code: 
  
  
  
  
  import argparse
  import os
  import re
  
  
  # All paths are set with the intent you should run this script from the root of the repo with the command
  # python utils/check_dummies.py
  SCREAMING_SNAKE_CASE__    :    Optional[int]								=					"src/transformers"
  
  # Matches is_xxx_available()
  SCREAMING_SNAKE_CASE__    :    Tuple								=					re.compile(r"is\_([a-z_]*)_available()")
  # Matches from xxx import bla
  SCREAMING_SNAKE_CASE__    :    Optional[int]								=					re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
  SCREAMING_SNAKE_CASE__    :    int								=					re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")
  
  
  SCREAMING_SNAKE_CASE__    :    Any								=					"\n{0} = None\n"
  
  SCREAMING_SNAKE_CASE__    :    int								=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"
  
  
  SCREAMING_SNAKE_CASE__    :    int								=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if _re_test_backend.search(SCREAMING_SNAKE_CASE   ) is None:
  						return None
  			SCREAMING_SNAKE_CASE_			=	[b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE   )]
  			backends.sort()
  			return "_and_".join(SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			with open(os.path.join(SCREAMING_SNAKE_CASE					,						'__init__.py'   )					,						'r'					,						encoding='utf-8'					,						newline='\n'   ) as f:
  						SCREAMING_SNAKE_CASE_			=	f.readlines()
  
  			# Get to the point we do the actual imports for type checking
  			SCREAMING_SNAKE_CASE_			=	0
  			while not lines[line_index].startswith('if TYPE_CHECKING'   ):
  						line_index += 1
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			# Go through the end of the file
  			while line_index < len(SCREAMING_SNAKE_CASE   ):
  						# If the line is an if is_backend_available, we grab all objects associated.
  						SCREAMING_SNAKE_CASE_			=	find_backend(lines[line_index]   )
  						if backend is not None:
  									while not lines[line_index].startswith('    else:'   ):
  												line_index += 1
  									line_index += 1
  
  									SCREAMING_SNAKE_CASE_			=	[]
  									# Until we unindent, add backend objects to the list
  									while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(' ' * 8   ):
  												SCREAMING_SNAKE_CASE_			=	lines[line_index]
  												SCREAMING_SNAKE_CASE_			=	_re_single_line_import.search(SCREAMING_SNAKE_CASE   )
  												if single_line_import_search is not None:
  															objects.extend(single_line_import_search.groups()[0].split(', '   )   )
  												elif line.startswith(' ' * 12   ):
  															objects.append(line[12:-2]   )
  												line_index += 1
  
  									SCREAMING_SNAKE_CASE_			=	objects
  						else:
  									line_index += 1
  
  			return backend_specific_objects
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if name.isupper():
  						return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE   )
  			elif name.islower():
  						return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			else:
  						return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE=None   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if backend_specific_objects is None:
  						SCREAMING_SNAKE_CASE_			=	read_init()
  			# For special correspondence backend to module name as used in the function requires_modulename
  			SCREAMING_SNAKE_CASE_			=	{}
  
  			for backend, objects in backend_specific_objects.items():
  						SCREAMING_SNAKE_CASE_			=	'[' + ', '.join(F'"{b}"' for b in backend.split('_and_'   )   ) + ']'
  						SCREAMING_SNAKE_CASE_			=	'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
  						dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  						dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) for o in objects]   )
  						SCREAMING_SNAKE_CASE_			=	dummy_file
  
  			return dummy_files
  
  def lowercase						(     SCREAMING_SNAKE_CASE=False   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	create_dummy_files()
  			# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  			SCREAMING_SNAKE_CASE_			=	{'torch': 'pt'}
  
  			# Locate actual dummy modules and read their content.
  			SCREAMING_SNAKE_CASE_			=	os.path.join(SCREAMING_SNAKE_CASE					,						'utils'   )
  			SCREAMING_SNAKE_CASE_			=	{
  			    backend: os.path.join(SCREAMING_SNAKE_CASE					,						F'dummy_{short_names.get(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )}_objects.py'   )
  			    for backend in dummy_files.keys()
  			}
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			for backend, file_path in dummy_file_paths.items():
  						if os.path.isfile(SCREAMING_SNAKE_CASE   ):
  									with open(SCREAMING_SNAKE_CASE					,						'r'					,						encoding='utf-8'					,						newline='\n'   ) as f:
  												SCREAMING_SNAKE_CASE_			=	f.read()
  						else:
  									SCREAMING_SNAKE_CASE_			=	''
  
  			for backend in dummy_files.keys():
  						if dummy_files[backend] != actual_dummies[backend]:
  									if overwrite:
  												print(
  												    F'Updating transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )}_objects.py as the main '
  												    '__init__ has new objects.'   )
  												with open(dummy_file_paths[backend]					,						'w'					,						encoding='utf-8'					,						newline='\n'   ) as f:
  															f.write(dummy_files[backend]   )
  									else:
  												raise ValueError(
  												    'The main __init__ has objects that are not present in '
  												    F'transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )}_objects.py. Run `make fix-copies` '
  												    'to fix this.'   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					argparse.ArgumentParser()
  				parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					parser.parse_args()
  
  				check_dummies(args.fix_and_overwrite)
  
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']			)			)
  