


'''simple docstring'''

import os
import sys
import unittest


lowerCAmelCase      :Tuple    	=			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
lowerCAmelCase      :Tuple    	=			os.path.join(git_repo_path, '''src''', '''transformers''')

lowerCAmelCase      :Optional[Any]    	=			'''\n{0} = None\n'''

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


lowerCAmelCase      :Any    	=			'''\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'''
class _lowerCamelCase       (						unittest.TestCase							):



		'''simple docstring'''







		def    __lowerCAmelCase		(					self		:						Tuple			)  -> Union[str, Any]:
			__magic_name__       :    int     	=     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
			self.assertIsNone(lowercase_			)

			__magic_name__       :    List[str]     	=     find_backend('    if not is_tokenizers_available():'			)
			self.assertEqual(lowercase_  ,     'tokenizers'			)

			__magic_name__       :    List[Any]     	=     find_backend('    if not is_tensorflow_text_available():'			)
			self.assertEqual(lowercase_  ,     'tensorflow_text'			)

			__magic_name__       :    int     	=     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
			self.assertEqual(lowercase_  ,     'sentencepiece_and_tokenizers'			)

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

			__magic_name__       :    List[Any]     	=     find_backend(
			    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
			self.assertEqual(lowercase_  ,     'sentencepiece_and_tokenizers_and_vision'			)







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

			# 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		:						int			)  -> Optional[Any]:
			__magic_name__       :    Optional[Any]     	=     create_dummy_object('CONSTANT'  ,     '\'torch\''			)
			self.assertEqual(lowercase_  ,     '\nCONSTANT = None\n'			)

			__magic_name__       :    Dict     	=     create_dummy_object('function'  ,     '\'torch\''			)
			self.assertEqual(
			    lowercase_  ,     '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

			__magic_name__       :    Union[str, Any]     	=     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
			__magic_name__       :    Tuple     	=     create_dummy_object('FakeClass'  ,     '\'torch\''			)
			self.assertEqual(lowercase_  ,     lowercase_			)







		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> Dict:
			__magic_name__       :    Dict     	=     '# 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'
			__magic_name__       :    Optional[int]     	=     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
			self.assertEqual(dummy_files['torch']  ,     lowercase_			)



import os
import sys
import unittest


lowerCamelCase											=       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
lowerCamelCase											=       os.path.join(git_repo_path, 'src', 'transformers')

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

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


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




class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
             self.assertIsNone(lowercase_ )

             _lowerCamelCase       :		List[str]		=find_backend('    if not is_tokenizers_available():' )
             self.assertEqual(lowercase_			,   'tokenizers' )

             _lowerCamelCase       :		List[Any]		=find_backend('    if not is_tensorflow_text_available():' )
             self.assertEqual(lowercase_			,   'tensorflow_text' )

             _lowerCamelCase       :		int		=find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers' )

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

             _lowerCamelCase       :		List[Any]		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers_and_vision' )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=read_init()
             # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
             self.assertIn('torch'			,   lowercase_ )
             self.assertIn('tensorflow_text'			,   lowercase_ )
             self.assertIn('sentencepiece_and_tokenizers'			,   lowercase_ )

             # 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						:     int )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=create_dummy_object('CONSTANT'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   '\nCONSTANT = None\n' )

             _lowerCamelCase       :		Dict		=create_dummy_object('function'			,   '\'torch\'' )
             self.assertEqual(
                 lowercase_			,   '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

             _lowerCamelCase       :		Union[str, Any]		='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
             _lowerCamelCase       :		Tuple		=create_dummy_object('FakeClass'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		='# 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'
             _lowerCamelCase       :		Optional[int]		=create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
             self.assertEqual(dummy_files['torch']			,   lowercase_ )


