
import os
import sys
import unittest


lowercase  :					str													=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase  :					Tuple													=       os.path.join(git_repo_path, 'src', 'transformers')

lowercase  :					Tuple													=       '\n{0} = None\n'

lowercase  :					List[Any]													=       '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


lowercase  :					List[Any]													=       '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Dict						)		-> Dict:
								__UpperCamelCase	:   Optional[Any]			   =					find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"						)
								self.assertIsNone(a						)

								__UpperCamelCase	:   int			   =					find_backend("    if not is_tokenizers_available():"						)
								self.assertEqual(a     ,   "tokenizers"						)

								__UpperCamelCase	:   List[str]			   =					find_backend("    if not is_tensorflow_text_available():"						)
								self.assertEqual(a     ,   "tensorflow_text"						)

								__UpperCamelCase	:   Optional[int]			   =					find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"						)
								self.assertEqual(a     ,   "sentencepiece_and_tokenizers"						)

								__UpperCamelCase	:   Optional[Any]			   =					find_backend(
								    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"						)
								self.assertEqual(a     ,   "sentencepiece_and_tensorflow_text"						)

								__UpperCamelCase	:   int			   =					find_backend(
								    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"						)
								self.assertEqual(a     ,   "sentencepiece_and_tokenizers_and_vision"						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								__UpperCamelCase	:   Dict			   =					read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn("torch"     ,   a						)
								self.assertIn("tensorflow_text"     ,   a						)
								self.assertIn("sentencepiece_and_tokenizers"     ,   a						)

								# Likewise, we can't assert on the exact content of a key
								self.assertIn("BertModel"     ,   objects["torch"]						)
								self.assertIn("TFBertModel"     ,   objects["tf"]						)
								self.assertIn("FlaxBertModel"     ,   objects["flax"]						)
								self.assertIn("BertModel"     ,   objects["torch"]						)
								self.assertIn("TFBertTokenizer"     ,   objects["tensorflow_text"]						)
								self.assertIn("convert_slow_tokenizer"     ,   objects["sentencepiece_and_tokenizers"]						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								__UpperCamelCase	:   Tuple			   =					create_dummy_object("CONSTANT"     ,   "'torch'"						)
								self.assertEqual(a     ,   "\nCONSTANT = None\n"						)

								__UpperCamelCase	:   Union[str, Any]			   =					create_dummy_object("function"     ,   "'torch'"						)
								self.assertEqual(
								    a     ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

								__UpperCamelCase	:   Tuple			   =					"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
								__UpperCamelCase	:   Dict			   =					create_dummy_object("FakeClass"     ,   "'torch'"						)
								self.assertEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								__UpperCamelCase	:   Optional[int]			   =					"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
								__UpperCamelCase	:   List[str]			   =					create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
								self.assertEqual(dummy_files["torch"]     ,   a						)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :					List[str]													=       {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					Optional[Any]													=       [
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		lowercase  :					Optional[Any]													=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)