


from typing import Any

import numpy as np


class     __lowerCAmelCase     :






  """simple docstring"""





  def __init__(     self       :       str    ,				_lowerCAmelCase       :       np.ndarray    ,				_lowerCAmelCase       :       int  )   -> None:




     """simple docstring"""
     # Check if RHS is negative
     if np.any(tableau[:, -1]    ,				where=tableau[:, -1] < 0  ):
        raise ValueError("RHS must be > 0"  )

     snake_case_					    =					tableau
     snake_case_    ,    snake_case_					    =					tableau.shape

     # Number of decision variables x1, x2, x3...
     snake_case_					    =					n_vars

     # Number of artificial variables to be minimised
     snake_case_					    =					len(np.where(tableau[self.n_vars : -1] == -1  )[0]  )

     # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
     snake_case_					    =					(self.n_art_vars > 0) + 1

     # Number of slack variables added to make inequalities into equalities
     snake_case_					    =					self.n_rows - self.n_stages

     # Objectives for each stage
     snake_case_					    =					["max"]

     # In two stage simplex, first minimise then maximise
     if self.n_art_vars:
        self.objectives.append("min"  )

     snake_case_					    =					[""]

     # Index of current pivot row and column
     snake_case_					    =					None
     snake_case_					    =					None

     # Does objective row only contain (non)-negative values?
     snake_case_					    =					False



  @staticmethod
  def 					lowerCAmelCase__  (     *_lowerCAmelCase       :       int  )   -> list[str]:




     """simple docstring"""
     if len(_lowerCAmelCase  ) != 3:
        raise ValueError("Must provide n_vars, n_slack, and n_art_vars"  )

     if not all(x >= 0 and isinstance(_lowerCAmelCase    ,				_lowerCAmelCase  ) for x in args  ):
        raise ValueError("All arguments must be non-negative integers"  )

     # decision | slack | artificial
     snake_case_					    =					["x", "s", "a"]
     snake_case_					    =					[]
     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 					lowerCAmelCase__  (     self       :       int    ,				_lowerCAmelCase       :       np.ndarray  )   -> tuple[Any, Any]:




     """simple docstring"""
     snake_case_					    =					self.objectives[-1]

     # Find entries of highest magnitude in objective rows
     snake_case_					    =					(objective == "min") - (objective == "max")
     snake_case_					    =					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:
        snake_case_					    =					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
     snake_case_					    =					slice(self.n_stages    ,				self.n_rows  )

     # RHS
     snake_case_					    =					tableau[s, -1]

     # Elements of pivot column within slice
     snake_case_					    =					tableau[s, col_idx]

     # Array filled with nans
     snake_case_					    =					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
     snake_case_					    =					np.divide(_lowerCAmelCase    ,				_lowerCAmelCase    ,				out=_lowerCAmelCase    ,				where=divisor > 0  )

     # Arg of minimum quotient excluding the nan values. n_stages is added
     # to compensate for earlier exclusion of objective columns
     snake_case_					    =					np.nanargmin(_lowerCAmelCase  ) + self.n_stages
     return row_idx, col_idx



  def 					lowerCAmelCase__  (     self       :       int    ,				_lowerCAmelCase       :       np.ndarray    ,				_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       int  )   -> np.ndarray:




     """simple docstring"""
     snake_case_					    =					tableau[row_idx].copy()

     snake_case_					    =					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
     snake_case_					    =					piv_row
     return tableau



  def 					lowerCAmelCase__  (     self       :       str    ,				_lowerCAmelCase       :       np.ndarray  )   -> np.ndarray:




     """simple docstring"""
     self.objectives.pop()

     if not self.objectives:
        return tableau

     # Slice containing ids for artificial columns
     snake_case_					    =					slice(-self.n_art_vars - 1    ,				-1  )

     # Delete the artificial variable columns
     snake_case_					    =					np.delete(_lowerCAmelCase    ,				_lowerCAmelCase    ,				axis=1  )

     # Delete the objective row of the first stage
     snake_case_					    =					np.delete(_lowerCAmelCase    ,				0    ,				axis=0  )

     snake_case_					    =					1
     self.n_rows -= 1
     snake_case_					    =					0
     snake_case_					    =					False
     return tableau



  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> dict[Any, Any]:




