


import os
import sys
import unittest


_lowercase       :      int	=	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       :      Optional[Any]	=	os.path.join(git_repo_path, "src", "transformers")

_lowercase       :      Tuple	=	"\n{0} = None\n"

_lowercase       :      Dict	=	"\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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""   )
					self.assertIsNone(a__   )

					A								=				find_backend("""    if not is_tokenizers_available():"""   )
					self.assertEqual(a__				,   """tokenizers"""   )

					A								=				find_backend("""    if not is_tensorflow_text_available():"""   )
					self.assertEqual(a__				,   """tensorflow_text"""   )

					A								=				find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""   )
					self.assertEqual(a__				,   """sentencepiece_and_tokenizers"""   )

					A								=				find_backend(
					    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""   )
					self.assertEqual(a__				,   """sentencepiece_and_tensorflow_text"""   )

					A								=				find_backend(
					    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""   )
					self.assertEqual(a__				,   """sentencepiece_and_tokenizers_and_vision"""   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				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      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				create_dummy_object("""CONSTANT"""				,   """'torch'"""   )
					self.assertEqual(a__				,   """\nCONSTANT = None\n"""   )

					A								=				create_dummy_object("""function"""				,   """'torch'"""   )
					self.assertEqual(
					    a__				,   """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""   )

					A								=				"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
					A								=				create_dummy_object("""FakeClass"""				,   """'torch'"""   )
					self.assertEqual(a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
					A								=				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       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



