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
UpperCamelCase      			=     'src/transformers'

# Matches is_xxx_available()
UpperCamelCase      			=     re.compile(r'is\_([a-z_]*)_available()')
# Matches from xxx import bla
UpperCamelCase      			=     re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
UpperCamelCase      			=     re.compile(r'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


UpperCamelCase      			=     '\n{0} = None\n'

UpperCamelCase      			=     '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


UpperCamelCase      			=     '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'



def    lowerCamelCase_   (  _lowercase    )							->	Union[str, Any]:
		if _re_test_backend.search(_lowercase    ) is None:
				return None
		__A							:   Optional[int]	  =				[b[0] for b in _re_backend.findall(_lowercase    )]
		backends.sort()
		return "_and_".join(_lowercase    )



def    lowerCamelCase_   (  )							->	List[str]:
		with open(os.path.join(_lowercase ,     "__init__.py"    ) ,     "r" ,     encoding="utf-8" ,     newline="\n"    ) as f:
				__A							:   Tuple	  =				f.readlines()

		# Get to the point we do the actual imports for type checking
		__A							:   Any	  =				0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		__A							:   Tuple	  =				{}
		# Go through the end of the file
		while line_index < len(_lowercase    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				__A							:   int	  =				find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						__A							:   Any	  =				[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								__A							:   Dict	  =				lines[line_index]
								__A							:   Union[str, Any]	  =				_re_single_line_import.search(_lowercase    )
								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

						__A							:   List[str]	  =				objects
				else:
						line_index += 1

		return backend_specific_objects



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	Tuple:
		if name.isupper():
				return DUMMY_CONSTANT.format(_lowercase    )
		elif name.islower():
				return DUMMY_FUNCTION.format(_lowercase ,     _lowercase    )
		else:
				return DUMMY_CLASS.format(_lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase=None    )							->	Optional[int]:
		if backend_specific_objects is None:
				__A							:   List[Any]	  =				read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		__A							:   Optional[int]	  =				{}

		for backend, objects in backend_specific_objects.items():
				__A							:   Tuple	  =				'''[''' + ''', '''.join(F"\"{b}\"" for b in backend.split("_and_"    )    ) + ''']'''
				__A							:   Optional[int]	  =				'''# 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(_lowercase ,     _lowercase    ) for o in objects]    )
				__A							:   List[Any]	  =				dummy_file

		return dummy_files



def    lowerCamelCase_   (  _lowercase=False    )							->	Dict:
		__A							:   Union[str, Any]	  =				create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		__A							:   str	  =				{'''torch''': '''pt'''}

		# Locate actual dummy modules and read their content.
		__A							:   Union[str, Any]	  =				os.path.join(_lowercase ,     "utils"    )
		__A							:   List[Any]	  =				{
		    backend: os.path.join(_lowercase ,     F"dummy_{short_names.get(_lowercase ,     _lowercase    )}_objects.py"    )
		    for backend in dummy_files.keys()
		}

		__A							:   Union[str, Any]	  =				{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(_lowercase    ):
						with open(_lowercase ,     "r" ,     encoding="utf-8" ,     newline="\n"    ) as f:
								__A							:   Optional[Any]	  =				f.read()
				else:
						__A							:   Any	  =				''''''

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    F"Updating transformers.utils.dummy_{short_names.get(_lowercase ,     _lowercase    )}_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(_lowercase ,     _lowercase    )}_objects.py. Run `make fix-copies` "
								    "to fix this."    )


if __name__ == "__main__":
					UpperCamelCase      			=     argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					UpperCamelCase      			=     parser.parse_args()

					check_dummies(args.fix_and_overwrite)






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      			=     namedtuple('ExampleNamedTuple', 'a b c')


class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				torch.randn(5							,			2   )
			__A							:   Any	  =				torch.device("cuda"   ) if torch.cuda.is_available() else torch.device("cpu"   )

			__A							:   Union[str, Any]	  =				send_to_device(__UpperCAmelCase							,			__UpperCAmelCase   )
			self.assertTrue(torch.equal(resulta.cpu()							,			__UpperCAmelCase   )   )

			__A							:   Dict	  =				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   )

			__A							:   Optional[Any]	  =				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   )

			__A							:   Any	  =				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   )

			__A							:   List[Any]	  =				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 					__UpperCAmelCase(       self   ):
			with self.assertRaises(__UpperCAmelCase   ) as cm:
					__A							:   List[Any]	  =				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 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				torch.tensor([1, 2, 3, 4, 5]   )
			self.assertEqual(listify(__UpperCAmelCase   )							,			[1, 2, 3, 4, 5]   )

			__A							:   List[str]	  =				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]]   )

			__A							:   Dict	  =				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 					__UpperCAmelCase(       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 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				RegressionModel()
			__A							:   str	  =				model.forward
			__A							:   List[Any]	  =				convert_outputs_to_fpaa(model.forward   )
			__A							:   Optional[int]	  =				extract_model_from_parallel(__UpperCAmelCase							,			keep_fpaa_wrapper=__UpperCAmelCase   )
			__A							:   Any	  =				pickle.dumps(__UpperCAmelCase   )

	@require_cuda
	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				RegressionModel()
			__A							:   str	  =				model.forward
			__A							:   Tuple	  =				torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			__A							:   List[Any]	  =				convert_outputs_to_fpaa(model.forward   )
			__A							:   Optional[Any]	  =				extract_model_from_parallel(__UpperCAmelCase							,			keep_fpaa_wrapper=__UpperCAmelCase   )
			__A							:   Any	  =				pickle.dumps(__UpperCAmelCase   )

	@require_cuda
	@require_torch_min_version(version="2.0"   )
	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				RegressionModel()
			__A							:   Optional[int]	  =				model.forward
			__A							:   Union[str, Any]	  =				torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			__A							:   int	  =				convert_outputs_to_fpaa(model.forward   )
			__A							:   Optional[Any]	  =				torch.compile(model.forward							,			backend="inductor"   )
			__A							:   Tuple	  =				torch.randn(4							,			10   ).cuda()
			__A							:   Union[str, Any]	  =				model(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Union[str, Any]	  =				RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			__A							:   Dict	  =				torch.nn.parallel.DataParallel(__UpperCAmelCase   )
			__A							:   int	  =				extract_model_from_parallel(__UpperCAmelCase   )

			self.assertEqual(__UpperCAmelCase							,			__UpperCAmelCase   )

	@require_torch_min_version(version="2.0"   )
	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				RegressionModel()
			__A							:   Dict	  =				torch.compile(__UpperCAmelCase   )

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			__A							:   Dict	  =				torch.nn.parallel.DataParallel(__UpperCAmelCase   )
			__A							:   Tuple	  =				torch.compile(__UpperCAmelCase   )
			__A							:   Any	  =				extract_model_from_parallel(__UpperCAmelCase   )

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







	def 					__UpperCAmelCase(       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"]   )   )





