




import os
import sys
import unittest


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

class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						int							=  find_backend("""    if not is_torch_available():"""  )
									self.assertEqual(lowerCAmelCase_ ,       """torch"""  )

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

									a__       :						Any							=  find_backend("""    if not (is_torch_available() and is_transformers_available()):"""  )
									self.assertEqual(lowerCAmelCase_ ,       """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__       :						Tuple							=  find_backend(
									    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""  )
									self.assertEqual(lowerCAmelCase_ ,       """torch_and_transformers_and_onnx"""  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						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""" ,       lowerCAmelCase_  )
									self.assertIn("""torch_and_transformers""" ,       lowerCAmelCase_  )
									self.assertIn("""flax_and_transformers""" ,       lowerCAmelCase_  )
									self.assertIn("""torch_and_transformers_and_onnx""" ,       lowerCAmelCase_  )

									# 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  ) ->				str:
									"""simple docstring"""




									a__       :						int							=  create_dummy_object("""CONSTANT""" ,       """'torch'"""  )
									self.assertEqual(lowerCAmelCase_ ,       """\nCONSTANT = None\n"""  )

									a__       :						List[Any]							=  create_dummy_object("""function""" ,       """'torch'"""  )
									self.assertEqual(
									    lowerCAmelCase_ ,       """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""  )

									a__       :						List[str]							=  """
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__       :						Any							=  create_dummy_object("""FakeClass""" ,       """'torch'"""  )
									self.assertEqual(lowerCAmelCase_ ,       lowerCAmelCase_  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Optional[int]							=  """# 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__       :						Any							=  create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}  )
									self.assertEqual(dummy_files["""torch"""] ,       lowerCAmelCase_  )









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




