





from typing import Any

import numpy as np




class      UpperCamelCase_				:


							def __init__(							self			:Union[str, Any]							, __A			:np.ndarray							, __A			:int						)      ->      None:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=					tableau
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					tableau.shape

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

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

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

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

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

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

									SCREAMING_SNAKE_CASE__								=					[""""""]

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

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


							@staticmethod
							def    _snake_case     (							*__A			:int						)      ->      list[str]:



									"""simple docstring"""



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

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

									# decision | slack | artificial
									SCREAMING_SNAKE_CASE__								=					["""x""", """s""", """a"""]
									SCREAMING_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    _snake_case     (							self			:List[Any]							, __A			:np.ndarray						)      ->      tuple[Any, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.objectives[-1]

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

									# RHS
									SCREAMING_SNAKE_CASE__								=					tableau[s, -1]

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

									# Array filled with nans
									SCREAMING_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
									SCREAMING_SNAKE_CASE__								=					np.divide(__A							, __A							, out=__A							, 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__								=					np.nanargmin(__A						) + self.n_stages
									return row_idx, col_idx


							def    _snake_case     (							self			:List[str]							, __A			:np.ndarray							, __A			:int							, __A			:int						)      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					tableau[row_idx].copy()

									SCREAMING_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
									SCREAMING_SNAKE_CASE__								=					piv_row
									return tableau


							def    _snake_case     (							self			:List[Any]							, __A			:np.ndarray						)      ->      np.ndarray:



									"""simple docstring"""



									self.objectives.pop()

									if not self.objectives:
											return tableau

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

									# Delete the artificial variable columns
									SCREAMING_SNAKE_CASE__								=					np.delete(__A							, __A							, axis=1						)

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

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


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



									"""simple docstring"""



									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__								=					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__								=					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__								=					self.change_stage(self.tableau						)
											else:
													SCREAMING_SNAKE_CASE__								=					self.pivot(self.tableau							, __A							, __A						)
									return {}


							def    _snake_case     (							self			:Union[str, Any]							, __A			:np.ndarray							, __A			:list[str]						)      ->      dict[str, float]:



									"""simple docstring"""



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

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

											# First entry in the nonzero ids
											SCREAMING_SNAKE_CASE__								=					nonzero[0][0]
											SCREAMING_SNAKE_CASE__								=					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__								=					tableau[nonzero_rowidx, -1]
													SCREAMING_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(__A							, 0						)
									return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()







"""simple docstring"""







import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
			    SpeechaTextaDecoder,
			    SpeechaTextaForCausalLM,
			)


@require_torch
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=99       ,  snake_case__=13       ,  snake_case__=16       ,  snake_case__=7       ,  snake_case__=True       ,  snake_case__=True       ,  snake_case__=True       ,  snake_case__=False       ,  snake_case__=True       ,  snake_case__=2       ,  snake_case__=32       ,  snake_case__=4       ,  snake_case__=4       ,  snake_case__=30       ,  snake_case__=0       ,  snake_case__=1       ,  snake_case__=2       ,  snake_case__=None       ,  ):
												'''simple docstring'''
												lowercase__				:      List[str]=       parent
												lowercase__				:      Dict=       batch_size
												lowercase__				:      List[str]=       decoder_seq_length
												# For common tests
												lowercase__				:      List[str]=       self.decoder_seq_length
												lowercase__				:      int=       is_training
												lowercase__				:      Any=       use_attention_mask
												lowercase__				:      List[str]=       use_labels

												lowercase__				:      Dict=       vocab_size
												lowercase__				:      Dict=       d_model
												lowercase__				:      Union[str, Any]=       d_model
												lowercase__				:      List[str]=       decoder_layers
												lowercase__				:      Optional[Any]=       decoder_layers
												lowercase__				:      Optional[Any]=       decoder_ffn_dim
												lowercase__				:      Union[str, Any]=       decoder_attention_heads
												lowercase__				:      Tuple=       decoder_attention_heads
												lowercase__				:      Optional[Any]=       eos_token_id
												lowercase__				:      List[Any]=       bos_token_id
												lowercase__				:      Any=       pad_token_id
												lowercase__				:      Union[str, Any]=       decoder_start_token_id
												lowercase__				:      Optional[Any]=       use_cache
												lowercase__				:      List[Any]=       max_position_embeddings

												lowercase__				:      Tuple=       None
												lowercase__				:      Optional[Any]=       decoder_seq_length
												lowercase__				:      Optional[int]=       2
												lowercase__				:      Tuple=       1




