code: 
  from typing import Any
  
  import numpy as np
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   List[Any]			,  snake_case							:   np.ndarray			,  snake_case							:   int						)   -> None:
  							# Check if RHS is negative
  							if np.any(tableau[:, -1]			,  where=tableau[:, -1] < 0						):
  													raise ValueError("RHS must be > 0"						)
  
  							_lowercase					:  Any    			=      tableau
  							_lowercase			,						_lowercase					:  Optional[Any]    			=      tableau.shape
  
  							# Number of decision variables x1, x2, x3...
  							_lowercase					:  Any    			=      n_vars
  
  							# Number of artificial variables to be minimised
  							_lowercase					:  Optional[int]    			=      len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)
  
  							# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  							_lowercase					:  List[str]    			=      (self.n_art_vars > 0) + 1
  
  							# Number of slack variables added to make inequalities into equalities
  							_lowercase					:  Any    			=      self.n_rows - self.n_stages
  
  							# Objectives for each stage
  							_lowercase					:  Dict    			=      ["max"]
  
  							# In two stage simplex, first minimise then maximise
  							if self.n_art_vars:
  													self.objectives.append("min"						)
  
  							_lowercase					:  Union[str, Any]    			=      [""]
  
  							# Index of current pivot row and column
  							_lowercase					:  Any    			=      None
  							_lowercase					:  Tuple    			=      None
  
  							# Does objective row only contain (non)-negative values?
  							_lowercase					:  int    			=      False
  
  
  
  
  
  
  	@staticmethod
  	def 			_a(*snake_case							:   int						)   -> 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
  							_lowercase					:  List[str]    			=      ["x", "s", "a"]
  							_lowercase					:  Dict    			=      []
  							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							:   Optional[int]			,  snake_case							:   np.ndarray						)   -> tuple[Any, Any]:
  							_lowercase					:  List[str]    			=      self.objectives[-1]
  
  							# Find entries of highest magnitude in objective rows
  							_lowercase					:  Dict    			=      (objective == "min") - (objective == "max")
  							_lowercase					:  int    			=      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:
  													_lowercase					:  str    			=      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
  							_lowercase					:  Union[str, Any]    			=      slice(self.n_stages			,  self.n_rows						)
  
  							# RHS
  							_lowercase					:  str    			=      tableau[s, -1]
  
  							# Elements of pivot column within slice
  							_lowercase					:  Optional[Any]    			=      tableau[s, col_idx]
  
  							# Array filled with nans
  							_lowercase					:  str    			=      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
  							_lowercase					:  int    			=      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
  							_lowercase					:  List[str]    			=      np.nanargmin(snake_case						) + self.n_stages
  							return row_idx, col_idx
  
  
  
  
  
  
  	def 			_a(self							:   Any			,  snake_case							:   np.ndarray			,  snake_case							:   int			,  snake_case							:   int						)   -> np.ndarray:
  							_lowercase					:  Any    			=      tableau[row_idx].copy()
  
  							_lowercase					:  Union[str, 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
  							_lowercase					:  List[str]    			=      piv_row
  							return tableau
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   np.ndarray						)   -> np.ndarray:
  							self.objectives.pop()
  
  							if not self.objectives:
  													return tableau
  
  							# Slice containing ids for artificial columns
  							_lowercase					:  List[Any]    			=      slice(-self.n_art_vars - 1			,  -1						)
  
  							# Delete the artificial variable columns
  							_lowercase					:  List[Any]    			=      np.delete(snake_case			,  snake_case			,  axis=1						)
  
  							# Delete the objective row of the first stage
  							_lowercase					:  List[str]    			=      np.delete(snake_case			,  0			,  axis=0						)
  
  							_lowercase					:  List[str]    			=      1
  							self.n_rows -= 1
  							_lowercase					:  Union[str, Any]    			=      0
  							_lowercase					:  Optional[int]    			=      False
  							return tableau
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> 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:
  																			_lowercase					:  List[str]    			=      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						)
  
