




'''simple docstring'''





import os
import sys
import unittest


UpperCAmelCase_		:				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
UpperCAmelCase_		:				Union[str, Any]									=     os.path.join(git_repo_path, "src", "diffusers")




class 		__A      (			unittest.TestCase			):

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	str								=find_backend("""    if not is_torch_available():""" )
										self.assertEqual(__snake_case    , """torch""" )

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

										__magic_name__	:	str								=find_backend("""    if not (is_torch_available() and is_transformers_available()):""" )
										self.assertEqual(__snake_case    , """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")

										__magic_name__	:	int								=find_backend(
										    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
										self.assertEqual(__snake_case    , """torch_and_transformers_and_onnx""" )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn("""torch"""    , __snake_case )
										self.assertIn("""torch_and_transformers"""    , __snake_case )
										self.assertIn("""flax_and_transformers"""    , __snake_case )
										self.assertIn("""torch_and_transformers_and_onnx"""    , __snake_case )

										# 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     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Dict								=create_dummy_object("""CONSTANT"""    , """'torch'""" )
										self.assertEqual(__snake_case    , """\nCONSTANT = None\n""" )

										__magic_name__	:	Union[str, Any]								=create_dummy_object("""function"""    , """'torch'""" )
										self.assertEqual(
										    __snake_case    , """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n""" )

										__magic_name__	:	Optional[int]								="""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
										__magic_name__	:	Dict								=create_dummy_object("""FakeClass"""    , """'torch'""" )
										self.assertEqual(__snake_case    , __snake_case )


						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[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\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
										__magic_name__	:	List[Any]								=create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
										self.assertEqual(dummy_files["""torch"""]    , __snake_case )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_		:				Union[str, Any]									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




