code: 
  
  
  
  
  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 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     ='''<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(''' '''							)
  											SCREAMING_SNAKE_CASE			: Dict	     =dict(zip(snake_case_		,		range(len(snake_case_							)							)							)							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     ={
  											    '''pad_token''': '''<pad>''',
  											    '''unk_token''': '''<unk>''',
  											    '''bos_token''': '''<s>''',
  											    '''eos_token''': '''</s>''',
  											}
  											SCREAMING_SNAKE_CASE			: Tuple	     ={
  											    '''feature_size''': 1,
  											    '''padding_value''': 0.0,
  											    '''sampling_rate''': 16_000,
  											    '''return_attention_mask''': False,
  											    '''do_normalize''': True,
  											}
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =tempfile.mkdtemp()
  											SCREAMING_SNAKE_CASE			: Tuple	     =os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''vocab_file''']							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(self.tmpdirname		,		snake_case_							)
  											with open(self.vocab_file		,		'''w'''		,		encoding='''utf-8'''							) as fp:
  																		fp.write(json.dumps(snake_case_							) + '''\n'''							)
  
  											with open(self.feature_extraction_file		,		'''w'''		,		encoding='''utf-8'''							) as fp:
  																		fp.write(json.dumps(snake_case_							) + '''\n'''							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.add_kwargs_tokens_map.copy()
  											kwargs.update(snake_case_							)
  											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Tuple:
  											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,		**snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											shutil.rmtree(self.tmpdirname							)
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_tokenizer()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.get_feature_extractor()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											processor.save_pretrained(self.tmpdirname							)
  											SCREAMING_SNAKE_CASE			: Any	     =WavaVecaProcessor.from_pretrained(self.tmpdirname							)
  
  											self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab()							)
  											self.assertIsInstance(processor.tokenizer		,		snake_case_							)
  
  											self.assertEqual(processor.feature_extractor.to_json_string()		,		feature_extractor.to_json_string()							)
  											self.assertIsInstance(processor.feature_extractor		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     =WavaVecaProcessor(tokenizer=self.get_tokenizer()		,		feature_extractor=self.get_feature_extractor()							)
  											processor.save_pretrained(self.tmpdirname							)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.get_tokenizer(bos_token='''(BOS)'''		,		eos_token='''(EOS)'''							)
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_feature_extractor(do_normalize=snake_case_		,		padding_value=1.0							)
  
  											SCREAMING_SNAKE_CASE			: int	     =WavaVecaProcessor.from_pretrained(
  											    self.tmpdirname		,		bos_token='''(BOS)'''		,		eos_token='''(EOS)'''		,		do_normalize=snake_case_		,		padding_value=1.0							)
  
  											self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab()							)
  											self.assertIsInstance(processor.tokenizer		,		snake_case_							)
  
  											self.assertEqual(processor.feature_extractor.to_json_string()		,		feature_extractor_add_kwargs.to_json_string()							)
  											self.assertIsInstance(processor.feature_extractor		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =floats_list((3, 1_000)							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =feature_extractor(snake_case_		,		return_tensors='''np'''							)
  											SCREAMING_SNAKE_CASE			: Any	     =processor(snake_case_		,		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]:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: Dict	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: int	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ='''This is a test string'''
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =tokenizer(snake_case_							)
  
  											for key in encoded_tok.keys():
  																		self.assertListEqual(encoded_tok[key]		,		encoded_processor[key]							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =processor.batch_decode(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tokenizer.batch_decode(snake_case_							)
  
  											self.assertListEqual(snake_case_		,		snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_feature_extractor()
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Dict	     =WavaVecaProcessor(tokenizer=snake_case_		,		feature_extractor=snake_case_							)
  
  											self.assertListEqual(
  											    processor.model_input_names		,		feature_extractor.model_input_names		,		msg='''`processor` and `feature_extractor` model input names do not match'''		,		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)
  
  											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1
  
  											# Number of slack variables added to make inequalities into equalities
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages
  
  											# Objectives for each stage
  											SCREAMING_SNAKE_CASE			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: int	     =[]
  											for i in range(3							):
  																		for j in range(args[i]							):
  																									titles.append(string_starts[i] + str(j + 1							)							)
  											titles.append('''RHS'''							)
  											return titles
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.argmax(sign * tableau[0, : self.n_vars]							)
  
  											# Choice is only valid if below 0 for maximise, and above for minimise
  											if sign * self.tableau[0, col_idx] <= 0:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =True
  																		return 0, 0
  
  											# Pivot row is chosen as having the lowest quotient when elements of
  											# the pivot column divide the right-hand side
  
  											# Slice excluding the objective rows
  											SCREAMING_SNAKE_CASE			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.full(self.n_rows - self.n_stages		,		np.nan							)
  
  											# If element in pivot column is greater than zeron_stages, return
  											# quotient or nan otherwise
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=snake_case_		,		where=divisor > 0							)
  
  											# Arg of minimum quotient excluding the nan values. n_stages is added
  											# to compensate for earlier exclusion of objective columns
  											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =piv_row[col_idx]
  
  											# Entry becomes 1
  											piv_row *= 1 / piv_val
  
  											# Variable in pivot column becomes basic, ie the only non-zero entry
  											for idx, coeff in enumerate(tableau[:, col_idx]							):
  																		tableau[idx] += -coeff * piv_row
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( self							)    ->      dict[Any, Any]:
  											# Stop simplex algorithm from cycling.
  											for _ in range(100							):
  																		# Completion of each stage removes an objective. If both stages
  																		# are complete, then no objectives are left
  																		if not self.objectives:
  																									SCREAMING_SNAKE_CASE			: int	     =self.generate_col_titles(
  																									    self.n_vars		,		self.n_slack		,		self.n_art_vars							)
  
  																									# Find the values of each variable at optimal solution
  																									return self.interpret_tableau(self.tableau		,		self.col_titles							)
  
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =self.find_pivot(self.tableau							)
  
  																		# If there are no more negative values in objective row
  																		if self.stop_iter:
  																									# Delete artificial variable columns and rows. Update attributes
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: Any	     =tableau[nonzero_rowidx, i]
  
  																		# If there is only one nonzero value in column, which is one
  																		if n_nonzero == nonzero_val == 1:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =rhs_val
  
          # Check for basic variables
  											for title in col_titles:
  																		# Don't add RHS or slack variables to output dict
  																		if title[0] not in "R-s-a":
  																									output_dict.setdefault(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  