



"""simple docstring"""




import os
import sys
import unittest


_lowerCAmelCase    : List[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
_lowerCAmelCase    : Union[str, Any]							=							os.path.join(git_repo_path, "src", "transformers")

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

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


_lowerCAmelCase    : str							=							"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"






class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"						)
           self.assertIsNone(_lowerCAmelCase						)

           _UpperCAmelCase      :     Dict        =   find_backend("    if not is_tokenizers_available():"						)
           self.assertEqual(_lowerCAmelCase					,     "tokenizers"						)

           _UpperCAmelCase      :     List[Any]        =   find_backend("    if not is_tensorflow_text_available():"						)
           self.assertEqual(_lowerCAmelCase					,     "tensorflow_text"						)

           _UpperCAmelCase      :     List[str]        =   find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"						)
           self.assertEqual(_lowerCAmelCase					,     "sentencepiece_and_tokenizers"						)

           _UpperCAmelCase      :     Union[str, Any]        =   find_backend(
               "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"						)
           self.assertEqual(_lowerCAmelCase					,     "sentencepiece_and_tensorflow_text"						)

           _UpperCAmelCase      :     Tuple        =   find_backend(
               "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"						)
           self.assertEqual(_lowerCAmelCase					,     "sentencepiece_and_tokenizers_and_vision"						)



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Optional[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"					,     _lowerCAmelCase						)
           self.assertIn("tensorflow_text"					,     _lowerCAmelCase						)
           self.assertIn("sentencepiece_and_tokenizers"					,     _lowerCAmelCase						)

           # 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 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     int        =   create_dummy_object("CONSTANT"					,     "'torch'"						)
           self.assertEqual(_lowerCAmelCase					,     "\nCONSTANT = None\n"						)

           _UpperCAmelCase      :     Dict        =   create_dummy_object("function"					,     "'torch'"						)
           self.assertEqual(
               _lowerCAmelCase					,     "\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      :     Any        =   create_dummy_object("FakeClass"					,     "'torch'"						)
           self.assertEqual(_lowerCAmelCase					,     _lowerCAmelCase						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     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      :     Any        =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
           self.assertEqual(dummy_files["torch"]					,     _lowerCAmelCase						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_lowerCAmelCase    : Union[str, Any]							=							{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[Any]							=							["GPTSw3Tokenizer"]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 _lowerCAmelCase    : List[Any]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
