
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 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						'<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(' '  )
							lowerCAmelCase__ : Optional[int]   =						dict(zip(a       ,	range(len(a  )  )  )  )

							lowerCAmelCase__ : Tuple   =						{
							    'pad_token': '<pad>',
							    'unk_token': '<unk>',
							    'bos_token': '<s>',
							    'eos_token': '</s>',
							}
							lowerCAmelCase__ : List[str]   =						{
							    'feature_size': 1,
							    'padding_value': 0.0,
							    'sampling_rate': 16_000,
							    'return_attention_mask': False,
							    'do_normalize': True,
							}

							lowerCAmelCase__ : List[Any]   =						tempfile.mkdtemp()
							lowerCAmelCase__ : Optional[int]   =						os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']  )
							lowerCAmelCase__ : Optional[Any]   =						os.path.join(self.tmpdirname       ,	a  )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'  ) as fp:
										fp.write(json.dumps(a  ) + '\n'  )

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






				def 				_lowerCamelCase						(		self   : str       ,	**a   : Union[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						self.add_kwargs_tokens_map.copy()
							kwargs.update(a  )
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,	**a  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	**a   : str  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							shutil.rmtree(self.tmpdirname  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self.get_tokenizer()
							lowerCAmelCase__ : Optional[int]   =						self.get_feature_extractor()

							lowerCAmelCase__ : Union[str, Any]   =						WavaVecaProcessor(tokenizer=a       ,	feature_extractor=a  )

							processor.save_pretrained(self.tmpdirname  )
							lowerCAmelCase__ : Union[str, Any]   =						WavaVecaProcessor.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 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()  )
							processor.save_pretrained(self.tmpdirname  )

							lowerCAmelCase__ : Any   =						self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'  )
							lowerCAmelCase__ : Optional[int]   =						self.get_feature_extractor(do_normalize=a       ,	padding_value=1.0  )

							lowerCAmelCase__ : Dict   =						WavaVecaProcessor.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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						self.get_feature_extractor()
							lowerCAmelCase__ : Any   =						self.get_tokenizer()

							lowerCAmelCase__ : Dict   =						WavaVecaProcessor(tokenizer=a       ,	feature_extractor=a  )

							lowerCAmelCase__ : Union[str, Any]   =						floats_list((3, 1_000)  )

							lowerCAmelCase__ : Union[str, Any]   =						feature_extractor(a       ,	return_tensors='np'  )
							lowerCAmelCase__ : Optional[int]   =						processor(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 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.get_feature_extractor()
							lowerCAmelCase__ : int   =						self.get_tokenizer()

							lowerCAmelCase__ : int   =						WavaVecaProcessor(tokenizer=a       ,	feature_extractor=a  )

							lowerCAmelCase__ : Union[str, Any]   =						'This is a test string'

							lowerCAmelCase__ : Optional[Any]   =						processor(text=a  )

							lowerCAmelCase__ : Dict   =						tokenizer(a  )

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






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						self.get_feature_extractor()
							lowerCAmelCase__ : Dict   =						self.get_tokenizer()

							lowerCAmelCase__ : Tuple   =						WavaVecaProcessor(tokenizer=a       ,	feature_extractor=a  )

							lowerCAmelCase__ : Optional[Any]   =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCAmelCase__ : Dict   =						processor.batch_decode(a  )
							lowerCAmelCase__ : Tuple   =						tokenizer.batch_decode(a  )

							self.assertListEqual(a       ,	a  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						self.get_feature_extractor()
							lowerCAmelCase__ : int   =						self.get_tokenizer()

							lowerCAmelCase__ : Tuple   =						WavaVecaProcessor(tokenizer=a       ,	feature_extractor=a  )

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

from typing import Any

import numpy as np
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						bool:
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[1]

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

			# Get eigenvalues and eignevectors for a symmetric matrix.
			lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						np.linalg.eigh(SCREAMING_SNAKE_CASE_	)

			# Check sign of all eigenvalues.
			# np.all returns a value of type np.bool_
			return bool(np.all(eigen_values > 0	)	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Any:
			lowerCAmelCase__ : List[Any]   =						np.random.randn(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Any   =						np.dot(SCREAMING_SNAKE_CASE_			,				random_matrix.T	)
			assert _is_matrix_spd(SCREAMING_SNAKE_CASE_	)
			return spd_matrix
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 1_000			,				SCREAMING_SNAKE_CASE_ = 1e-8			,				)							->						Any:
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[1]
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[0]
			assert _is_matrix_spd(SCREAMING_SNAKE_CASE_	)

			# Initialize solution guess, residual, search direction.
			lowerCAmelCase__ : Optional[Any]   =						np.zeros((np.shape(SCREAMING_SNAKE_CASE_	)[0], 1)	)
			lowerCAmelCase__ : str   =						np.copy(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						np.copy(SCREAMING_SNAKE_CASE_	)

			# Set initial errors in solution guess and residual.
			lowerCAmelCase__ : Union[str, Any]   =						1e9
			lowerCAmelCase__ : int   =						1e9
			lowerCAmelCase__ : Union[str, Any]   =						1e9

			# Set iteration counter to threshold number of iterations.
			lowerCAmelCase__ : List[str]   =						0

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

						# The main algorithm.

						# Update search direction magnitude.
						lowerCAmelCase__ : List[str]   =						np.dot(ra.T			,				SCREAMING_SNAKE_CASE_	) / np.dot(pa.T			,				SCREAMING_SNAKE_CASE_	)
						# Update solution guess.
						lowerCAmelCase__ : Dict   =						xa + alpha * pa
						# Calculate new residual.
						lowerCAmelCase__ : Tuple   =						ra - alpha * w
						# Calculate new Krylov subspace scale.
						lowerCAmelCase__ : str   =						np.dot(r.T			,				SCREAMING_SNAKE_CASE_	) / np.dot(ra.T			,				SCREAMING_SNAKE_CASE_	)
						# Calculate new A conjuage search direction.
						lowerCAmelCase__ : Dict   =						r + beta * pa

						# Calculate errors.
						lowerCAmelCase__ : List[str]   =						np.linalg.norm(r - ra	)
						lowerCAmelCase__ : Tuple   =						np.linalg.norm(x - xa	)
						lowerCAmelCase__ : str   =						np.maximum(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

						# Update variables.
						lowerCAmelCase__ : List[Any]   =						np.copy(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : Dict   =						np.copy(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : str   =						np.copy(SCREAMING_SNAKE_CASE_	)

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

			return x
def 					lowerCAmelCase__     (    )							->						None:
			lowerCAmelCase__ : Optional[Any]   =						3
			lowerCAmelCase__ : Optional[int]   =						_create_spd_matrix(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Tuple   =						np.random.randn(SCREAMING_SNAKE_CASE_			,				1	)
			lowerCAmelCase__ : int   =						np.dot(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Numpy solution.
			lowerCAmelCase__ : Optional[Any]   =						np.linalg.solve(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Our implementation.
			lowerCAmelCase__ : Optional[int]   =						conjugate_gradient(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# 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()