






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase			:					Optional[Any]         =	{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					Dict         =	['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					Any         =	[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase			:					Dict         =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














import os
import sys
import unittest


lowerCamelCase			:					int         =	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			:					Any         =	os.path.join(git_repo_path, '''src''', '''diffusers''')



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					find_backend("    if not is_torch_available():"	)
           self.assertEqual(__UpperCamelCase					,						"torch"	)

           # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
           # self.assertEqual(backend_with_underscore, "tensorflow_text")

           __lowerCAmelCase								=					find_backend("    if not (is_torch_available() and is_transformers_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers"	)

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

           __lowerCAmelCase								=					find_backend(
               "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers_and_onnx"	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					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("torch_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("flax_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers_and_onnx"					,						__UpperCamelCase	)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("UNet2DModel"					,						objects["torch"]	)
           self.assertIn("FlaxUNet2DConditionModel"					,						objects["flax"]	)
           self.assertIn("StableDiffusionPipeline"					,						objects["torch_and_transformers"]	)
           self.assertIn("FlaxStableDiffusionPipeline"					,						objects["flax_and_transformers"]	)
           self.assertIn("LMSDiscreteScheduler"					,						objects["torch_and_scipy"]	)
           self.assertIn("OnnxStableDiffusionPipeline"					,						objects["torch_and_transformers_and_onnx"]	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					create_dummy_object("CONSTANT"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						"\nCONSTANT = None\n"	)

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

           __lowerCAmelCase								=					"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
           __lowerCAmelCase								=					create_dummy_object("FakeClass"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					"# 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\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
           __lowerCAmelCase								=					create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}	)
           self.assertEqual(dummy_files["torch"]					,						__UpperCamelCase	)






