


"""simple docstring"""







import os
import sys
import unittest


lowerCamelCase_   :   Optional[Any]									=  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_   :   Dict									=  os.path.join(git_repo_path, """src""", """diffusers""")






class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=find_backend('''    if not is_torch_available():'''						)
     self.assertEqual(__A ,    '''torch'''						)

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

     a 		=find_backend('''    if not (is_torch_available() and is_transformers_available()):'''						)
     self.assertEqual(__A ,    '''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")

     a 		=find_backend(
         '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''						)
     self.assertEqual(__A ,    '''torch_and_transformers_and_onnx'''						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     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''' ,    __A						)
     self.assertIn('''torch_and_transformers''' ,    __A						)
     self.assertIn('''flax_and_transformers''' ,    __A						)
     self.assertIn('''torch_and_transformers_and_onnx''' ,    __A						)

     # 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       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=create_dummy_object('''CONSTANT''' ,    '''\'torch\''''						)
     self.assertEqual(__A ,    '''\nCONSTANT = None\n'''						)

     a 		=create_dummy_object('''function''' ,    '''\'torch\''''						)
     self.assertEqual(
         __A ,    '''\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\')

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

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
     a 		=create_dummy_object('''FakeClass''' ,    '''\'torch\''''						)
     self.assertEqual(__A ,    __A						)





 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     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"])

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

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
     a 		=create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}						)
     self.assertEqual(dummy_files['''torch'''] ,    __A						)




'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       warnings.warn(
           'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ChineseCLIPImageProcessor instead.'  ,						UpperCamelCase__  ,						)
       super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__ )

