UpperCamelCase		)

						# Initialize solution guess, residual, search direction.
						lowercase__								=    np.zeros((np.shape(__UpperCamelCase		)[0], 1)		)
						lowercase__								=    np.copy(__UpperCamelCase		)
						lowercase__								=    np.copy(__UpperCamelCase		)

						# Set initial errors in solution guess and residual.
						lowercase__								=    1e9
						lowercase__								=    1e9
						lowercase__								=    1e9

						# Set iteration counter to threshold number of iterations.
						lowercase__								=    0

						while error > tol:
												# Save this value so we only calculate the matrix-vector product once.
												lowercase__								=    np.dot(__UpperCamelCase						,						__UpperCamelCase		)

												# The main algorithm.

												# Update search direction magnitude.
												lowercase__								=    np.dot(ra.T						,						__UpperCamelCase		) / np.dot(pa.T						,						__UpperCamelCase		)
												# Update solution guess.
												lowercase__								=    xa + alpha * pa
												# Calculate new residual.
												lowercase__								=    ra - alpha * w
												# Calculate new Krylov subspace scale.
												lowercase__								=    np.dot(r.T						,						__UpperCamelCase		) / np.dot(ra.T						,						__UpperCamelCase		)
												# Calculate new A conjuage search direction.
												lowercase__								=    r + beta * pa

												# Calculate errors.
												lowercase__								=    np.linalg.norm(r - ra		)
												lowercase__								=    np.linalg.norm(x - xa		)
												lowercase__								=    np.maximum(__UpperCamelCase						,						__UpperCamelCase		)

												# Update variables.
												lowercase__								=    np.copy(__UpperCamelCase		)
												lowercase__								=    np.copy(__UpperCamelCase		)
												lowercase__								=    np.copy(__UpperCamelCase		)

												# Update number of iterations.
												iterations += 1
												if iterations > max_iterations:
																		break

						return x





def 	_A						(							):
						lowercase__								=    3
						lowercase__								=    _create_spd_matrix(__UpperCamelCase		)
						lowercase__								=    np.random.randn(__UpperCamelCase						,						1		)
						lowercase__								=    np.dot(__UpperCamelCase						,						__UpperCamelCase		)

						# Numpy solution.
						lowercase__								=    np.linalg.solve(__UpperCamelCase						,						__UpperCamelCase		)

						# Our implementation.
						lowercase__								=    conjugate_gradient(__UpperCamelCase						,						__UpperCamelCase		)

						# Ensure both solutions are close to x_true (and therefore one another).
						assert np.linalg.norm(x_numpy - x_true		) <= 1e-6
						assert np.linalg.norm(x_conjugate_gradient - x_true		) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_conjugate_gradient()





'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class      A							(    unittest.TestCase			):



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




									'''simple docstring'''



									lowercase__								=    """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """    )
									lowercase__								=    dict(zip(lowerCamelCase__    ,       range(len(lowerCamelCase__    )    )    )    )

									lowercase__								=    {
									    """pad_token""": """<pad>""",
									    """unk_token""": """<unk>""",
									    """bos_token""": """<s>""",
									    """eos_token""": """</s>""",
									}
									lowercase__								=    {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}

									lowercase__								=    tempfile.mkdtemp()
									lowercase__								=    os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
									lowercase__								=    os.path.join(self.tmpdirname    ,       lowerCamelCase__    )
									with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )

									with open(self.feature_extraction_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									lowercase__								=    self.add_kwargs_tokens_map.copy()
									kwargs.update(lowerCamelCase__    )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname    )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    self.get_tokenizer()
									lowercase__								=    self.get_feature_extractor()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									processor.save_pretrained(self.tmpdirname    )
									lowercase__								=    WavaVecaProcessor.from_pretrained(self.tmpdirname    )

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

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()    )
									processor.save_pretrained(self.tmpdirname    )

									lowercase__								=    self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    )
									lowercase__								=    self.get_feature_extractor(do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									lowercase__								=    WavaVecaProcessor.from_pretrained(
									    self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

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

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    floats_list((3, 1_000)    )

									lowercase__								=    feature_extractor(lowerCamelCase__    ,       return_tensors="""np"""    )
									lowercase__								=    processor(lowerCamelCase__    ,       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 A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    """This is a test string"""

									lowercase__								=    processor(text=lowerCamelCase__    )

									lowercase__								=    tokenizer(lowerCamelCase__    )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowercase__								=    processor.batch_decode(lowerCamelCase__    )
									lowercase__								=    tokenizer.batch_decode(lowerCamelCase__    )

									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

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