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  self.vocab_size			)

												lowercase__				:      Tuple=       None
												if self.use_attention_mask:
																		lowercase__				:      List[str]=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  vocab_size=2			)

												lowercase__				:      Any=       None
												if self.use_labels:
																		lowercase__				:      str=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  self.vocab_size			)

												lowercase__				:      Dict=       SpeechaTextaConfig(
												    vocab_size=self.vocab_size       ,  d_model=self.d_model       ,  decoder_layers=self.decoder_layers       ,  decoder_ffn_dim=self.decoder_ffn_dim       ,  decoder_attention_heads=self.decoder_attention_heads       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.bos_token_id       ,  use_cache=self.use_cache       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.decoder_start_token_id       ,  max_position_embeddings=self.max_position_embeddings       ,  )

												return (
												    config,
												    input_ids,
												    attention_mask,
												    lm_labels,
												)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       True
												lowercase__				:      List[Any]=       SpeechaTextaDecoder(config=snake_case__			).to(snake_case__			).eval()
												lowercase__				:      Any=       input_ids[:2]

												input_ids[input_ids == 0] += 1
												# first forward pass
												lowercase__				:      List[str]=       model(snake_case__       ,  use_cache=snake_case__			)
												lowercase__				:      Optional[Any]=       model(snake_case__			)
												lowercase__				:      List[Any]=       model(snake_case__       ,  use_cache=snake_case__			)

												self.parent.assertTrue(len(snake_case__			) == len(snake_case__			)			)
												self.parent.assertTrue(len(snake_case__			) == len(snake_case__			) + 1			)

												lowercase__				:      List[Any]=       outputs["past_key_values"]

												# create hypothetical next token and extent to next_input_ids
												lowercase__				:      List[Any]=       ids_tensor((2, 1)       ,  config.vocab_size - 1			) + 1

												# append to next input_ids and
												lowercase__				:      Tuple=       torch.cat([input_ids, next_tokens]       ,  dim=-1			)

												print(snake_case__			)

												lowercase__				:      int=       model(snake_case__			)["last_hidden_state"]
												lowercase__				:      int=       model(snake_case__       ,  past_key_values=snake_case__			)["last_hidden_state"]

												# select random slice
												lowercase__				:      str=       ids_tensor((1,)       ,  output_from_past.shape[-1]			).item()
												lowercase__				:      Any=       output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
												lowercase__				:      int=       output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												assert torch.allclose(snake_case__       ,  snake_case__       ,  atol=1e-3			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       self.prepare_config_and_inputs()
												(
												    (
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	
												)				:      Dict=       config_and_inputs

												lowercase__				:      Optional[int]=       {
												    "input_ids": input_ids,
												    "attention_mask": attention_mask,
												}
												return config, inputs_dict


@require_torch
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
						__lowerCamelCase				       =					(SpeechaTextaForCausalLM,) if is_torch_available() else ()
						__lowerCamelCase				       =					{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
						__lowerCamelCase				       =					True
						__lowerCamelCase				       =					False




						def 		UpperCAmelCase_       (   self       ,  ):
												'''simple docstring'''
												lowercase__				:      List[str]=       SpeechaTextaStandaloneDecoderModelTester(self       ,  is_training=snake_case__			)
												lowercase__				:      int=       ConfigTester(self       ,  config_class=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												self.config_tester.run_common_tests()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past(*snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return