  													_lowercase			,						_lowercase					:  Tuple    			=      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
  																			_lowercase					:  int    			=      self.change_stage(self.tableau						)
  													else:
  																			_lowercase					:  Tuple    			=      self.pivot(self.tableau			,  snake_case			,  snake_case						)
  							return {}
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   np.ndarray			,  snake_case							:   list[str]						)   -> dict[str, float]:
  							_lowercase					:  Optional[Any]    			=      {"P": abs(tableau[0, -1]						)}
  
  							for i in range(self.n_vars						):
  													# Gives ids of nonzero entries in the ith column
  													_lowercase					:  Tuple    			=      np.nonzero(tableau[:, i]						)
  													_lowercase					:  Any    			=      len(nonzero[0]						)
  
  													# First entry in the nonzero ids
  													_lowercase					:  Any    			=      nonzero[0][0]
  													_lowercase					:  Tuple    			=      tableau[nonzero_rowidx, i]
  
  													# If there is only one nonzero value in column, which is one
  													if n_nonzero == nonzero_val == 1:
  																			_lowercase					:  str    			=      tableau[nonzero_rowidx, -1]
  																			_lowercase					:  List[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()
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  							import tensorflow as tf
  
  							from transformers import (
  							    TFDebertaForMaskedLM,
  							    TFDebertaForQuestionAnswering,
  							    TFDebertaForSequenceClassification,
  							    TFDebertaForTokenClassification,
  							    TFDebertaModel,
  							)
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   List[Any]=13			,  snake_case							:   Union[str, Any]=7			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   int=True			,  snake_case							:   Dict=True			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   str=99			,  snake_case							:   str=32			,  snake_case							:   List[Any]=2			,  snake_case							:   Optional[int]=4			,  snake_case							:   Union[str, Any]=37			,  snake_case							:   int="gelu"			,  snake_case							:   Any=0.1			,  snake_case							:   str=0.1			,  snake_case							:   Dict=512			,  snake_case							:   Any=16			,  snake_case							:   Tuple=2			,  snake_case							:   Dict=0.02			,  snake_case							:   Tuple=3			,  snake_case							:   Dict=4			,  snake_case							:   Optional[Any]=None			,  )   -> str:
  							_lowercase					:  Optional[int]    			=      parent
  							_lowercase					:  Union[str, Any]    			=      13
  							_lowercase					:  Dict    			=      7
  							_lowercase					:  Any    			=      True
  							_lowercase					:  Tuple    			=      True
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  str    			=      99
  							_lowercase					:  Optional[int]    			=      32
  							_lowercase					:  int    			=      2
  							_lowercase					:  Optional[int]    			=      4
  							_lowercase					:  List[str]    			=      37
  							_lowercase					:  Dict    			=      "gelu"
  							_lowercase					:  str    			=      0.1
  							_lowercase					:  Optional[int]    			=      0.1
  							_lowercase					:  int    			=      512
  							_lowercase					:  str    			=      16
  							_lowercase					:  List[Any]    			=      False
  							_lowercase					:  Dict    			=      -1
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  Dict    			=      2
  							_lowercase					:  Union[str, Any]    			=      0.02
  							_lowercase					:  int    			=      3
  							_lowercase					:  Tuple    			=      4
  							_lowercase					:  Optional[Any]    			=      None
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> List[Any]:
  							_lowercase					:  Optional[Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size						)
  
  							_lowercase					:  Optional[Any]    			=      None
  							if self.use_input_mask:
  													_lowercase					:  int    			=      random_attention_mask([self.batch_size, self.seq_length]						)
  
  							_lowercase					:  Dict    			=      None
  							if self.use_token_type_ids:
  													_lowercase					:  Any    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size						)
  
  							_lowercase					:  Optional[Any]    			=      None
  							_lowercase					:  Tuple    			=      None
  							_lowercase					:  List[str]    			=      None
  							if self.use_labels:
  													_lowercase					:  List[Any]    			=      ids_tensor([self.batch_size]			,  self.type_sequence_label_size						)
  													_lowercase					:  Union[str, Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels						)
  
  							_lowercase					:  Union[str, Any]    			=      DebertaConfig(
  							    vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  num_attention_heads=self.num_attention_heads			,  intermediate_size=self.intermediate_size			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  max_position_embeddings=self.max_position_embeddings			,  type_vocab_size=self.type_vocab_size			,  relative_attention=self.relative_attention			,  max_relative_positions=self.max_relative_positions			,  position_biased_input=self.position_biased_input			,  initializer_range=self.initializer_range			,  return_dict=snake_case			,  )
  
