

import os
import sys
import unittest


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

_lowerCAmelCase  :      Optional[Any]			 ="""
{0} = None
"""

_lowerCAmelCase  :      Any			 ="""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


_lowerCAmelCase  :      Any			 ="""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"						)
						self.assertIsNone(SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Tuple									= find_backend("    if not is_tokenizers_available():"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "tokenizers"						)

						UpperCAmelCase__:	Optional[int]									= find_backend("    if not is_tensorflow_text_available():"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "tensorflow_text"						)

						UpperCAmelCase__:	Any									= find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "sentencepiece_and_tokenizers"						)

						UpperCAmelCase__:	List[str]									= find_backend(
						    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "sentencepiece_and_tensorflow_text"						)

						UpperCAmelCase__:	List[str]									= find_backend(
						    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "sentencepiece_and_tokenizers_and_vision"						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"				,      SCREAMING_SNAKE_CASE__						)
						self.assertIn("tensorflow_text"				,      SCREAMING_SNAKE_CASE__						)
						self.assertIn("sentencepiece_and_tokenizers"				,      SCREAMING_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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= create_dummy_object("CONSTANT"				,      "'torch'"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      "\nCONSTANT = None\n"						)

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

						UpperCAmelCase__:	Dict									= "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
						UpperCAmelCase__:	List[Any]									= create_dummy_object("FakeClass"				,      "'torch'"						)
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= "# 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"]				,      SCREAMING_SNAKE_CASE__						)




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				warnings.warn(
				    "The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DeiTImageProcessor instead."						,	SCREAMING_SNAKE_CASE__						,	)
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)




