




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'''		,		)












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()






