




from typing import TYPE_CHECKING

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


lowercase__ :     Dict			       =   {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

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

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__ :     Tuple			       =   ["XLNetTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__ :     Dict			       =   [
       "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "XLNetForMultipleChoice",
       "XLNetForQuestionAnswering",
       "XLNetForQuestionAnsweringSimple",
       "XLNetForSequenceClassification",
       "XLNetForTokenClassification",
       "XLNetLMHeadModel",
       "XLNetModel",
       "XLNetPreTrainedModel",
       "load_tf_weights_in_xlnet",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__ :     Optional[Any]			       =   [
       "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFXLNetForMultipleChoice",
       "TFXLNetForQuestionAnsweringSimple",
       "TFXLNetForSequenceClassification",
       "TFXLNetForTokenClassification",
       "TFXLNetLMHeadModel",
       "TFXLNetMainLayer",
       "TFXLNetModel",
       "TFXLNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

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




'''simple docstring'''







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									=     '''
{0} = None
'''

__lowerCamelCase									=     '''
class {0}(metaclass=DummyObject):
    _backends = {1}

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


__lowerCamelCase									=     '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
           self.assertIsNone(UpperCamelCase__							)

           A_						       =   find_backend("""    if not is_tokenizers_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tokenizers"""							)

           A_						       =   find_backend("""    if not is_tensorflow_text_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tensorflow_text"""							)

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

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

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



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           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"""		,					UpperCamelCase__							)
           self.assertIn("""tensorflow_text"""		,					UpperCamelCase__							)
           self.assertIn("""sentencepiece_and_tokenizers"""		,					UpperCamelCase__							)

           # 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							)    ->   str:





           '''simple docstring'''
           A_						       =   create_dummy_object("""CONSTANT"""		,					"""'torch'"""							)
           self.assertEqual(UpperCamelCase__		,					"""\nCONSTANT = None\n"""							)

           A_						       =   create_dummy_object("""function"""		,					"""'torch'"""							)
           self.assertEqual(
               UpperCamelCase__		,					"""\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(UpperCamelCase__		,					UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           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"""]		,					UpperCamelCase__							)
