code: 
  
  
  
  
  import logging
  
  # standard libraries
  import sys
  
  import numpy as np
  import pytest  # type: ignore
  
  # Custom/local libraries
  from matrix import matrix_operation as matop
  
  SCREAMING_SNAKE_CASE__    :    str								=					[[12, 10], [3, 9]]
  SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					[[3, 4], [7, 4]]
  SCREAMING_SNAKE_CASE__    :    int								=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  SCREAMING_SNAKE_CASE__    :    int								=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
  SCREAMING_SNAKE_CASE__    :    Any								=					[1]
  SCREAMING_SNAKE_CASE__    :    int								=					[2]
  
  SCREAMING_SNAKE_CASE__    :    int								=					logging.getLogger()
  SCREAMING_SNAKE_CASE__    :    Optional[int]								=					logging.DEBUG
  SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ('mat1', 'mat2')					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]   )
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2):
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(F'\n\t{test_addition.__name__} returned integer'   )
  									matop.add(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif (np.array(SCREAMING_SNAKE_CASE   )).shape == (np.array(SCREAMING_SNAKE_CASE   )).shape:
  						logger.info(F'\n\t{test_addition.__name__} with same matrix dims'   )
  						SCREAMING_SNAKE_CASE_			=	(np.array(SCREAMING_SNAKE_CASE   ) + np.array(SCREAMING_SNAKE_CASE   )).tolist()
  						SCREAMING_SNAKE_CASE_			=	matop.add(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  						assert theo == act
  			else:
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(F'\n\t{test_addition.__name__} with different matrix dims'   )
  									matop.add(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ('mat1', 'mat2')					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]   )
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2):
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(F'\n\t{test_subtraction.__name__} returned integer'   )
  									matop.subtract(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif (np.array(SCREAMING_SNAKE_CASE   )).shape == (np.array(SCREAMING_SNAKE_CASE   )).shape:
  						logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'   )
  						SCREAMING_SNAKE_CASE_			=	(np.array(SCREAMING_SNAKE_CASE   ) - np.array(SCREAMING_SNAKE_CASE   )).tolist()
  						SCREAMING_SNAKE_CASE_			=	matop.subtract(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  						assert theo == act
  			else:
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'   )
  									assert matop.subtract(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ('mat1', 'mat2')					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]   )
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2) or (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2):
  						logger.info(F'\n\t{test_multiplication.__name__} returned integer'   )
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									matop.add(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif (np.array(SCREAMING_SNAKE_CASE   )).shape == (np.array(SCREAMING_SNAKE_CASE   )).shape:
  						logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'   )
  						SCREAMING_SNAKE_CASE_			=	(np.matmul(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )).tolist()
  						SCREAMING_SNAKE_CASE_			=	matop.multiply(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  						assert theo == act
  			else:
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(
  									    F'\n\t{test_multiplication.__name__} does not meet dim requirements'   )
  									assert matop.subtract(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  @pytest.mark.mat_ops
  def lowercase						(     ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	(3.5 * np.array(SCREAMING_SNAKE_CASE   )).tolist()
  			SCREAMING_SNAKE_CASE_			=	matop.scalar_multiply(SCREAMING_SNAKE_CASE					,						3.5   )
  			assert theo == act
  
  @pytest.mark.mat_ops
  def lowercase						(     ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	(np.identity(5   )).tolist()
  			SCREAMING_SNAKE_CASE_			=	matop.identity(5   )
  			assert theo == act
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize('mat'					,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]   )
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if (np.array(SCREAMING_SNAKE_CASE   )).shape < (2, 2):
  						with pytest.raises(SCREAMING_SNAKE_CASE   ):
  									logger.info(F'\n\t{test_transpose.__name__} returned integer'   )
  									matop.transpose(SCREAMING_SNAKE_CASE   )
  			else:
  						SCREAMING_SNAKE_CASE_			=	(np.transpose(SCREAMING_SNAKE_CASE   )).tolist()
  						SCREAMING_SNAKE_CASE_			=	matop.transpose(SCREAMING_SNAKE_CASE					,						return_map=SCREAMING_SNAKE_CASE   )
  						assert theo == act
  
style_context: 
  
  
  
  
  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,
  )
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
  				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta[2]			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta.c			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
  							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
  				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 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  
  				SCREAMING_SNAKE_CASE_			=	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(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
  				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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']			)			)
  