




'''simple docstring'''





from typing import Any

import numpy as np




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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

      _a				:							Any							       =      tableau
      _a       , _a				:							int							       =      tableau.shape

      # Number of decision variables x1, x2, x3...
      _a				:							Any							       =      n_vars

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

      # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
      _a				:							List[str]							       =      (self.n_art_vars > 0) + 1

      # Number of slack variables added to make inequalities into equalities
      _a				:							Optional[Any]							       =      self.n_rows - self.n_stages

      # Objectives for each stage
      _a				:							List[Any]							       =      ["""max"""]

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

      _a				:							List[str]							       =      [""""""]

      # Index of current pivot row and column
      _a				:							List[str]							       =      None
      _a				:							List[str]							       =      None

      # Does objective row only contain (non)-negative values?
      _a				:							List[Any]							       =      False



  @staticmethod
  def     a_							(			*__lowerCAmelCase ):







      '''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
      _a				:							Optional[Any]							       =      ["""x""", """s""", """a"""]
      _a				:							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     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.objectives[-1]

      # Find entries of highest magnitude in objective rows
      _a				:							Union[str, Any]							       =      (objective == """min""") - (objective == """max""")
      _a				:							Tuple							       =      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:
          _a				:							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
      _a				:							int							       =      slice(self.n_stages		,     self.n_rows )

      # RHS
      _a				:							int							       =      tableau[s, -1]

      # Elements of pivot column within slice
      _a				:							Optional[Any]							       =      tableau[s, col_idx]

      # Array filled with nans
      _a				:							Dict							       =      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
      _a				:							Optional[Any]							       =      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
      _a				:							Any							       =      np.nanargmin(__lowerCAmelCase ) + self.n_stages
      return row_idx, col_idx



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      tableau[row_idx].copy()

      _a				:							Dict							       =      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
      _a				:							str							       =      piv_row
      return tableau



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      self.objectives.pop()

      if not self.objectives:
          return tableau

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

      # Delete the artificial variable columns
      _a				:							Union[str, Any]							       =      np.delete(__lowerCAmelCase		,     __lowerCAmelCase		,     axis=1 )

      # Delete the objective row of the first stage
      _a				:							Optional[Any]							       =      np.delete(__lowerCAmelCase		,     0		,     axis=0 )

      _a				:							int							       =      1
      self.n_rows -= 1
      _a				:							Union[str, Any]							       =      0
      _a				:							Any							       =      False
      return tableau



  def     a_							(			self ):







      '''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:
              _a				:							Union[str, 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 )

          _a       , _a				:							Dict							       =      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
              _a				:							Optional[Any]							       =      self.change_stage(self.tableau )
          else:
              _a				:							List[str]							       =      self.pivot(self.tableau		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return {}



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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

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

          # First entry in the nonzero ids
          _a				:							List[Any]							       =      nonzero[0][0]
          _a				:							Tuple							       =      tableau[nonzero_rowidx, i]

          # If there is only one nonzero value in column, which is one
          if n_nonzero == nonzero_val == 1:
              _a				:							List[str]							       =      tableau[nonzero_rowidx, -1]
              _a				:							Dict							       =      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 os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase__    :      List[Any]       					=namedtuple('''ExampleNamedTuple''', '''a b c''')

class    __A  (    unittest.TestCase     ):


				def _snake_case							(		self				):
								lowerCamelCase									=torch.randn(5 ,			2				)
								lowerCamelCase									=torch.device("""cuda"""				) if torch.cuda.is_available() else torch.device("""cpu"""				)

								lowerCamelCase									=send_to_device(UpperCAmelCase_ ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta.cpu() ,			UpperCAmelCase_				)				)

								lowerCamelCase									=send_to_device((tensor, [tensor, tensor], 1) ,			UpperCAmelCase_				)
								self.assertIsInstance(UpperCAmelCase_ ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta[0].cpu() ,			UpperCAmelCase_				)				)
								self.assertIsInstance(resulta[1] ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta[1][0].cpu() ,			UpperCAmelCase_				)				)
								self.assertTrue(torch.equal(resulta[1][1].cpu() ,			UpperCAmelCase_				)				)
								self.assertEqual(resulta[2] ,			1				)

								lowerCamelCase									=send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1} ,			UpperCAmelCase_				)
								self.assertIsInstance(UpperCAmelCase_ ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta["""a"""].cpu() ,			UpperCAmelCase_				)				)
								self.assertIsInstance(resulta["""b"""] ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta["""b"""][0].cpu() ,			UpperCAmelCase_				)				)
								self.assertTrue(torch.equal(resulta["""b"""][1].cpu() ,			UpperCAmelCase_				)				)
								self.assertEqual(resulta["""c"""] ,			1				)

								lowerCamelCase									=send_to_device(ExampleNamedTuple(a=UpperCAmelCase_ ,			b=[tensor, tensor] ,			c=1				) ,			UpperCAmelCase_				)
								self.assertIsInstance(UpperCAmelCase_ ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta.a.cpu() ,			UpperCAmelCase_				)				)
								self.assertIsInstance(resulta.b ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta.b[0].cpu() ,			UpperCAmelCase_				)				)
								self.assertTrue(torch.equal(resulta.b[1].cpu() ,			UpperCAmelCase_				)				)
								self.assertEqual(resulta.c ,			1				)

