



'''simple docstring'''






import os
import sys
import unittest


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

UpperCamelCase__:   List[Any]								=			"\n{0} = None\n"

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


UpperCamelCase__:   List[str]								=			"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Optional[int]:
								UpperCAmelCase :       int 							=		find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''		)
								self.assertIsNone(__snake_case		)

								UpperCAmelCase :       List[Any] 							=		find_backend('''    if not is_tokenizers_available():'''		)
								self.assertEqual(__snake_case		,			'''tokenizers'''		)

								UpperCAmelCase :       Any 							=		find_backend('''    if not is_tensorflow_text_available():'''		)
								self.assertEqual(__snake_case		,			'''tensorflow_text'''		)

								UpperCAmelCase :       Union[str, Any] 							=		find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''		)
								self.assertEqual(__snake_case		,			'''sentencepiece_and_tokenizers'''		)

								UpperCAmelCase :       Dict 							=		find_backend(
								    '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''		)
								self.assertEqual(__snake_case		,			'''sentencepiece_and_tensorflow_text'''		)

								UpperCAmelCase :       int 							=		find_backend(
								    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''		)
								self.assertEqual(__snake_case		,			'''sentencepiece_and_tokenizers_and_vision'''		)







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

								# 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      A	(						self			:     Union[str, Any]		)			->		str:
								UpperCAmelCase :       List[Any] 							=		create_dummy_object('''CONSTANT'''		,			'''\'torch\''''		)
								self.assertEqual(__snake_case		,			'''\nCONSTANT = None\n'''		)

								UpperCAmelCase :       Any 							=		create_dummy_object('''function'''		,			'''\'torch\''''		)
								self.assertEqual(
								    __snake_case		,			'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''		)

								UpperCAmelCase :       List[Any] 							=		'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
								UpperCAmelCase :       Dict 							=		create_dummy_object('''FakeClass'''		,			'''\'torch\''''		)
								self.assertEqual(__snake_case		,			__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Tuple:
								UpperCAmelCase :       List[str] 							=		'''# 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"])
'''
								UpperCAmelCase :       List[Any] 							=		create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}		)
								self.assertEqual(dummy_files['''torch''']		,			__snake_case		)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__:   str								=			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			["LxmertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Union[str, Any]								=			[
						    "LxmertEncoder",
						    "LxmertForPreTraining",
						    "LxmertForQuestionAnswering",
						    "LxmertModel",
						    "LxmertPreTrainedModel",
						    "LxmertVisualFeatureEncoder",
						    "LxmertXLayer",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			[
						    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFLxmertForPreTraining",
						    "TFLxmertMainLayer",
						    "TFLxmertModel",
						    "TFLxmertPreTrainedModel",
						    "TFLxmertVisualFeatureEncoder",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						UpperCamelCase__:   Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





