






"""simple docstring"""







from typing import Any

import numpy as np


class     __UpperCAmelCase:




						"""simple docstring"""




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

												lowercase__				:      Dict=       tableau
												lowercase__,	lowercase__				:      Optional[Any]=       tableau.shape

												# Number of decision variables x1, x2, x3...
												lowercase__				:      Optional[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__				:      Tuple=       self.n_rows - self.n_stages

												# Objectives for each stage
												lowercase__				:      Optional[Any]=       ["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__				:      Tuple=       None
												lowercase__				:      List[Any]=       None

												# Does objective row only contain (non)-negative values?
												lowercase__				:      str=       False




						@staticmethod
						def 		UpperCAmelCase_       (   *snake_case__			):
												'''simple docstring'''
												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__				:      Optional[Any]=       ["x", "s", "a"]
												lowercase__				:      List[Any]=       []
												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 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      str=       self.objectives[-1]

												# Find entries of highest magnitude in objective rows
												lowercase__				:      Any=       (objective == "min") - (objective == "max")
												lowercase__				:      str=       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__				:      List[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__				:      str=       slice(self.n_stages       ,  self.n_rows			)

												# RHS
												lowercase__				:      Tuple=       tableau[s, -1]

												# Elements of pivot column within slice
												lowercase__				:      List[str]=       tableau[s, col_idx]

												# Array filled with nans
												lowercase__				:      Tuple=       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 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       tableau[row_idx].copy()

												lowercase__				:      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
												lowercase__				:      Dict=       piv_row
												return tableau




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												self.objectives.pop()

												if not self.objectives:
																		return tableau

												# Slice containing ids for artificial columns
												lowercase__				:      str=       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__				:      Any=       1
												self.n_rows -= 1
												lowercase__				:      str=       0
												lowercase__				:      Dict=       False
												return tableau




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# 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[Any]=       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__				:      Optional[int]=       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__				:      List[Any]=       self.change_stage(self.tableau			)
																		else:
																								lowercase__				:      Dict=       self.pivot(self.tableau       ,  snake_case__       ,  snake_case__			)
												return {}




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       {"P": abs(tableau[0, -1]			)}

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

																		# First entry in the nonzero ids
																		lowercase__				:      Dict=       nonzero[0][0]
																		lowercase__				:      Optional[int]=       tableau[nonzero_rowidx, i]

																		# If there is only one nonzero value in column, which is one
																		if n_nonzero == nonzero_val == 1:
																								lowercase__				:      int=       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()








from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
       TFMPNetForMaskedLM,
       TFMPNetForMultipleChoice,
       TFMPNetForQuestionAnswering,
       TFMPNetForSequenceClassification,
       TFMPNetForTokenClassification,
       TFMPNetModel,
   )







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    int				,    _A      :    Tuple=1_3				,    _A      :    List[str]=7				,    _A      :    Any=True				,    _A      :    Union[str, Any]=True				,    _A      :    Optional[Any]=False				,    _A      :    Tuple=True				,    _A      :    str=9_9				,    _A      :    Optional[Any]=6_4				,    _A      :    Optional[int]=2				,    _A      :    Union[str, Any]=4				,    _A      :    Any=6_4				,    _A      :    Any="gelu"				,    _A      :    str=0.1				,    _A      :    Union[str, Any]=0.1				,    _A      :    Optional[Any]=5_1_2				,    _A      :    int=1_6				,    _A      :    List[str]=2				,    _A      :    Optional[int]=0.02				,    _A      :    Optional[Any]=3				,    _A      :    List[str]=4				,    _A      :    int=None				,    )						->						List[Any]:
         snake_case      =					parent
         snake_case      =					batch_size
         snake_case      =					seq_length
         snake_case      =					is_training
         snake_case      =					use_input_mask
         snake_case      =					use_token_type_ids
         snake_case      =					use_labels
         snake_case      =					vocab_size
         snake_case      =					hidden_size
         snake_case      =					num_hidden_layers
         snake_case      =					num_attention_heads
         snake_case      =					intermediate_size
         snake_case      =					hidden_act
         snake_case      =					hidden_dropout_prob
         snake_case      =					attention_probs_dropout_prob
         snake_case      =					max_position_embeddings
         snake_case      =					type_vocab_size
         snake_case      =					type_sequence_label_size
         snake_case      =					initializer_range
         snake_case      =					num_labels
         snake_case      =					num_choices
         snake_case      =					scope





     def 		UpperCAmelCase(self      :    List[str]   )						->						Dict:
         snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size   )