								lowerCamelCase									=send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}				) ,			UpperCAmelCase_				)
								self.assertIsInstance(UpperCAmelCase_ ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta["""a"""].cpu() ,			UpperCAmelCase_				)				)
								self.assertIsInstance(resulta["""b"""] ,			UpperCAmelCase_				)
								self.assertTrue(torch.equal(resulta["""b"""][0].cpu() ,			UpperCAmelCase_				)				)
								self.assertTrue(torch.equal(resulta["""b"""][1].cpu() ,			UpperCAmelCase_				)				)
								self.assertEqual(resulta["""c"""] ,			1				)


				def _snake_case							(		self				):
								with self.assertRaises(UpperCAmelCase_				) as cm:
												lowerCamelCase									=recursively_apply(torch.tensor ,			(torch.tensor(1				), 1) ,			error_on_other_type=UpperCAmelCase_				)
								self.assertEqual(
								    str(cm.exception				) ,			"""Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.""" ,			)


				def _snake_case							(		self				):
								lowerCamelCase									=torch.tensor([1, 2, 3, 4, 5]				)
								self.assertEqual(listify(UpperCAmelCase_				) ,			[1, 2, 3, 4, 5]				)

								lowerCamelCase									=torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]				)
								self.assertEqual(listify(UpperCAmelCase_				) ,			[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]				)

								lowerCamelCase									=torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]				)
								self.assertEqual(
								    listify(UpperCAmelCase_				) ,			[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]				)


				def _snake_case							(		self				):
								with patch_environment(aa=1 ,			BB=2				):
												self.assertEqual(os.environ.get("""AA"""				) ,			"""1"""				)
												self.assertEqual(os.environ.get("""BB"""				) ,			"""2"""				)

								self.assertNotIn("""AA""" ,			os.environ				)
								self.assertNotIn("""BB""" ,			os.environ				)


				def _snake_case							(		self				):
								lowerCamelCase									=RegressionModel()
								lowerCamelCase									=model.forward
								lowerCamelCase									=convert_outputs_to_fpaa(model.forward				)
								lowerCamelCase									=extract_model_from_parallel(UpperCAmelCase_ ,			keep_fpaa_wrapper=UpperCAmelCase_				)
								lowerCamelCase									=pickle.dumps(UpperCAmelCase_				)


				@require_cuda
				def _snake_case							(		self				):
								lowerCamelCase									=RegressionModel()
								lowerCamelCase									=model.forward
								lowerCamelCase									=torch.cuda.amp.autocast(dtype=torch.floataa				)(model.forward				)
								lowerCamelCase									=convert_outputs_to_fpaa(model.forward				)
								lowerCamelCase									=extract_model_from_parallel(UpperCAmelCase_ ,			keep_fpaa_wrapper=UpperCAmelCase_				)
								lowerCamelCase									=pickle.dumps(UpperCAmelCase_				)


				@require_cuda
				@require_torch_min_version(version="""2.0"""				)
				def _snake_case							(		self				):
								lowerCamelCase									=RegressionModel()
								lowerCamelCase									=model.forward
								lowerCamelCase									=torch.cuda.amp.autocast(dtype=torch.floataa				)(model.forward				)
								lowerCamelCase									=convert_outputs_to_fpaa(model.forward				)
								lowerCamelCase									=torch.compile(model.forward ,			backend="""inductor"""				)
								lowerCamelCase									=torch.randn(4 ,			10				).cuda()
								lowerCamelCase									=model(UpperCAmelCase_				)


				def _snake_case							(		self				):
								lowerCamelCase									=RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								lowerCamelCase									=torch.nn.parallel.DataParallel(UpperCAmelCase_				)
								lowerCamelCase									=extract_model_from_parallel(UpperCAmelCase_				)

								self.assertEqual(UpperCAmelCase_ ,			UpperCAmelCase_				)


				@require_torch_min_version(version="""2.0"""				)
				def _snake_case							(		self				):
								lowerCamelCase									=RegressionModel()
								lowerCamelCase									=torch.compile(UpperCAmelCase_				)

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								lowerCamelCase									=torch.nn.parallel.DataParallel(UpperCAmelCase_				)
								lowerCamelCase									=torch.compile(UpperCAmelCase_				)
								lowerCamelCase									=extract_model_from_parallel(UpperCAmelCase_				)

								self.assertEqual(compiled_model._orig_mod ,			compiled_model_unwrapped._orig_mod				)


				def _snake_case							(		self				):
								self.assertEqual(find_device([1, """a""", torch.tensor([1, 2, 3]				)]				) ,			torch.device("""cpu"""				)				)
								self.assertEqual(find_device({"""a""": 1, """b""": torch.tensor([1, 2, 3]				)}				) ,			torch.device("""cpu"""				)				)
								self.assertIsNone(find_device([1, """a"""]				)				)





