



'''simple docstring'''







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class 				__lowerCAmelCase(							lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		str		=      SpeechaTextaTokenizer
			__snake_case  :		str		=      False






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					super().setUp()

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ['he re</w> 123', 'here a 1456']
					SCREAMING_SNAKE_CASE_						:str										=      dict(zip(SCREAMING_SNAKE_CASE      ,       range(len(SCREAMING_SNAKE_CASE	)	)	)	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

					SCREAMING_SNAKE_CASE_						:List[str]										=      tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE_						:Tuple										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file']	)
					SCREAMING_SNAKE_CASE_						:int										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file']	)

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

					with open(self.merges_file      ,       'w'	) as fp:
							fp.write('\n'.join(SCREAMING_SNAKE_CASE	)	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      list(self.get_tokenizer().get_vocab().keys()	)

					self.assertEqual(vocab_keys[0]      ,       '<s>'	)
					self.assertEqual(vocab_keys[1]      ,       '<pad>'	)
					self.assertEqual(vocab_keys[-1]      ,       'vocab'	)
					self.assertEqual(len(SCREAMING_SNAKE_CASE	)      ,       14	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					self.assertEqual(self.get_tokenizer().vocab_size      ,       14	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

					# make sure @@ is correctly concatenated
					SCREAMING_SNAKE_CASE_						:Tuple										=      [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
					SCREAMING_SNAKE_CASE_						:str										=      tokenizer.decode(SCREAMING_SNAKE_CASE	)

					self.assertTrue(output_string == 'herecouple words ofthe'	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
							os.remove(os.path.join(SCREAMING_SNAKE_CASE      ,       'merges.txt'	)	)

							# load tokenizer without merges file should not throw an error
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							# save tokenizer and load again
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Any										=      SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

					self.assertIsNotNone(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					if not self.test_slow_tokenizer:
							return

					SCREAMING_SNAKE_CASE_						:int										=      inspect.signature(self.tokenizer_class.__init__	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_tokenizer()

					for parameter_name, parameter in signature.parameters.items():
							if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
									self.assertIn(SCREAMING_SNAKE_CASE      ,       tokenizer.init_kwargs	)







from typing import Any

import numpy as np





def  __UpperCamelCase   (     a)			->bool:
					assert np.shape(a)[0] == np.shape(a)[1]

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

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

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





def  __UpperCamelCase   (     a)			->Any:
					lowerCamelCase__      =							np.random.randn(a,  a)
					lowerCamelCase__      =							np.dot(a,  random_matrix.T)
					assert _is_matrix_spd(a)
					return spd_matrix





def  __UpperCamelCase   (     a,  a,  a = 1000,  a = 1E-8,  )			->Any:
					assert np.shape(a)[0] == np.shape(a)[1]
					assert np.shape(a)[0] == np.shape(a)[0]
					assert _is_matrix_spd(a)

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

					# Set initial errors in solution guess and residual.
					lowerCamelCase__      =							1E9
					lowerCamelCase__      =							1E9
					lowerCamelCase__      =							1E9

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

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

										# The main algorithm.

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

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

										# Update variables.
										lowerCamelCase__      =							np.copy(a)
										lowerCamelCase__      =							np.copy(a)
										lowerCamelCase__      =							np.copy(a)

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

					return x





def  __UpperCamelCase   (     )			->None:
					lowerCamelCase__      =							3
					lowerCamelCase__      =							_create_spd_matrix(a)
					lowerCamelCase__      =							np.random.randn(a,  1)
					lowerCamelCase__      =							np.dot(a,  a)

					# Numpy solution.
					lowerCamelCase__      =							np.linalg.solve(a,  a)

					# Our implementation.
					lowerCamelCase__      =							conjugate_gradient(a,  a)

					# 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()

