



'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase_									=    {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ['''FNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ['''FNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FNetForMaskedLM''',
      '''FNetForMultipleChoice''',
      '''FNetForNextSentencePrediction''',
      '''FNetForPreTraining''',
      '''FNetForQuestionAnswering''',
      '''FNetForSequenceClassification''',
      '''FNetForTokenClassification''',
      '''FNetLayer''',
      '''FNetModel''',
      '''FNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  lowercase_									=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











import os
import sys
import unittest


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

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

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


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

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

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

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

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

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


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			int							)						->	Any:


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

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

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    """# 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							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)