  							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   List[Any]			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   List[Any]			,  snake_case							:   Union[str, Any]						)   -> str:
  							_lowercase					:  Tuple    			=      TFDebertaModel(config=snake_case						)
  							_lowercase					:  Any    			=      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  
  							_lowercase					:  Tuple    			=      [input_ids, input_mask]
  							_lowercase					:  Any    			=      model(snake_case						)
  
  							_lowercase					:  Any    			=      model(snake_case						)
  
  							self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   int			,  snake_case							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Tuple			,  snake_case							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   Union[str, Any]			,  snake_case							:   int						)   -> int:
  							_lowercase					:  int    			=      TFDebertaForMaskedLM(config=snake_case						)
  							_lowercase					:  List[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  							_lowercase					:  int    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   Dict			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   Tuple			,  snake_case							:   int			,  snake_case							:   Any			,  snake_case							:   Any						)   -> List[str]:
  							_lowercase					:  Dict    			=      self.num_labels
  							_lowercase					:  Tuple    			=      TFDebertaForSequenceClassification(config=snake_case						)
  							_lowercase					:  Optional[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  
  							_lowercase					:  Dict    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   int			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   List[str]						)   -> Dict:
  							_lowercase					:  Union[str, Any]    			=      self.num_labels
  							_lowercase					:  List[Any]    			=      TFDebertaForTokenClassification(config=snake_case						)
  							_lowercase					:  Union[str, Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  							_lowercase					:  str    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   Optional[Any]						)   -> Optional[Any]:
  							_lowercase					:  str    			=      TFDebertaForQuestionAnswering(config=snake_case						)
  							_lowercase					:  Optional[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  
  							_lowercase					:  Tuple    			=      model(snake_case						)
  							self.parent.assertEqual(result.start_logits.shape			,  (self.batch_size, self.seq_length)						)
  							self.parent.assertEqual(result.end_logits.shape			,  (self.batch_size, self.seq_length)						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> Optional[Any]:
  							_lowercase					:  int    			=      self.prepare_config_and_inputs()
  							(
  							    (
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						
  							)					:  Optional[Any]    			=      config_and_inputs
  							_lowercase					:  str    			=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  							return config, inputs_dict
  
  
  
  
  @require_tf
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_A   			=      (
  	    (
  	        TFDebertaModel,
  	        TFDebertaForMaskedLM,
  	        TFDebertaForQuestionAnswering,
  	        TFDebertaForSequenceClassification,
  	        TFDebertaForTokenClassification,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	_A   			=      (
  	    {
  	        "feature-extraction": TFDebertaModel,
  	        "fill-mask": TFDebertaForMaskedLM,
  	        "question-answering": TFDebertaForQuestionAnswering,
  	        "text-classification": TFDebertaForSequenceClassification,
  	        "token-classification": TFDebertaForTokenClassification,
  	        "zero-shot": TFDebertaForSequenceClassification,
  	    }
  	    if is_tf_available()
  	    else {}
  	)
  
  	_A   			=      False
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> List[Any]:
  							_lowercase					:  List[str]    			=      TFDebertaModelTester(self						)
  							_lowercase					:  Optional[int]    			=      ConfigTester(self			,  config_class=snake_case			,  hidden_size=37						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> Tuple:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Dict:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Any:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> int:
  							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> List[Any]:
  							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> str:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*snake_case						)
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Optional[Any]						)   -> int:
  							_lowercase					:  Optional[int]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
  							self.assertIsNotNone(snake_case						)
  
  
  
  
  @require_tf
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@unittest.skip(reason="Model not available yet"						)
  	def 			_a(self							:   Optional[int]						)   -> int:
  							pass
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Optional[int]						)   -> Optional[int]:
  							_lowercase					:  Optional[Any]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
  							_lowercase					:  List[Any]    			=      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
  							_lowercase					:  Union[str, Any]    			=      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
  							_lowercase					:  List[str]    			=      model(snake_case			,  attention_mask=snake_case						)[0]
  
  							_lowercase					:  List[str]    			=      tf.constant(
  							    [
  							        [
  							            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
  							            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
  							            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
  							        ]
  							    ]						)
  							tf.debugging.assert_near(output[:, 1:4, 1:4]			,  snake_case			,  atol=1e-4						)
  
  
  
  
  