         snake_case      =					None
         if self.use_input_mask:
             snake_case      =					random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case      =					None
         snake_case      =					None
         snake_case      =					None
         if self.use_labels:
             snake_case      =					ids_tensor([self.batch_size]				,    self.type_sequence_label_size   )
             snake_case      =					ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels   )
             snake_case      =					ids_tensor([self.batch_size]				,    self.num_choices   )

         snake_case      =					MPNetConfig(
             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				,    initializer_range=self.initializer_range				,    )
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    str				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    List[str]				,    _A      :    int   )						->						List[Any]:
         snake_case      =					TFMPNetModel(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         snake_case      =					[input_ids, input_mask]
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)   )





     def 		UpperCAmelCase(self      :    int				,    _A      :    str				,    _A      :    Optional[Any]				,    _A      :    Optional[Any]				,    _A      :    Union[str, Any]				,    _A      :    Optional[Any]				,    _A      :    Any   )						->						int:
         snake_case      =					TFMPNetForMaskedLM(config=_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)   )





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    Optional[int]				,    _A      :    int				,    _A      :    int				,    _A      :    List[str]				,    _A      :    Union[str, Any]				,    _A      :    Union[str, Any]   )						->						int:
         snake_case      =					TFMPNetForQuestionAnswering(config=_A   )
         snake_case      =					{
             "input_ids": input_ids,
             "attention_mask": input_mask,
         }
         snake_case      =					model(_A   )
         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 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    Dict				,    _A      :    Optional[int]				,    _A      :    str				,    _A      :    Optional[Any]   )						->						Dict:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForSequenceClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)   )





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    List[str]				,    _A      :    int				,    _A      :    Optional[int]				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    int   )						->						Optional[Any]:
         snake_case      =					self.num_choices
         snake_case      =					TFMPNetForMultipleChoice(_A   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					tf.tile(tf.expand_dims(_A				,    1   )				,    (1, self.num_choices, 1)   )
         snake_case      =					{
             "input_ids": multiple_choice_inputs_ids,
             "attention_mask": multiple_choice_input_mask,
         }
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)   )





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Optional[Any]				,    _A      :    int				,    _A      :    int				,    _A      :    Tuple   )						->						Tuple:
         snake_case      =					self.num_labels
         snake_case      =					TFMPNetForTokenClassification(_A   )
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         snake_case      =					model(_A   )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)   )





     def 		UpperCAmelCase(self      :    List[Any]   )						->						List[str]:
         snake_case      =					self.prepare_config_and_inputs()
         ((snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case)     ,							(snake_case))      =					config_and_inputs
         snake_case      =					{"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase		(  A_							, A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							List[Any]           =		(
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     UpperCAmelCase__				:							Optional[int]           =		(
         {
             "feature-extraction": TFMPNetModel,
             "fill-mask": TFMPNetForMaskedLM,
             "question-answering": TFMPNetForQuestionAnswering,
             "text-classification": TFMPNetForSequenceClassification,
             "token-classification": TFMPNetForTokenClassification,
             "zero-shot": TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__				:							int           =		False
     UpperCAmelCase__				:							str           =		False





     def 		UpperCAmelCase(self      :    List[Any]   )						->						str:
         snake_case      =					TFMPNetModelTester(self   )
         snake_case      =					ConfigTester(self				,    config_class=_A				,    hidden_size=3_7   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						int:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*_A   )





     def 		UpperCAmelCase(self      :    List[str]   )						->						List[str]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_masked_lm(*_A   )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						str:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*_A   )





     def 		UpperCAmelCase(self      :    Dict   )						->						Tuple:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A   )





     def 		UpperCAmelCase(self      :    int   )						->						List[Any]:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						int:
         snake_case      =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*_A   )





     @slow
     def 		UpperCAmelCase(self      :    Optional[int]   )						->						Union[str, Any]:
         for model_name in ["microsoft/mpnet-base"]:
             snake_case      =					TFMPNetModel.from_pretrained(_A   )
             self.assertIsNotNone(_A   )







@require_tf
class      lowerCamelCase		(  unittest.TestCase  ):





     @slow
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						List[Any]:
         snake_case      =					TFMPNetModel.from_pretrained("microsoft/mpnet-base"   )
         snake_case      =					tf.constant([[0, 1, 2, 3, 4, 5]]   )
         snake_case      =					model(_A   )[0]

         snake_case      =					[1, 6, 7_6_8]
         self.assertEqual(output.shape				,    _A   )

         snake_case      =					tf.constant(
             [
                 [
                     [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
                     [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
                     [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
                 ]
             ]   )
         tf.debugging.assert_near(output[:, :3, :3]				,    _A				,    atol=1E-4   )