     """simple docstring"""
     # Stop simplex algorithm from cycling.
     for _ in range(1_0_0  ):
        # Completion of each stage removes an objective. If both stages
        # are complete, then no objectives are left
        if not self.objectives:
           snake_case_					    =					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  )

        snake_case_    ,    snake_case_					    =					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
           snake_case_					    =					self.change_stage(self.tableau  )
        else:
           snake_case_					    =					self.pivot(self.tableau    ,				_lowerCAmelCase    ,				_lowerCAmelCase  )
     return {}



  def 					lowerCAmelCase__  (     self       :       str    ,				_lowerCAmelCase       :       np.ndarray    ,				_lowerCAmelCase       :       list[str]  )   -> dict[str, float]:




     """simple docstring"""
     snake_case_					    =					{"P": abs(tableau[0, -1]  )}

     for i in range(self.n_vars  ):
        # Gives ids of nonzero entries in the ith column
        snake_case_					    =					np.nonzero(tableau[:, i]  )
        snake_case_					    =					len(nonzero[0]  )

        # First entry in the nonzero ids
        snake_case_					    =					nonzero[0][0]
        snake_case_					    =					tableau[nonzero_rowidx, i]

        # If there is only one nonzero value in column, which is one
        if n_nonzero == nonzero_val == 1:
           snake_case_					    =					tableau[nonzero_rowidx, -1]
           snake_case_					    =					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(_lowerCAmelCase    ,				0  )
     return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()








import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
   pass

if is_speech_available():
   from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     random.Random()



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:Optional[int]=1.0		,						lowerCAmelCase_							:List[str]=None		,						lowerCAmelCase_							:str=None							)->Union[str, Any]:
   '''simple docstring'''






   if rng is None:
      snake_case_					    =					global_rng

   snake_case_					    =					[]
   for batch_idx in range(shape[0]							):
      values.append([]							)
      for _ in range(shape[1]							):
         values[-1].append(rng.random() * scale							)

   return values



@require_torch
@require_sentencepiece
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					"facebook/musicgen-small"
     snake_case_					    =					tempfile.mkdtemp()



  def 					lowerCAmelCase__  (     self       :       Tuple    ,				**_lowerCAmelCase       :       Tuple  )   -> Optional[int]:




     """simple docstring"""
     return TaTokenizer.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				**_lowerCAmelCase       :       Union[str, Any]  )   -> int:




     """simple docstring"""
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Optional[int]:




     """simple docstring"""
     shutil.rmtree(self.tmpdirname  )



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_tokenizer()
     snake_case_					    =					self.get_feature_extractor()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     processor.save_pretrained(self.tmpdirname  )
     snake_case_					    =					MusicgenProcessor.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 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					MusicgenProcessor(tokenizer=self.get_tokenizer()    ,				feature_extractor=self.get_feature_extractor()  )
     processor.save_pretrained(self.tmpdirname  )

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

     snake_case_					    =					MusicgenProcessor.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 					lowerCAmelCase__  (     self       :       List[str]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					floats_list((3, 1_0_0_0)  )

     snake_case_					    =					feature_extractor(_lowerCAmelCase    ,				return_tensors="np"  )
     snake_case_					    =					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 					lowerCAmelCase__  (     self       :       Tuple  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					"This is a test string"

     snake_case_					    =					processor(text=_lowerCAmelCase  )

     snake_case_					    =					tokenizer(_lowerCAmelCase  )

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



  def 					lowerCAmelCase__  (     self       :       Any  )   -> str:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

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

     snake_case_					    =					processor.batch_decode(sequences=_lowerCAmelCase  )
     snake_case_					    =					tokenizer.batch_decode(_lowerCAmelCase  )

     self.assertListEqual(_lowerCAmelCase    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(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"    ,				)



  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> int:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor(padding_side="left"  )
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[floats_list((1, x)  )[0] for x in range(5    ,				2_0    ,				5  )]
     snake_case_					    =					processor(_lowerCAmelCase  ).padding_mask

     snake_case_					    =					np.asarray(floats_list((3, 2_0)  )  )[:, None, :]
     snake_case_					    =					processor.batch_decode(_lowerCAmelCase    ,				padding_mask=_lowerCAmelCase  )

     self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )

     for audio in decoded_audios:
        self.assertIsInstance(_lowerCAmelCase    ,				np.ndarray  )

     self.assertTrue(decoded_audios[0].shape == (1, 1_0)  )
     self.assertTrue(decoded_audios[1].shape == (1, 1_5)  )
     self.assertTrue(decoded_audios[2].shape == (1, 2_0)  )




